CN112699593A - Multi-target equipment group optimization control method considering multiple factors - Google Patents
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Abstract
The invention discloses a multi-target equipment group optimization control method considering multiple factors, which comprises the following steps: step one, establishing a mathematical model of the large-user energy equipment, and determining constraint conditions; analyzing an energy consumption equipment optimization target based on the quantity of the regulation and control equipment and the comfort level of a user; and step three, realizing the optimized regulation and control of the large-user energy utilization equipment. The constraint condition of optimization regulation is determined by establishing a mathematical model of the large-user energy utilization equipment, the optimization regulation objective function of the energy utilization equipment is provided, the optimization regulation of the energy utilization equipment is realized, and the problem that the regulation quantity and the user comfort degree of the large-user energy utilization equipment cannot be considered in the past optimization regulation is effectively solved.
Description
Technical Field
The invention relates to a multi-target equipment group optimization control method considering multiple factors, and belongs to the technical field of demand response.
Background
At present, the electric load of China is rapidly increased. The ever increasing grid peak-to-valley difference presents a number of economic and safety issues. The traditional solution starts from the power supply side, and is high in cost and poor in effect. With the continuous and deep research of power technology, the management of the power demand side gets more and more attention, and large users become the first choice of national grid companies due to large load and high participation willingness. In order to better enable a large user to participate in response of a demand side, the method establishes an equipment group optimization model, and meets the load reduction requirement on the premise of considering the comfort level of the user and the participation number of the equipment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multi-target equipment group optimization control method considering multiple factors.
In order to achieve the above object, the present invention provides a multi-objective device group optimization control method considering multiple factors, which includes the following steps:
step one, establishing a mathematical model of the large-user energy equipment, and determining constraint conditions;
analyzing an energy consumption equipment optimization target based on the quantity of the regulation and control equipment and the comfort level of a user;
and step three, realizing the optimized regulation and control of the large-user energy utilization equipment.
Preferably, the determining the constraint condition in the first step includes the following steps:
(3) energy consumption equipment mathematical model
1) When the energy utilization equipment is an air conditioner, the following definitions are defined:
ΔpaN＝Δpt+Δpw+Δpv+Δpf
in the formula: Δ paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation;
global temperature control/increase of supply air temperature
Based on the control of the room temperature at 26 ℃, the energy consumption of the air conditioner is reduced by about 7% when the set temperature of the air conditioner is increased by 1 ℃, and the load reduction calculation is shown as the following formula:
ΔPt＝7％×Pair·ΔT
in the formula, Δ PtThe unit is kW for reducing the power of the air conditioner; pairThe unit is the power of an air conditioning system, is the sum of all air conditioning powers and is kW; and delta T is an upward adjustment value of the air conditioner temperature and has the unit of ℃.
Preferably, the determining the constraint condition in the first step includes the following steps:
② increasing the temperature of the chilled water
For a constant-power centrifugal cold water refrigerating unit, the energy can be saved by 0.91-1.97% when the outlet water temperature of the chilled water is raised by 1 ℃, and the load reduction calculation of the constant-power centrifugal cold water refrigerating unit is shown as the following formula:
ΔPw＝ΔT'·δ2·Pm
in the formula: delta PwThe unit of the power reduced by the constant-power centrifugal cold water refrigerating unit is kW for increasing the temperature of the chilled water; delta T' is the upward adjustment quantity of the outlet water temperature of the chilled water, and the unit is; delta2The value range is 0.91% -1.97% for the corrected value; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
③ freezing water supply flow
The chilled water valve limits the flow of cold water flowing into the fan heat exchange coil, the load reduction effect is reduced to a certain extent by adjusting the flow of the cold water, and the load reduction calculation of the chilled water valve is as shown in the formula:
ΔPv＝(Va/Vr)·δ3·Pm
in the formula, Δ PvThe power is reduced for the adjustment of a chilled water valve, and the unit is kW; vaThe total amount of the water flow is regulated, namely the upper limit value of the refrigerating water flow of the threshold of the refrigerating water valve; vrThe total running water flow of the chilled water valve is the water flow output value at the moment of implementing the limiting of the chilled water valve; delta3The value range is 20% -80% as a correction value; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
frequency conversion control of fan
After the fan is subjected to frequency conversion control, based on the relation between the fan frequency and the power, and considering the influence of the self-reduction capacity of the fan on the output of the air conditioner host, the fan load reduction calculation is as shown in the formula:
ΔPf＝∑(1-(f1/f)3)·Pb+δ4·Pm
in the formula, Δ PfThe power is reduced for the frequency conversion control of the fan, and the unit is kW; f. of1The unit is Hz of limited operation frequency after the frequency conversion of the fan; f is a reference frequency in Hz; pbThe unit is kW for the reference load of the fan; delta4Correction value of 5%, when f1If f is equal to 0, the frequency conversion control of the fan is not carried out; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
2) illumination device
ΔpLnFor lighting n, n being less than the total number of lighting operations, PLIs a constant;
(4) constraint conditions
ΔpaN+ΔpLn≥Δp
Wherein: and the delta P is a load reduction instruction issued by the platform to the gateway.
Preferably, the specific steps of step two are as follows:
user comfort is characterized by a load reduction, a smaller reduction amount indicates a higher comfort, and the objective function is as follows:
Z1＝λ1(N+n)+λ2(Δpa+ΔpL)
Δpa＝Δpa1+...+ΔpaN
ΔpL＝ΔpL1+...+ΔpLn
in the formula: Δ paTotal load, Δ P, reducible for air conditioninga1Representing the amount of load the air conditioner 1 can reduce, …, Δ paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation; Δ pLTotal load, Δ P, reducible for illuminationLnRepresenting the amount of load that the lighting 1 can cut, …, Δ pLnThe load amount that n can be reduced for illumination, n being less than the total number of illumination runs; lambda [ alpha ]1、λ2Is selected by the user as a weight coefficient.
Preferably, the specific steps of step three are as follows:
1) initializing a particle swarm, and setting the number of particles, the initial position of the particle swarm and the initial speed of the particle swarm;
2) calculating the fitness of each particle, and searching the individual extreme value of the current particle and the global extreme value of the current particle;
3) calculating a velocity of the particle update and a position of the particle update;
4) determining whether the velocity of the particles exceeds a set velocity limit or whether the position of the particles exceeds a set position limit: if the speed of the particles exceeds the set speed limit value, setting the speed of the particles as the speed limit value, updating the individual extreme value of the particles and the global extreme value of the particles, and otherwise, updating the individual extreme value of the particles and the global extreme value of the particles; if the position of the particle exceeds the set position limit value, setting the position of the particle as the position limit value, updating the individual extreme value of the particle and the global extreme value of the particle, and otherwise, updating the individual extreme value of the particle and the global extreme value of the particle;
5) judging whether the set maximum iteration number is reached: if the set maximum iteration times are reached, outputting a minimum global extreme value, and finishing the operation; and if the set maximum iteration number is not reached, the step 3) is executed.
The invention achieves the following beneficial effects:
the invention provides a multi-factor considered multi-target equipment group optimization control method, which determines constraint conditions of optimization regulation and control by establishing a mathematical model of large-user energy equipment, provides an energy equipment optimization regulation and control objective function, realizes the optimization regulation and control of the energy equipment, and effectively solves the problem that the prior optimization regulation and control cannot solve the problem of considering both the regulation and control quantity of the large-user energy equipment and the user comfort level.
Drawings
FIG. 1 is a flow chart of the present method;
fig. 2 is a flow chart of step three of the method.
Detailed Description
The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
A multi-target equipment group optimization control method considering multiple factors comprises the following steps:
step one, establishing a mathematical model of the large-user energy equipment, and determining constraint conditions;
analyzing an energy consumption equipment optimization target based on the quantity of the regulation and control equipment and the comfort level of a user;
and step three, realizing the optimized regulation and control of the large-user energy utilization equipment.
Preferably, the determining the constraint condition in the first step includes the following steps:
(5) energy consumption equipment mathematical model
1) When the energy utilization equipment is an air conditioner, the following definitions are defined:
ΔpaN＝Δpt+Δpw+Δpv+Δpf
in the formula: Δ paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation;
global temperature control/increase of supply air temperature
Based on the control of the room temperature at 26 ℃, the energy consumption of the air conditioner is reduced by about 7% when the set temperature of the air conditioner is increased by 1 ℃, and the load reduction calculation is shown as the following formula:
ΔPt＝7％×Pair·ΔT
in the formula, Δ PtThe unit is kW for reducing the power of the air conditioner; pairFor the power of the air-conditioning system, all air conditionersThe sum of the powers, in kW; the delta T is an upward adjustment value of the air conditioner temperature and is expressed in unit; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
② increasing the temperature of the chilled water
For a constant-power centrifugal cold water refrigerating unit, the energy can be saved by 0.91-1.97% when the outlet water temperature of the chilled water is raised by 1 ℃, and the load reduction calculation of the constant-power centrifugal cold water refrigerating unit is shown as the following formula:
ΔPw＝ΔT'·δ2·Pm
in the formula: delta PwThe unit of the power reduced by the constant-power centrifugal cold water refrigerating unit is kW for increasing the temperature of the chilled water; delta T' is the upward adjustment quantity of the outlet water temperature of the chilled water, and the unit is; delta2The value range is 0.91% -1.97% for the corrected value; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
③ freezing water supply flow
The chilled water valve limits the flow of cold water flowing into the fan heat exchange coil, the load reduction effect is reduced to a certain extent by adjusting the flow of the cold water, and the load reduction calculation of the chilled water valve is as shown in the formula:
ΔPv＝(Va/Vr)·δ3·Pm
in the formula, Δ PvThe power is reduced for the adjustment of a chilled water valve, and the unit is kW; vaThe total amount of the water flow is regulated, namely the upper limit value of the refrigerating water flow of the threshold of the refrigerating water valve; vrThe total running water flow of the chilled water valve is the water flow output value at the moment of implementing the limiting of the chilled water valve; delta3The value range is 20% -80% as a correction value; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
frequency conversion control of fan
After the fan is subjected to frequency conversion control, based on the relation between the fan frequency and the power, and considering the influence of the self-reduction capacity of the fan on the output of the air conditioner host, the fan load reduction calculation is as shown in the formula:
ΔPf＝∑(1-(f1/f)3)·Pb+δ4·Pm
in the formula, Δ PfThe power is reduced for the frequency conversion control of the fan, and the unit is kW; f. of1The unit is Hz of limited operation frequency after the frequency conversion of the fan; f is a reference frequency in Hz; pbThe unit is kW for the reference load of the fan; delta4Correction value of 5%, when f1If f is equal to 0, the frequency conversion control of the fan is not carried out; pmIs the rated power of the air conditioner.
Preferably, the determining the constraint condition in the first step includes the following steps:
2) illumination device
ΔpLnFor lighting n, n being less than the total number of lighting operations, PLIs a constant;
(6) constraint conditions
ΔpaN+ΔpLn≥Δp
Wherein: and the delta P is a load reduction instruction issued by the platform to the gateway.
Preferably, the specific steps of step two are as follows:
user comfort is characterized by a load reduction, a smaller reduction amount indicates a higher comfort, and the objective function is as follows:
Z1＝λ1(N+n)+λ2(Δpa+ΔpL)
Δpa＝Δpa1+...+ΔpaN
ΔpL＝ΔpL1+...+ΔpLn
in the formula: Δ paTotal load, Δ P, reducible for air conditioninga1Representative air conditioner 1 being reducibleLoad, …,. DELTA.paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation; Δ pLTotal load, Δ P, reducible for illuminationLnRepresenting the amount of load that the lighting 1 can cut, …, Δ pLnThe load amount that n can be reduced for illumination, n being less than the total number of illumination runs; lambda [ alpha ]1、λ2Is selected by the user as a weight coefficient.
Preferably, the specific steps of step three are as follows:
1) initializing a particle swarm, and setting the number of particles, the initial position of the particle swarm and the initial speed of the particle swarm;
2) calculating the fitness of each particle, and searching the individual extreme value of the current particle and the global extreme value of the current particle;
3) calculating a velocity of the particle update and a position of the particle update;
4) determining whether the velocity of the particles exceeds a set velocity limit or whether the position of the particles exceeds a set position limit: if the speed of the particles exceeds the set speed limit value, setting the speed of the particles as the speed limit value, updating the individual extreme value of the particles and the global extreme value of the particles, and otherwise, updating the individual extreme value of the particles and the global extreme value of the particles; if the position of the particle exceeds the set position limit value, setting the position of the particle as the position limit value, updating the individual extreme value of the particle and the global extreme value of the particle, and otherwise, updating the individual extreme value of the particle and the global extreme value of the particle;
5) judging whether the set maximum iteration number is reached: if the set maximum iteration times are reached, outputting a minimum global extreme value, and finishing the operation; and if the set maximum iteration number is not reached, the step 3) is executed.
The third step comprises the following specific steps:
solving the established mathematical model of the large-user energy equipment by selecting a particle swarm optimization algorithm, wherein the flow is as follows:
(1) the particle code is mainly divided into three parts, wherein the first part represents the position of the particle in the search space and represents an optimization variable; the second part represents the position of the particles in the target space, i.e. the respective objective function value; the third part represents particle density information, here useful only for particles of the outer particle population;
(2) particle population initialization
The method comprises the steps of firstly, setting the boundaries according to the global temperature, the water supply flow and the lighting, wherein the boundaries are set according to the user experience. Randomly generating a set value of a control variable in the particles, and initializing all the particles according to the process;
(3) calculating a fitness value
The value of the fitness function is the basis of the particle swarm algorithm for guiding the search direction, and the target function is directly selected as the fitness function.
The main air conditioner unit is relative to the tail end of an air conditioner or an indoor unit, and the main air conditioner unit mainly refers to a large-scale air conditioner unit or an outdoor unit of a multi-split air conditioner. The air conditioner main machine is provided with a condenser, a fan motor, a compressor, a main machine electric control component and the like.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A multi-target equipment group optimization control method considering multiple factors is characterized by comprising the following steps:
step one, establishing a mathematical model of the large-user energy equipment, and determining constraint conditions;
analyzing an energy consumption equipment optimization target based on the quantity of the regulation and control equipment and the comfort level of a user;
and step three, realizing the optimized regulation and control of the large-user energy utilization equipment.
2. The method as claimed in claim 1, wherein the step of determining the constraint condition comprises the steps of:
(1) energy consumption equipment mathematical model
1) When the energy utilization equipment is an air conditioner, the following definitions are defined:
ΔpaN＝Δpt+Δpw+Δpv+Δpf
in the formula: Δ paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation;
global temperature control/increase of supply air temperature
Based on the control of the room temperature at 26 ℃, the energy consumption of the air conditioner is reduced by about 7% when the set temperature of the air conditioner is increased by 1 ℃, and the load reduction calculation is shown as the following formula:
ΔPt＝7％×Pair·ΔT
in the formula, Δ PtThe unit is kW for reducing the power of the air conditioner; pairThe unit is the power of an air conditioning system, is the sum of all air conditioning powers and is kW; and delta T is an upward adjustment value of the air conditioner temperature and has the unit of ℃.
3. The method as claimed in claim 1, wherein the step of determining the constraint condition comprises the steps of:
② increasing the temperature of the chilled water
For a constant-power centrifugal cold water refrigerating unit, the energy can be saved by 0.91-1.97% when the outlet water temperature of the chilled water is raised by 1 ℃, and the load reduction calculation of the constant-power centrifugal cold water refrigerating unit is shown as the following formula:
ΔPw＝ΔT'·δ2·Pm
in the formula: delta PwThe unit of the power reduced by the constant-power centrifugal cold water refrigerating unit is kW for increasing the temperature of the chilled water; delta T' is the upward adjustment quantity of the outlet water temperature of the chilled water, and the unit is; delta2The value range is 0.91% -1.97% for the corrected value; pmIs the rated power of the air conditioner.
4. The method as claimed in claim 1, wherein the step of determining the constraint condition comprises the steps of:
③ freezing water supply flow
The chilled water valve limits the flow of cold water flowing into the fan heat exchange coil, the load reduction effect is reduced to a certain extent by adjusting the flow of the cold water, and the load reduction calculation of the chilled water valve is as shown in the formula:
ΔPv＝(Va/Vr)·δ3·Pm
in the formula, Δ PvThe power is reduced for the adjustment of a chilled water valve, and the unit is kW; vaThe total amount of the water flow is regulated, namely the upper limit value of the refrigerating water flow of the threshold of the refrigerating water valve; vrThe total running water flow of the chilled water valve is the water flow output value at the moment of implementing the limiting of the chilled water valve; delta3The value range is 20% -80% as a correction value; pmIs the rated power of the air conditioner.
5. The method as claimed in claim 1, wherein the step of determining the constraint condition comprises the steps of:
frequency conversion control of fan
After the fan is subjected to frequency conversion control, based on the relation between the fan frequency and the power, and considering the influence of the self-reduction capacity of the fan on the output of the air conditioner host, the fan load reduction calculation is as shown in the formula:
ΔPf＝∑(1-(f1/f)3)·Pb+δ4·Pm
in the formula, Δ PfThe power is reduced for the frequency conversion control of the fan, and the unit is kW; f. of1The unit is Hz of limited operation frequency after the frequency conversion of the fan; f is a reference frequency in Hz; pbThe unit is kW for the reference load of the fan; delta4Correction value of 5%, when f1If f is equal to 0, the frequency conversion control of the fan is not carried out; pmIs the rated power of the air conditioner.
6. The method as claimed in claim 1, wherein the step of determining the constraint condition comprises the steps of:
2) illumination device
ΔpLnFor lighting n, n being less than the total number of lighting operations, PLIs a constant;
(2) constraint conditions
ΔpaN+ΔpLn≥ΔP
Wherein: and the delta P is a load reduction instruction issued by the platform to the gateway.
7. The method as claimed in claim 1, wherein the second step comprises the following steps:
user comfort is characterized by a load reduction, a smaller reduction amount indicates a higher comfort, and the objective function is as follows:
Z1＝λ1(N+n)+λ2(Δpa+ΔpL)
Δpa＝Δpa1+...+ΔpaN
ΔpL＝ΔpL1+...+ΔpLn
in the formula: Δ paTotal load, Δ P, reducible for air conditioninga1Representing the amount of load the air conditioner 1 can reduce, …, Δ paNThe load amount of the air conditioner N which can be reduced is less than the total number of the air conditioners in operation; Δ pLTotal load, Δ P, reducible for illuminationLnRepresenting the amount of load that the lighting 1 can cut, …, Δ pLnThe load amount that n can be reduced for illumination, n being less than the total number of illumination runs; lambda [ alpha ]1、λ2Is selected by the user as a weight coefficient.
8. The multi-objective device group optimization control method considering multiple factors according to claim 1, wherein the concrete steps of the third step are as follows:
1) initializing a particle swarm, and setting the number of particles, the initial position of the particle swarm and the initial speed of the particle swarm;
2) calculating the fitness of each particle, and searching the individual extreme value of the current particle and the global extreme value of the current particle;
3) calculating a velocity of the particle update and a position of the particle update;
4) determining whether the velocity of the particles exceeds a set velocity limit or whether the position of the particles exceeds a set position limit: if the speed of the particles exceeds the set speed limit value, setting the speed of the particles as the speed limit value, updating the individual extreme value of the particles and the global extreme value of the particles, and otherwise, updating the individual extreme value of the particles and the global extreme value of the particles; if the position of the particle exceeds the set position limit value, setting the position of the particle as the position limit value, updating the individual extreme value of the particle and the global extreme value of the particle, and otherwise, updating the individual extreme value of the particle and the global extreme value of the particle;
5) judging whether the set maximum iteration number is reached: if the set maximum iteration times are reached, outputting a minimum global extreme value, and finishing the operation; and if the set maximum iteration number is not reached, the step 3) is executed.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778631A (en) * | 2015-03-11 | 2015-07-15 | 国家电网公司 | Method for optimizing power utilization modes of residential users with orientation to demand response |
| CN110848895A (en) * | 2019-11-26 | 2020-02-28 | 国网江苏省电力有限公司电力科学研究院 | Non-industrial air conditioner flexible load control method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778631A (en) * | 2015-03-11 | 2015-07-15 | 国家电网公司 | Method for optimizing power utilization modes of residential users with orientation to demand response |
| CN110848895A (en) * | 2019-11-26 | 2020-02-28 | 国网江苏省电力有限公司电力科学研究院 | Non-industrial air conditioner flexible load control method and system |
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