CN116544935B - Optimization control method and device for motor loads participating in demand response - Google Patents
Optimization control method and device for motor loads participating in demand response Download PDFInfo
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- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention relates to the technical field of electric energy-saving application containing motor loads, and particularly provides an optimization control method and device for motor loads participating in demand response, wherein the method comprises the following steps: determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load; substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load; substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load; and optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load. The technical scheme provided by the invention can realize low-cost transformation and upgrading of the frequency conversion device and improve the interaction capability with a power grid.
Description
Technical Field
The invention relates to the technical field of electric energy-saving application of loads containing motors, in particular to an optimal control method and device for the loads of the motors participating in demand response.
Background
As the 'new electrification' process of the demand side increases in speed, power electronics are widely used in power terminals. The industrial and commercial energy systems such as the large commercial industry and the industrial park comprise a large number of motor loads with adjustable rotating speeds such as air-conditioning water pumps, elevators, ventilators and the like, which are one of important electric energy application terminals, and the loads are usually regulated by adopting an alternating current-alternating current frequency conversion device, so that the speed regulation is high in controllability, and the system is an ideal object for reducing peak load.
The existing variable-frequency speed regulating device needs to be manually set in advance and is regulated to operate according to the needs. Aiming at the requirement of the variable frequency load on demand response, the prior device lacks of a signal receiving interface and the embedding of a requirement response adjusting technology, lacks of an information channel participating in the requirement response, and has difficult active adjustment.
The requirement response adjustment of the power electronic device can be realized by changing the original conventional frequency conversion device into a requirement response type frequency converter, but the cost of replacing the frequency converter is higher and the frequency converter has certain potential safety hazard for an installed complete speed regulation system. On the basis, the frequency conversion device can be upgraded and improved through the external additional installation of the adjusting module, and the adjusting requirement of load side resources is met. The prior proposal is as follows:
(1) Scheme 1: novel load response terminal. By integrating the functions of multipath signals, instruction receiving and transmitting, demand response technology and the like, various loads are ensured to participate in load demand response. The existing system is not required to be modified, only the original load control system is required to be accessed, but the scene which can be regulated and controlled by the existing terminal is single, and the difficulty and the cost of the regulation and control are high due to the fact that a user is often invited to actively regulate or passively control the load.
(2) Scheme 2: novel wisdom load unit. The system is oriented to high-energy-consumption industrial and commercial users, and a set of functional modules for supporting internet of things communication, data acquisition, logic operation and operation control are installed to finely sense the power load change, monitor sampling analysis data and automatically perform fusion services such as demand response, load control, safe power utilization, reactive power management, energy efficiency management and the like. However, the current units often adopt global control of a combined superior system, a power consumption main body is required to realize digital infrastructure in advance, and the transformation cost is high.
The existing adjusting module scheme can realize miniaturization of the terminal, but the adopted demand response technology has single interaction mode, more executing and converting mechanisms of upper adjusting signals are adopted, and the fine adjusting demands of user characteristic constraint are difficult to meet; meanwhile, the novel load management unit can realize response diversification, but the adopted business framework is too complicated and bloated, and the response functions of automatic analysis and automatic intelligent decision under multiple scenes and multiple signals can not be realized only by a single module.
Disclosure of Invention
In order to overcome the defects, the invention provides an optimized control method and device for motor loads participating in demand response.
In a first aspect, there is provided a method for optimally controlling a motor class load involved in demand response, the method for optimally controlling a motor class load involved in demand response comprising:
determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load;
substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load;
and optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load.
Preferably, the calculation formula of the adjustable potential of the motor load under the current set satisfaction is as follows:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Further, when the motor load is a water pump load, the minimum required running power of the motor load is: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min And r, the maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balance factor, G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
Preferably, the pre-constructed day-ahead scheduling plan model includes: the first objective function and the first constraint condition configured for motor class load optimization control participating in demand response.
Further, the mathematical model of the first objective function is as follows:
minF 1 =∑ i m 1 (C 1 +C 2 -C 3 )P worki +m 2 ∑ i PMV i
P day-front =∑ i P worki
in the above, PMV i Satisfaction degree F for current setting of ith motor class load 1 For the first objective function value, m 1 For cost weight, i.e. [1, n ]]N is the total number of motor class loads participating in demand response,C 1 for the running cost of the equipment, C 2 For equipment maintenance cost, C 3 Is patch for time-staggered, P worki Working power m for day-ahead running of ith motor class load 2 To weight satisfaction, P day-front The operating power is planned for the future of the motor type load.
Further, the mathematical model of the first constraint is as follows:
∑ i P ratei -P day-front ≤P L
V min ≤V≤V max
in the above, P ratei Rated output for load of ith motor class, P L The potential of the motor type load can be adjusted under the current set satisfaction degree, V is the rotating speed of the motor type load, and V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is set.
Preferably, the pre-built daily operation optimization model includes: and optimizing a second objective function and a second constraint condition configured for the motor class load participating in the demand response.
Further, the mathematical model of the second objective function is as follows:
minF 2 =∑ t (P day-front-t - P day-in-t )+f
in the above, F 2 For the second objective function value, P day-front-t Planned daily operating power for a motor-like load at time t, P day-in-t The power is optimized for the daily rolling of the motor load at the moment t, and f is the additional power required by new energy self-control regulation.
Further, the mathematical model of the second constraint is as follows:
∣P day-in-t -P day-in-t-1 ∣≤r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )≤P day-in-t
in the above, P day-in-t-1 Optimizing power for daily rolling of motor loads at t-1 moment, wherein r is climbing speed and P is Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Further, when a load demand response signal is received, solving a pre-built adjustment optimization model to obtain the daily rolling optimization power of the motor load;
wherein the pre-built adjustment optimization model comprises: and optimizing a third objective function and a third constraint condition configured for controlling the motor class load participating in the demand response.
Further, the mathematical model of the third objective function is as follows:
minF 3 =∣P grid-t -P day-in-t ∣
in the above, F 3 For the third objective function value, P grid-t And scheduling power for the daily requirement of the power grid of the motor type load at the moment t.
Further, the mathematical model of the third constraint is as follows:
T res ≤T r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )≤P day-in-t
in the above, T res For response time, T r The upper layer is given a maximum response time.
Preferably, the optimizing adjustment of the motor load based on the daily rolling optimizing power of the motor load comprises:
Determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
in the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
and in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
Further, the calculation formula of the motor load start-stop regulation and control behavior coefficient is as follows:
ξ i =[(ξ ia ) 2 +(ξ ib ) 2 +(ξ ic ) 2 ] 1/2
in the above, xi i For the i-th motor load start-stop regulation and control action coefficient, xi ia For the satisfaction degree state quantity of the ith motor type load, xi ib For the quantity state quantity of the ith motor class load, xi ic The energy consumption demand state quantity of the i motor type load.
Further, the calculation formula of the satisfaction degree state quantity of the ith motor type load is as follows:
ξ ia =PMV i /PMV ref
The calculation formula of the quantity state quantity of the ith motor type load is as follows:
ξ ib =n/n ref
the calculation formula of the energy consumption demand state quantity of the ith motor type load is as follows:
ξ ic = [∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )]/P rate
in the above, n ref For the total number of motor loads reference value, PMV ref For satisfaction reference value of motor load, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
In a second aspect, there is provided an optimal control device for motor class loads involved in demand response, the optimal control device for motor class loads involved in demand response comprising:
the determining module is used for determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
The first analysis module is used for substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduled running power of the motor load;
the second analysis module is used for substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving the model to obtain daily rolling optimized power of the motor load;
and the adjusting module is used for optimally adjusting the motor load based on the daily rolling optimal power of the motor load.
Preferably, the determining module is specifically configured to: the adjustable potential of the motor class load under the current set satisfaction is calculated according to the following formula:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Further, when the motor load is a water pump load, the minimum required running power of the motor load is: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min And r, the maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balanceCoefficient G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
Preferably, the pre-constructed day-ahead scheduling plan model includes: the first objective function and the first constraint condition configured for motor class load optimization control participating in demand response.
Preferably, the pre-built daily operation optimization model includes: and optimizing a second objective function and a second constraint condition configured for the motor class load participating in the demand response.
Further, when a load demand response signal is received, solving a pre-built adjustment optimization model to obtain the daily rolling optimization power of the motor load;
wherein the pre-built adjustment optimization model comprises: and optimizing a third objective function and a third constraint condition configured for controlling the motor class load participating in the demand response.
Preferably, the adjustment module is particularly adapted to
Determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
In the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
and in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
In a third aspect, there is provided a computer device comprising: one or more processors;
the processor is used for storing one or more programs;
and when the one or more programs are executed by the one or more processors, the motor type load optimization control method participating in the demand response is realized.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored, the computer program, when executed, implementing the method for optimally controlling the motor class load involved in demand response.
The technical scheme provided by the invention has at least one or more of the following beneficial effects:
the invention provides an optimization control method and device for motor loads participating in demand response, comprising the following steps: determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load; substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load; substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load; and optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load. According to the technical scheme provided by the invention, user regulation will and satisfaction application requirements of energy utilization equipment of different partitions are comprehensively researched and judged, regulation and control partition sequencing is performed, differential regulation is realized, and further:
The invention can be applied to various scenes of industrial and commercial main bodies such as large shops and office buildings, and motor-contained speed regulation loads (such as air-conditioning water pumps, elevators and ventilators) of industrial parks. The intelligent regulation of the terminal frequency converter equipment and the flexible interactive operation with the power grid can be realized, and the interactive response and the fine regulation capability of the terminal frequency conversion speed regulation type load can be improved through low-cost transformation. Compared with the transformation scheme for replacing the frequency converter device, the integrated terminal applying the technical scheme of the invention can save 90%, and the plug-and-play unit module can improve the transformation efficiency by 50%.
Drawings
FIG. 1 is a schematic flow chart of main steps of a motor load optimization control method participating in demand response according to an embodiment of the present invention;
FIG. 2 is a hardware architecture diagram of a demand-responsive intelligent control unit according to an embodiment of the present invention;
FIG. 3 is a block diagram of a functional module of a demand-responsive intelligent control unit according to an embodiment of the present invention;
fig. 4 is a main block diagram of a motor load optimizing control device participating in demand response according to an embodiment of the present invention.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As disclosed in the background art, as the demand side 'new electrification' progresses faster, power electronics devices are widely used in power terminals. The industrial and commercial energy systems such as the large commercial industry and the industrial park comprise a large number of motor loads with adjustable rotating speeds such as air-conditioning water pumps, elevators, ventilators and the like, which are one of important electric energy application terminals, and the loads are usually regulated by adopting an alternating current-alternating current frequency conversion device, so that the speed regulation is high in controllability, and the system is an ideal object for reducing peak load.
The existing variable-frequency speed regulating device needs to be manually set in advance and is regulated to operate according to the needs. Aiming at the requirement of the variable frequency load on demand response, the prior device lacks of a signal receiving interface and the embedding of a requirement response adjusting technology, lacks of an information channel participating in the requirement response, and has difficult active adjustment.
The requirement response adjustment of the power electronic device can be realized by changing the original conventional frequency conversion device into a requirement response type frequency converter, but the cost of replacing the frequency converter is higher and the frequency converter has certain potential safety hazard for an installed complete speed regulation system. On the basis, the frequency conversion device can be upgraded and improved through the external additional installation of the adjusting module, and the adjusting requirement of load side resources is met. The prior proposal is as follows:
(1) Scheme 1: novel load response terminal. By integrating the functions of multipath signals, instruction receiving and transmitting, demand response technology and the like, various loads are ensured to participate in load demand response. The existing system is not required to be modified, only the original load control system is required to be accessed, but the scene which can be regulated and controlled by the existing terminal is single, and the difficulty and the cost of the regulation and control are high due to the fact that a user is often invited to actively regulate or passively control the load.
(2) Scheme 2: novel wisdom load unit. The system is oriented to high-energy-consumption industrial and commercial users, and a set of functional modules for supporting internet of things communication, data acquisition, logic operation and operation control are installed to finely sense the power load change, monitor sampling analysis data and automatically perform fusion services such as demand response, load control, safe power utilization, reactive power management, energy efficiency management and the like. However, the current units often adopt global control of a combined superior system, a power consumption main body is required to realize digital infrastructure in advance, and the transformation cost is high.
The existing adjusting module scheme can realize miniaturization of the terminal, but the adopted demand response technology has single interaction mode, more executing and converting mechanisms of upper adjusting signals are adopted, and the fine adjusting demands of user characteristic constraint are difficult to meet; meanwhile, the novel load management unit can realize response diversification, but the adopted business framework is too complicated and bloated, and the response functions of automatic analysis and automatic intelligent decision under multiple scenes and multiple signals can not be realized only by a single module.
Currently, there are patents for electrical energy conservation applications involving loads of the motor type, such as:
patent: user intelligent power consumption terminal CN 107706922A based on automatic demand response
The invention designs an intelligent electricity utilization terminal of a user side based on automatic demand response, the terminal receives load shedding information and household equipment cutting-off priority, a demand response program is stored in a terminal processor, different cutting-off priorities are set according to electricity utilization conditions, when the load shedding information is received, a precompiled response strategy is automatically triggered and executed, and when the user side manually or automatically cuts off electric equipment, the automatic balance of an electricity utilization line is realized in peak hours.
The invention in the technology only considers two working conditions of on-off of the user load, and does not consider strategies such as autonomously adjusting frequency and the like under the complex electric field scene to realize energy saving and consumption reduction, can not support the normal operation of the load, can not meet the diversified regulation and control requirements of the electric equipment in the user, and has poor control refinement and precision level.
For another example: patent: power utilization management and control terminal and system supporting automatic demand response and load identification method CN 108964276B
The invention designs a power consumption management and control terminal supporting automatic demand response, which comprises an intelligent host, a plug-and-play external terminal and other devices, wherein voltage and current information of a power consumption is captured by the external terminal and is sent to the intelligent host in a communication mode, and the intelligent host analyzes loads to determine real-time load types; the power utilization information is summarized through an indoor network and uploaded to a server, and the power departments acquire and regulate the information, so that the classification, time sharing, partition and grading real-time monitoring and on-line management of the power utilization load are realized.
The invention in the technology does not uniformly package the plug-and-play external terminal and the intelligent host, data are required to be transmitted to the switchboard based on the internet of things technology, a central control system and an electric power department server are highly dependent, and autonomous regulation and control of the electricity utilization user participation demand response equipment cannot be realized in a decentralization mode.
In order to improve the above problems, the present invention provides a method and an apparatus for optimally controlling a motor load participating in demand response, including: determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load; substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load; substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load; and optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load. According to the technical scheme provided by the invention, user regulation will and satisfaction application requirements of energy utilization equipment of different partitions are comprehensively researched and judged, regulation and control partition sequencing is performed, differential regulation is realized, and further:
the invention can be applied to various scenes of industrial and commercial main bodies such as large shops and office buildings, and motor-contained speed regulation loads (such as air-conditioning water pumps, elevators and ventilators) of industrial parks. The intelligent regulation of the terminal frequency converter equipment and the flexible interactive operation with the power grid can be realized, and the interactive response and the fine regulation capability of the terminal frequency conversion speed regulation type load can be improved through low-cost transformation. Compared with the transformation scheme for replacing the frequency converter device, the integrated terminal applying the technical scheme of the invention can save 90%, and the plug-and-play unit module can improve the transformation efficiency by 50%.
The above-described scheme is explained in detail below.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of main steps of a method for optimally controlling a motor class load participating in demand response according to an embodiment of the present invention. As shown in fig. 1, the method for optimally controlling the motor loads participating in demand response in the embodiment of the invention mainly comprises the following steps:
step S101: determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
step S102: substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load;
step S103: substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load;
step S104: and optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load.
In this embodiment, the calculation formula of the adjustable potential of the motor load under the current set satisfaction is as follows:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
In one embodiment, when the motor class load is a water pump class load, the minimum required operating power of the motor class load is: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min /r, getThe maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balance factor, G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
In this embodiment, the pre-constructed day-ahead scheduling model includes: the first objective function and the first constraint condition configured for motor class load optimization control participating in demand response.
In one embodiment, the mathematical model of the first objective function is as follows:
minF 1 =∑ i m 1 (C 1 +C 2 -C 3 )P worki +m 2 ∑ i PMV i
P day-front =∑ i P worki
in the above, PMV i Satisfaction degree F for current setting of ith motor class load 1 For the first objective function value, m 1 For cost weight, i.e. [1, n ]]N is the total number of motor loads participating in demand response, C 1 For the running cost of the equipment, C 2 For equipment maintenance cost, C 3 Is patch for time-staggered, P worki Working power m for day-ahead running of ith motor class load 2 To weight satisfaction, P day-front The operating power is planned for the future of the motor type load.
In one embodiment, the mathematical model of the first constraint is as follows:
∑ i P ratei -P day-front ≤P L
V min ≤V≤V max
in the above, P ratei Rated output for load of ith motor class, P L The potential of the motor type load can be adjusted under the current set satisfaction degree, V is the rotating speed of the motor type load, and V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is set.
In this embodiment, the pre-built intra-day operation optimization model includes: and optimizing a second objective function and a second constraint condition configured for the motor class load participating in the demand response.
In one embodiment, the mathematical model of the second objective function is as follows:
minF 2 =∑ t (P day-front-t - P day-in-t )+f
in the above, F 2 For the second objective function value, P day-front-t Planned daily operating power for a motor-like load at time t, P day-in-t The power is optimized for the daily rolling of the motor load at the moment t, and f is the additional power required by new energy self-control regulation.
In one embodiment, the mathematical model of the second constraint is as follows:
∣P day-in-t -P day-in-t-1 ∣≤r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin ) ≤P day-in-t
In the above, P day-in-t-1 Optimizing power for daily rolling of motor loads at t-1 moment, wherein r is climbing speed and P is Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax For the most part of the userSatisfactory operating power to rated power ratio, PMV Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
In one embodiment, when the load demand response signal is received, the control priority is the highest, and the optimization operation should be performed according to the adjustment task of the power grid, so as to determine the scheduling target at the current moment. The remote signal is sent to the intelligent equipment control unit of the terminal, and the load of the variable-frequency speed-regulating motor adjusts the rotating speed of the motor so as to meet the requirement of a dispatching signal. Therefore, when a load demand response signal is received, solving a pre-constructed regulation optimization model to obtain the daily rolling optimization power of the motor load;
Wherein the pre-built adjustment optimization model comprises: and optimizing a third objective function and a third constraint condition configured for controlling the motor class load participating in the demand response.
In one embodiment, the mathematical model of the third objective function is as follows:
minF 3 =∣P grid-t -P day-in-t ∣
in the above, F 3 For the third objective function value, P grid-t And scheduling power for the daily requirement of the power grid of the motor type load at the moment t.
In one embodiment, the mathematical model of the third constraint is as follows:
T res ≤T r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin ) ≤P day-in-t
in the above, T res For response time, T r The upper layer is given a maximum response time.
In this embodiment, the optimizing adjustment of the motor load based on the daily rolling optimized power of the motor load includes:
determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
in the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
And in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
In one embodiment, the calculation formula of the motor load start-stop regulation and control behavior coefficient is as follows:
ξ i =[(ξ ia ) 2 +(ξ ib ) 2 +(ξ ic ) 2 ] 1/2
in the above, xi i For the i-th motor load start-stop regulation and control action coefficient, xi ia For the satisfaction degree state quantity of the ith motor type load, xi ib For the quantity state quantity of the ith motor class load, xi ic The energy consumption demand state quantity of the i motor type load.
In one embodiment, the satisfaction state quantity of the ith motor class load is calculated as follows:
ξ ia =PMV i /PMV ref
the calculation formula of the quantity state quantity of the ith motor type load is as follows:
ξ ib =n/n ref
the calculation formula of the energy consumption demand state quantity of the ith motor type load is as follows:
ξ ic = [∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )]/P rate
in the above, n ref For the total number of motor loads reference value, PMV ref For satisfaction reference value of motor load, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
In an optimal implementation manner, the invention further provides a plug-and-play demand response type intelligent management and control unit based on the optimized control of the motor type load participating in the demand response, and the intelligent management and control unit is specifically:
system architecture
The plug-and-play demand response type intelligent control unit can autonomously detect bus voltage, bus current, frequency and rotating speed, can receive demand response instructions, and can receive local new energy source main regulation signals and electricity price signals, and support digital, analog and communication type multi-type load equipment interaction interfaces. The plug-and-play demand response type intelligent control unit can flexibly interface with the frequency converter equipment, and can automatically detect and allocate resources, i.e. plug-and-play. And the local and remote multi-type adjusting signals can be actively perceived, and an optimized running scheme of a load monomer or a load cluster is generated through a multi-signal multi-scene demand response adjusting method embedded by the unit.
Hardware structure
The hardware architecture of the plug-and-play demand-responsive intelligent management and control unit is shown in FIG. 2. The system comprises an embedded core control unit, an electrical parameter detection unit, a communication unit, a hardware protection unit, a power supply unit, a control interface unit, a data storage unit, a security encryption unit and the like. The communication unit supports a plurality of communication interfaces of CAN communication, 232 and 485, CAN perform bidirectional data transmission, and supports a plurality of data receiving and transmitting baud rates.
Functional module
The plug-and-play demand response intelligent management and control unit functional module is shown in fig. 3. The main functions are as follows:
(1) A universal interface. In order to meet the communication connection and control requirements of the multi-type typical converter equipment, multi-type communication units and multi-type control units covering switching values, voltage and current analog values, duty ratios and the like are required to be designed. Aiming at different electric equipment without manufacturers, the butt joint of the electric equipment and the terminal is convenient.
(2) Plug and play functions. The intelligent control unit with the demand response has the advantages that the intelligent control unit with the demand response is provided with a communication type interface, an analog type interface and a digital type interface which are in butt joint with the frequency conversion device, a complex handshake protocol is not needed in the butt joint process, and various control interfaces can realize automatic detection and resource allocation, so that the frequency converter receives resource allocation and instruction adjustment.
(3) And an uplink and downlink communication function. The downlink can receive a demand response instruction, a local new energy source main regulation signal and an electricity price signal, and the uplink can send the regulation depth, the regulation speed, the working state, the energy utilization information and the like of the frequency conversion equipment.
(4) And a local energy information acquisition function. The intelligent control unit needs to collect local energy consumption information, analyzes the energy consumption characteristics of the converter load equipment, and supports the adjustment depth calculation of the converter load equipment.
(5) Multi-scenario day-ahead plan optimization function. The unit can form a daily satisfaction type, an economic type and a compromise type control plan through sensing power grid information, energy consumption information and environment information, and form a daily instruction of self-optimization adjustment of the frequency conversion device.
(6) An active response function for multi-signal intelligent decision in the day. The daily optimization control mainly follows a daily schedule, and simultaneously senses various adjustment requirements such as power grid adjustment, price signals, park distributed energy signals and the like, and an intelligent management and control unit makes an independent decision to form a comprehensive adjustment instruction for responding to power grids, independent participation and park independent response.
(7) Load cluster differentiation regulation function. The method is oriented to a load cluster containing multiple frequency converter devices, and utilizes a state queue algorithm to comprehensively judge user regulation willingness of the current partition and satisfaction application requirements of energy utilization equipment, regulate and control the partition sequencing, and realize differential regulation.
Example 2
Based on the same inventive concept, the invention also provides an optimized control device of motor loads participating in demand response, as shown in fig. 4, the optimized control device of motor loads participating in demand response comprises:
the determining module is used for determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
the first analysis module is used for substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduled running power of the motor load;
the second analysis module is used for substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving the model to obtain daily rolling optimized power of the motor load;
and the adjusting module is used for optimally adjusting the motor load based on the daily rolling optimal power of the motor load.
Preferably, the calculation formula of the adjustable potential of the motor load under the current set satisfaction is as follows:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For the preset maximum satisfaction of the motor loadDegree of PMV (PMV) Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Further, when the motor load is a water pump load, the minimum required running power of the motor load is: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min And r, the maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balance factor, G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
Preferably, the pre-constructed day-ahead scheduling plan model includes: the first objective function and the first constraint condition configured for motor class load optimization control participating in demand response.
Further, the mathematical model of the first objective function is as follows:
minF 1 =∑ i m 1 (C 1 +C 2 -C 3 )P worki +m 2 ∑ i PMV i
P day-front =∑ i P worki
in the above, PMV i Satisfaction degree F for current setting of ith motor class load 1 For the first objective function value, m 1 For cost weight, i.e. [1, n ]]N is the total number of motor loads participating in demand response, C 1 For the running cost of the equipment, C 2 For equipment maintenance cost, C 3 Is patch for time-staggered, P worki Working power m for day-ahead running of ith motor class load 2 To weight satisfaction, P day-front The operating power is planned for the future of the motor type load.
Further, the mathematical model of the first constraint is as follows:
∑ i P ratei -P day-front ≤P L
V min ≤V≤V max
in the above, P ratei Rated output for load of ith motor class, P L The potential of the motor type load can be adjusted under the current set satisfaction degree, V is the rotating speed of the motor type load, and V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is set.
Preferably, the pre-built daily operation optimization model includes: and optimizing a second objective function and a second constraint condition configured for the motor class load participating in the demand response.
Further, the mathematical model of the second objective function is as follows:
minF 2 =∑ t (P day-front-t - P day-in-t )+f
in the above, F 2 For the second objective function value, P day-front-t Day-ahead planning for motor class load at time tRow power, P day-in-t The power is optimized for the daily rolling of the motor load at the moment t, and f is the additional power required by new energy self-control regulation.
Further, the mathematical model of the second constraint is as follows:
∣P day-in-t -P day-in-t-1 ∣≤r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin ) ≤P day-in-t
in the above, P day-in-t-1 Optimizing power for daily rolling of motor loads at t-1 moment, wherein r is climbing speed and P is Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Further, when a load demand response signal is received, solving a pre-built adjustment optimization model to obtain the daily rolling optimization power of the motor load;
wherein the pre-built adjustment optimization model comprises: and optimizing a third objective function and a third constraint condition configured for controlling the motor class load participating in the demand response.
Further, the mathematical model of the third objective function is as follows:
minF 3 =∣P grid-t -P day-in-t ∣
in the above, F 3 For the third objective function value, P grid-t And scheduling power for the daily requirement of the power grid of the motor type load at the moment t.
Further, the mathematical model of the third constraint is as follows:
T res ＜T r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )＜P day-in-t
in the above, T res For response time, T r The upper layer is given a maximum response time.
Preferably, the optimizing adjustment of the motor load based on the daily rolling optimizing power of the motor load comprises:
determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
in the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
and in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
Further, the calculation formula of the motor load start-stop regulation and control behavior coefficient is as follows:
ξ i =[(ξ ia ) 2 +(ξ ib ) 2 +(ξ ic ) 2 ] 1/2
in the above, xi i For the i-th motor load start-stop regulation and control action coefficient, xi ia For the satisfaction degree state quantity of the ith motor type load, xi ib For the quantity state quantity of the ith motor class load, xi ic The energy consumption demand state quantity of the i motor type load.
Further, the calculation formula of the satisfaction degree state quantity of the ith motor type load is as follows:
ξ ia =PMV i /PMV ref
the calculation formula of the quantity state quantity of the ith motor type load is as follows:
ξ ib =n/n ref
the calculation formula of the energy consumption demand state quantity of the ith motor type load is as follows:
ξ ic = [∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )]/P rate
in the above, n ref For the total number of motor loads reference value, PMV ref For satisfaction reference value of motor load, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
Example 3
Based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of an optimized control method of motor class loads involved in demand response in the above embodiments.
Example 4
Based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a method for optimizing control of motor class loads involved in demand response in the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (13)
1. An optimized control method of motor loads participating in demand response, which is characterized by comprising the following steps:
determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduling operation power of the motor load;
substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving to obtain daily rolling optimization power of the motor load;
optimizing and adjusting the motor load based on the daily rolling optimizing power of the motor load;
the calculation formula of the adjustable potential of the motor type load under the current set satisfaction degree is as follows:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor loads participating in demand response;
the pre-constructed day-ahead dispatch plan model includes: optimizing a first objective function and a first constraint condition configured for controlling the motor class load participating in demand response;
the mathematical model of the first objective function is as follows:
minF 1 =∑ i m 1 (C 1 +C 2 -C 3 )P worki +m 2 ∑ i PMV i
P day-front =∑ i P worki
in the above, PMV i Satisfaction degree F for current setting of ith motor class load 1 For the first objective function value, m 1 For cost weight, i.e. [1, n ]]N is the total number of motor loads participating in demand response, C 1 For the running cost of the equipment, C 2 For equipment maintenance cost, C 3 Is patch for time-staggered, P worki Working power m for day-ahead running of ith motor class load 2 To weight satisfaction, P day-front Planned operating power for the day before of the motor class load;
the pre-built intra-day operation optimization model comprises the following steps: optimizing a second objective function and a second constraint condition configured for controlling the motor type load participating in the demand response;
The mathematical model of the second objective function is as follows:
minF 2 =∑ t (P day-front-t - P day-in-t )+f
in the above, F 2 For the second objective function value, P day-front-t Planned daily operating power for a motor-like load at time t, P day-in-t Optimizing power for daily rolling of motor loads at the moment t, wherein f is additional power required by new energy self-control regulation;
when a load demand response signal is received, solving a pre-built adjustment optimization model to obtain daily rolling optimization power of the motor load;
wherein the pre-built adjustment optimization model comprises: optimizing a third objective function and a third constraint condition configured for controlling the motor type load participating in the demand response;
the mathematical model of the third objective function is as follows:
minF 3 =∣P grid-t -P day-in-t ∣
in the above, F 3 For the third objective function value, P grid-t And scheduling power for the daily requirement of the power grid of the motor type load at the moment t.
2. The method of claim 1, wherein when the motor class load is a water pump class load, the minimum required operating power of the motor class load is: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min And r, the maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balance factor, G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
3. The method of claim 1, wherein the mathematical model of the first constraint is as follows:
∑ i P ratei -P day-front ≤P L
V min ≤V≤V max
in the above, P ratei Rated output for load of ith motor class, P L The potential of the motor type load can be adjusted under the current set satisfaction degree, V is the rotating speed of the motor type load, and V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is set.
4. The method of claim 1, wherein the mathematical model of the second constraint is as follows:
∣P day-in-t -P day-in-t-1 ∣≤r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )＜P day-in-t
in the above, P day-in-t-1 Optimizing power for daily rolling of motor loads at t-1 moment, wherein r is climbing speed and P is Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
5. The method of claim 1, wherein the mathematical model of the third constraint is as follows:
T res ≤T r
∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )≤P day-in-t
in the above, T res For response time, T r The upper layer is given a maximum response time.
6. The method of claim 1, wherein the optimally adjusting the motor class load based on the daily rolling optimal power of the motor class load comprises:
Determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
in the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
and in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
7. The method of claim 6, wherein the motor class load start-stop regulation behavior coefficient is calculated as follows:
ξ i =[(ξ ia ) 2 +(ξ ib ) 2 +(ξ ic ) 2 ] 1/2
in the above, xi i For the i-th motor load start-stop regulation and control action coefficient, xi ia For the satisfaction degree state quantity of the ith motor type load, xi ib For the quantity state quantity of the ith motor class load, xi ic The energy consumption demand state quantity of the i motor type load.
8. The method of claim 7, wherein the satisfaction state quantity of the i-th motor class load is calculated as follows:
ξ ia =PMV i /PMV ref
the calculation formula of the quantity state quantity of the ith motor type load is as follows:
ξ ib =n/n ref
the calculation formula of the energy consumption demand state quantity of the ith motor type load is as follows:
ξ ic = [∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )]/P rate
in the above, n ref For the total number of motor loads reference value, PMV ref For satisfaction reference value of motor load, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor class loads involved in demand response.
9. An apparatus for optimally controlling motor-like loads involved in demand response, the apparatus comprising:
the determining module is used for determining the adjustable potential of the motor type load under the current set satisfaction degree based on the required running power of the motor type load;
The first analysis module is used for substituting the adjustable potential of the motor load under the current set satisfaction into a pre-constructed day-ahead scheduling plan model and solving to obtain the day-ahead scheduled running power of the motor load;
the second analysis module is used for substituting the daily planned running power of the motor load into a pre-built daily running optimization model and solving the model to obtain daily rolling optimized power of the motor load;
the adjusting module is used for optimally adjusting the motor load based on the daily rolling optimal power of the motor load;
the determining module is specifically configured to: the adjustable potential of the motor class load under the current set satisfaction is calculated according to the following formula:
P L =nP rate -∑ i (P Lmaxi - P Lmini )PMV i /( PMV Lmax - PMV Lmin )
in the above, P L For the adjustable potential of the motor load under the current set satisfaction degree, P rate For rated operating power of motor type load, P Lmaxi For the maximum required operating power of the ith motor class load, P Lmini For minimum required operating power of the ith motor class load, PMV i For the current set satisfaction of the ith motor class load, PMV Lmax For a preset maximum satisfaction of the motor class load, the PMV Lmax PMV for the ratio of the most satisfactory operating power to the rated power of the user Lmin For a preset minimum satisfaction of the motor class load, the PMV Lmin For the ratio of the lower limit value of the operation power accepted by the user to the rated power, i is E [1, n]N is the total number of motor loads participating in demand response;
the pre-constructed day-ahead dispatch plan model includes: optimizing a first objective function and a first constraint condition configured for controlling the motor class load participating in demand response;
the pre-built intra-day operation optimization model comprises the following steps: optimizing a second objective function and a second constraint condition configured for controlling the motor type load participating in the demand response;
when a load demand response signal is received, solving a pre-built adjustment optimization model to obtain daily rolling optimization power of the motor load;
wherein the pre-built adjustment optimization model comprises: optimizing a third objective function and a third constraint condition configured for controlling the motor type load participating in the demand response;
the mathematical model of the first objective function is as follows:
minF 1 =∑ i m 1 (C 1 +C 2 -C 3 )P worki +m 2 ∑ i PMV i
P day-front =∑ i P worki
in the above, PMV i Satisfaction degree F for current setting of ith motor class load 1 For the first objective function value, m 1 For cost weight, i.e. [1, n ]]N is the total number of motor loads participating in demand response, C 1 For the running cost of the equipment, C 2 For equipment maintenance cost, C 3 Is patch for time-staggered, P worki Working power m for day-ahead running of ith motor class load 2 To weight satisfaction, P day-front Planned operating power for the day before of the motor class load;
the mathematical model of the second objective function is as follows:
minF 2 =∑ t (P day-front-t - P day-in-t )+f
in the above, F 2 For the second objective function value, P day-front-t Planned daily operating power for a motor-like load at time t, P day-in-t Optimizing power for daily rolling of motor loads at the moment t, wherein f is additional power required by new energy self-control regulation;
the mathematical model of the third objective function is as follows:
minF 3 =∣P grid-t -P day-in-t ∣
in the above, F 3 For the third objective function value, P grid-t And scheduling power for the daily requirement of the power grid of the motor type load at the moment t.
10. The apparatus of claim 9, wherein when the motor type load is a water pump type load, a minimum demand of the motor type loadThe running power is as follows: ρgQaV min And/1000, wherein the maximum required running power of the motor load is as follows: ρgQaV max /1000；
When the motor load is a ventilation load, the minimum required running power of the motor load is as follows: k (k) 1 (V min ) 3 The maximum required running power of the motor load is as follows: k (k) 1 (V max ) 3 ；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: TV set min And r, the maximum required running power of the motor load is as follows: TV set max /r；
When the motor load is an elevator load, the minimum required running power of the motor load is as follows: (1-K) GV min / P s The maximum required running power of the motor load is as follows: (1-K) GV max /P s ；
Wherein ρ is the liquid density, g is the gravitational acceleration, Q is the water consumption, a is the lift coefficient, k 1 For ventilator load factor, K is elevator balance factor, G is rated load, P s For mechanically driving the total power, V max For the rotation speed corresponding to the motor load under the maximum satisfaction degree, V min The rotation speed corresponding to the motor load under the minimum satisfaction degree is represented by T, the motor torque is represented by T, and the radius of the motor rotating part is represented by r.
11. The device according to claim 9, wherein the adjustment module is specifically configured to
Determining a motor load start-stop regulation and control behavior coefficient based on the satisfaction degree state quantity, the quantity state quantity and the energy consumption requirement state quantity of the motor load;
constructing a first motor type load sequence according to the sequence from small to large of motor type load start-stop regulation and control action coefficients, and constructing a second motor type load sequence according to the sequence from large to small of motor type load start-stop regulation and control action coefficients;
in the load increasing process, sequentially selecting motor loads from the first motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads;
And in the load reduction process, sequentially selecting motor loads from the second motor load sequence, and adjusting the running power of the motor loads according to the daily rolling optimized power of the motor loads.
12. A computer device, comprising: one or more processors;
the processor is used for storing one or more programs;
when the one or more programs are executed by the one or more processors, an optimized control method of motor class loads participating in demand response as claimed in any one of claims 1 to 8 is achieved.
13. A computer-readable storage medium, on which a computer program is stored, which, when executed, implements the method for optimally controlling a motor class load participating in demand response as claimed in any one of claims 1 to 8.
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