CN112531746B - Virtual power plant operation method based on local autonomous optimization of central air conditioner - Google Patents

Virtual power plant operation method based on local autonomous optimization of central air conditioner Download PDF

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CN112531746B
CN112531746B CN202011104226.8A CN202011104226A CN112531746B CN 112531746 B CN112531746 B CN 112531746B CN 202011104226 A CN202011104226 A CN 202011104226A CN 112531746 B CN112531746 B CN 112531746B
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power generation
central air
generation unit
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CN112531746A (en
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高赐威
马思思
陈涛
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Nanjing Chunning Electric Power Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H02J2310/14—The load or loads being home appliances
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances
    • Y04S20/244—Home appliances the home appliances being or involving heating ventilating and air conditioning [HVAC] units

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  • Power Engineering (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention discloses a virtual power plant operation method based on local autonomous optimization of a central air conditioner, which comprises the following steps: (1) Constructing a system virtual energy storage model according to the thermal inertia of the central air-conditioning system; (2) Evaluating the adjustment potential of the central air conditioning system based on the virtual energy storage characteristic; (3) Constructing a virtual power generation unit of the central air-conditioning system based on an internal resource coordination control mechanism; (4) Carrying out normalized calling on the virtual power generation unit by adopting a local autonomous optimization method; (5) And forming a virtual power plant participating power grid peak shaving operation method based on the standardized calling of the virtual power generation unit. The method provided by the invention integrates the central air-conditioning clusters into the virtual power plant, fully utilizes the thermal inertia of the central air-conditioning system to provide adjustable capacity for the power grid, realizes the standardized modeling of the complex central air-conditioning system, and relieves the operating pressure during the peak load period of the power grid by fully exploiting the adjustment potential of resources on the demand side.

Description

Virtual power plant operation method based on local autonomous optimization of central air conditioner
Technical Field
The invention relates to a virtual power plant operation method based on local autonomous optimization of a central air conditioner, and belongs to a power system and an automation technology thereof.
Background
In recent years, with the development of social economy, the power load of China is continuously increased, the peak-valley difference is continuously increased, and the peak regulation difficulty of a power system is highlighted. In the high-temperature period in summer of 2019, the power load of the power grid in east China is as high as 2.9791 hundred million kilowatts, the power load is increased by 5.95 percent on year-on-year basis, and the creation history is new. The urban power load is concentrated in China, and air-conditioning power consumers are numerous and large in size, so that the urban power grid summer peak load is caused mainly. Compared with a split air conditioner, the load electric power demand of a central air conditioner comprising a plurality of subsystems such as a refrigeration system, a water system and a wind system is larger. In the Huangpu district of the sea city as an example, the peak load of a large commercial building mainly based on the load of a central air conditioner is near 500MW, and the annual power consumption is about 13 hundred million kilowatt hours, which accounts for more than 65% of the total energy consumption of the whole district.
Although the central air-conditioning load consumes significant energy, the building to which the central air-conditioning load belongs can convert electric energy into heat energy for storage within a specific time, and load transfer or reduction can be realized through reasonable regulation, so that the central air-conditioning load has power regulation potential within a comfort range which can be borne by a user. The highest power reduction of a flexible load of a certain commercial building including a central air conditioner in Nanjing West road in Huangpu district in Shanghai can reach 1069kW in peak time in summer, and the regulation potential is very huge, so that the flexible load becomes an effective substitute resource for the adjustable capacity at the side of a power grid.
The virtual power plant can aggregate distributed user side resources through an advanced coordination control technology, an intelligent metering technology and an information communication technology, external virtual power generation is achieved through load demand response, the whole system is used as an intermediate unit to participate in power grid dispatching and market trading, interconnection and interaction of a plurality of user power equipment can be achieved, and an effective way is provided for large-scale calling of user side resources with small size and scattered positions.
Therefore, the virtual power plant operation method based on the local autonomous optimization of the central air conditioner is provided, the central air conditioner cluster is aggregated into a virtual power plant, the virtual power plant participating in power grid peak shaving is taken as an application scene, the thermal inertia of the central air conditioner system is fully utilized to provide adjustable capacity for a power grid, the whole system is packaged into a virtual power generation unit which provides continuous and stable output through comprehensive coordination control of a plurality of power devices in the system, not only is standardized modeling of the complex central air conditioner system realized, but also the operation pressure during the peak load period of the power grid is relieved by fully excavating the adjustment potential of resources on the demand side.
Disclosure of Invention
The purpose of the invention is as follows: in order to excavate the adjustment potential of user side resources, utilize load demand response to carry out power grid peak shaving, relieve power grid scheduling operation pressure and save power grid investment and construction cost, the invention provides a virtual power plant operation method based on central air conditioner local autonomous optimization, a central air conditioner cluster is aggregated into a virtual power plant, the whole system is packaged into a virtual power generation unit which provides continuous and stable output through the comprehensive coordination control of power equipment in a central air conditioner system, and the operation method of the virtual power plant participating in power grid peak shaving is formed based on the standardized calling of the virtual power generation unit.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a virtual power plant operation method based on central air-conditioning local autonomous optimization comprises the following steps:
(1) Constructing a system virtual energy storage model according to the thermal inertia of the central air-conditioning system;
(2) Evaluating the adjustment potential of the central air conditioning system based on the virtual energy storage characteristic;
(3) Constructing a virtual power generation unit of a central air-conditioning system based on an internal resource coordination control mechanism;
(4) Carrying out standardized calling on the virtual power generation unit by adopting a local autonomous optimization method;
(5) And forming a virtual power plant participating power grid peak shaving operation method based on the standardized calling of the virtual power generation unit.
Specifically, in the step (1), a system virtual energy storage model is constructed according to the thermal inertia of the central air conditioning system, and the method specifically comprises the following steps:
(1-1) virtual energy storage charging and discharging power of the system:
Figure BDA0002726401240000021
wherein: n is a power equipment number; n is the total number of the electrical equipment in the central air-conditioning system; p t ES Virtual energy storage power for the central air-conditioning system; p n,t Real-time power for electrical equipment；P n,0 And (4) reference power for the electric equipment.
(1-2) virtual stored energy variation:
Figure BDA0002726401240000022
Figure BDA0002726401240000023
Figure BDA0002726401240000024
wherein:
Figure BDA0002726401240000025
change of the virtual stored energy in the t period;
Figure BDA0002726401240000026
energy is stored virtually for the current system.
(1-3) virtual energy storage state of charge:
Figure BDA0002726401240000027
Figure BDA0002726401240000028
wherein: VSOC t Storing the current state of charge for the virtual energy storage; e ES,max Is the virtual maximum energy storage.
Specifically, in the step (2), the evaluation of the adjustment potential of the central air conditioning system is performed based on the virtual energy storage characteristics, specifically:
(2-1) minimizing an electric power value of each device for a prescribed period in the future.
(a) End fan power minimization
Figure BDA0002726401240000029
S＝{fa,ra,ma,rla} (8)
Constraint conditions are as follows:
(1) and (5) restraining the air quantity of the tail end fan.
Figure BDA0002726401240000031
Figure BDA0002726401240000032
Figure BDA0002726401240000033
(2) And (5) cold quantity utilization restraint of a user area. The minimum cooling capacity calculation formula actually taken by the fan coil in each user area is as follows.
Figure BDA0002726401240000034
Figure BDA0002726401240000035
Wherein: outdoor temperature T t out Considered a constant in a short time; indoor temperature
Figure BDA0002726401240000036
Taking the highest temperature T tolerable by users in the area l a,in,max (ii) a Because the chilled water system has thermal inertia and timely ductility, the water temperature at the inlet of the fan coil
Figure BDA0002726401240000037
The change speed is far less than the change speed of the fan power, so that the water at the inlet at the current moment is takenTemperature of
Figure BDA0002726401240000038
(b) Minimization of refrigerating machine power
By adjusting the outlet water temperature of the chilled water to the upper limit T of the outlet water temperature allowed by the refrigerator w,ch,out* The minimization of the power of the refrigerating machine is realized, as shown in the following formula.
Figure BDA0002726401240000039
Wherein: t is w,ch,in* The temperature of the outlet water of the chilled water is adjusted to T w,ch,out* Then, the system reaches the inlet water temperature of the refrigerator at the new steady state.
In the actual regulation and control process, the chilled water outlet water temperature set value T can be set in consideration of the cold quantity requirement of a user area w,sp Up-regulated to [ T w,ch,out0 ,T w,ch,out* ]A value within the range.
(2-2) the system can continuously provide the maximum adjustable capacity as the sustainable time of the adjustment potential, and the specific calculation process is as follows.
(a) Indoor temperature from T l a,in0 Up to T l a,in,max In the process, the total cold energy released by the air-conditioning room is the maximum energy storage of the virtual battery in the current state of the room, and the specific expression is as follows:
Figure BDA00027264012400000310
(b) In analogy to lithium batteries, reducing the amount of cold input to a user area is equivalent to discharging a virtual battery of the user area, wherein the reduced amount of cold input is equivalent to the discharge power of the virtual battery, as shown in the following equation.
Figure BDA00027264012400000311
In the formula：T l a,in' To reach the room temperature at the new steady state.
(c) Due to T l a,in' >T l a,in0 Deviates from the actual temperature set value of the user, so the user can only set the room temperature to be T l a ,in' The user's tolerable duration is considered as the maximum discharge duration τ' of the virtual battery. If the cold input of the user area starts to be reduced at the moment t, the cold energy stored in the area at the moment t + tau
Figure BDA00027264012400000312
When the release is completed, the energy conservation expression in the process is shown as the following formula.
Figure BDA00027264012400000313
From this, the sustainable time expression can be derived as follows:
Figure BDA0002726401240000041
specifically, in the step (3), the virtual power generation unit of the central air conditioning system is constructed based on an internal resource coordination control mechanism, which specifically includes:
(3-1) according to the power regulation characteristic of the central air-conditioning system, establishing a system internal resource coordination control mechanism combining global control and local control, regulating the temperature of a cold source and the air quantity of a tail end fan, regulating and controlling the power of the central air-conditioning system, and prolonging the sustainable time of the maximum adjustable capacity of the system.
And (3-2) calculating the maximum adjustable electric quantity which can be provided by the adjustable electric quantity gap and the tail end fan cluster of the refrigerator.
(a) The maximum adjustable electric quantity of the fan is measured by the scene with the minimum requirement on the cold quantity of each user area, and the power of the tail end fan is
Figure BDA0002726401240000042
The sustainable time of a scene is tau * And calculating the total maximum adjustable electric quantity value of the end fans of all user areas according to the following formula:
Figure BDA0002726401240000043
(b) Calculating the maximum adjustable electric quantity of the refrigerator as follows:
Figure BDA0002726401240000044
wherein: p is 0 F (t) is a dynamic power expression of the refrigerator.
(c) With P 0 -P 1 As the maximum reducible power of the refrigerator, the reducible power cut in the virtual power generation unit is calculated as follows:
ΔE＝(P 0 -P 1 )T * -E e,ch (21)
wherein: p is 1 The minimum value of the power of the refrigerator corresponding to the maximum value of the outlet water temperature of the refrigerator is obtained.
(3-3) according to Delta E and E e,f The virtual power generation unit of the central air conditioner is constructed under two conditions.
(a) When E is e,f When the power is more than or equal to delta E, calculating the maximum reducible power delta P max The following were used:
Figure BDA0002726401240000045
(b) When E is e,f <When delta E is reached, in order to fully compensate the electric quantity reducible gap of the refrigerator by the electric quantity reducible of the tail end fan, the water outlet temperature set value of the chilled water of the refrigerator is required to be
Figure BDA0002726401240000046
Up to [ T ] w,ch,out0 ,T w,ch,out* ]A certain value within the range. To maximizeReducible power delta P of central air conditioning system max As a target, pair
Figure BDA0002726401240000047
The values are optimized.
Figure BDA0002726401240000048
Constraint conditions are as follows:
Figure BDA0002726401240000049
ΔE＝ΔP max T * -E e,ch (25)
E e,f ≥ΔE (26)
(3-4) the sustainable time period generated by the control center of the central air-conditioning system is T * The virtual power generation unit of (2) reduces the maximum power of the system by delta P max (i.e., virtual generated power P) VGU ) And reporting to a virtual power plant control center, and performing unified regulation and control management by the virtual power plant control center.
Specifically, in the step (4), a local autonomous optimization method is adopted to perform normalized calling on the virtual power generation unit, and specifically, the method includes:
(4-1) Global one-time optimization
(a) Cold source temperature set value optimization
The refrigerator agent takes the difference between the maximum reduction amount of the refrigerator power and the virtual power generation unit as a target, and carries out optimization decision on the outlet water temperature set value of the chilled water, and the specific process is as follows.
Figure BDA0002726401240000051
ΔP ch,max ＝P ch0 -P ch,min (28)
Wherein: t is w,sp The temperature set value of the cold source is set; deltaP ch,max Cutting down the maximum power of the refrigerator in the whole virtual power generation period; p VGU Generating power for the virtual power generation unit; p ch0 The initial power of the refrigerator; p ch,min Is the minimum power of the chiller for the entire virtual power generation period.
Constraint conditions are as follows:
the temperature setting value of the cold source is within the range of the outlet water temperature allowed by the refrigerator.
T w,sp,min ≤T w,sp ≤T w,sp,max (29)
In the formula: t is a unit of w,sp,min And T w,sp,max The minimum value and the maximum value of the set value of the cold source temperature are respectively.
(b) Regional virtual energy storage calling
The central air-conditioning system control center regards each user area as an independent and uniformly packaged virtual battery, reduces calling of area virtual stored energy as far as possible on the basis of making up a gap of the overall virtual generated power of the system, namely maximizes the area virtual charge state, and optimizes the virtual generated power of each area, and the specific process is as follows.
Figure BDA0002726401240000052
Wherein:
Figure BDA0002726401240000053
generating power for the regional virtual battery; delta P t VGU A virtual power generation unit power gap; VSOC t,l A regional virtual battery state of charge; omega P And ω S The weight coefficients of the two optimization terms are respectively.
Constraint conditions are as follows:
1) And (5) energy balance constraint. The virtual generator unit power notch is the difference between the overall virtual generator power requirement and the refrigerator power reduction.
ΔP t VGU ＝P VGU -N ch ΔP t ch (31)
2) And (5) virtual generated power constraint. The area virtual power generation power is a positive value and does not exceed the maximum discharge power of the area virtual energy storage.
Figure BDA0002726401240000054
In the formula: p l VES,max The maximum discharge power of the regional virtual energy storage.
3) Virtual stored energy constraints. The regional virtual energy storage needs to satisfy the following state transition process.
Figure BDA0002726401240000061
Figure BDA0002726401240000062
Wherein:
Figure BDA0002726401240000063
the maximum energy storage capacity of the regional virtual energy storage;
Figure BDA0002726401240000064
the current stored energy is the virtual stored energy of the region; Δ t is a unit period duration.
4) Virtual state of charge constraints. The virtual state of charge is the ratio of the current virtual stored energy to the total stored energy, and is required to be within a specified range.
Figure BDA0002726401240000065
0≤VSOC t,l 1 (36) wherein: VSOC t,l And a virtual state of charge for regional virtual energy storage.
(4-2) local quadratic optimization
After receiving the virtual power generation power requirement, each regional agent performs virtual power generation power sharing in each regional fan through local autonomous optimization on the premise of maximizing the comfort of users to generate an optimal air volume control signal, and the specific process is as follows.
Figure BDA0002726401240000066
Figure BDA0002726401240000067
Wherein: beta is a t,l For the comfort level of regional users, the specific calculation mode is shown in section 2.2.6;
Figure BDA0002726401240000068
a decision variable vector in a local autonomous optimization problem;
Figure BDA0002726401240000069
and
Figure BDA00027264012400000610
the air supply amount, the fresh air amount, the return air amount and the exhaust air amount are respectively expressed.
Constraint conditions are as follows:
1) And (4) power balance constraint. The regional virtual power generation power is the difference between the initial value and the current value of the regional fan power.
Figure BDA00027264012400000611
2) And (5) decision variable constraint. The decision variables have to be between their upper and lower limits.
Figure BDA00027264012400000612
Specifically, in the step (5), the method for forming the virtual power plant to participate in the peak shaving operation of the power grid based on the standardized calling of the virtual power generation unit specifically includes:
(5-1) after the virtual power plant control center receives a peak clipping instruction issued by a power grid dispatching department, the virtual power plant control center carries out peak clipping according to the total peak clipping duration T dur Determining the virtual power generation time length T of each virtual power generation unit * Dividing the calling process of the virtual power generation unit into R = T dur /T * Wheel, virtual power generation time length T * Sending to each central air-conditioning system control center;
(5-2) the virtual power plant control center determines a peak clipping capacity margin gamma belongs to [0,1] according to historical response statistics conditions of each virtual power generation unit;
(5-3) the virtual power plant control center records the number of calling wheels of the virtual power generation unit as r =1;
(5-4) each central air-conditioning system control center generates electricity according to the virtual electricity generation time length T * According to the requirements, local autonomous optimization is carried out, a virtual power generation unit is constructed, and a parameter set { P } of the virtual power generation unit is obtained VGU ,T idle Reporting to a virtual power plant control center, wherein T idle The idle time of the virtual power generation unit;
(5-5) the virtual power plant control center enables the virtual power generation units to be in accordance with T idle The parameters are sorted from big to small, T idle The parameters are the same according to delta P max Sequencing from big to small to form a virtual power generation unit sequence;
(5-6) the virtual power plant control center selects the first N virtual power generation units from the sequence to participate in power grid peak clipping, wherein N meets the following conditions:
Figure BDA0002726401240000071
δ k ＝1,k＝1,2,…,N-1 (42)
Figure BDA0002726401240000072
(5-7) the virtual power plant control center issues a calling coefficient delta to each virtual power generation unit in the sequence k And a throw-in time t k If the virtual generation unit is included in the call sequence of the current round, δ k Taking the above formula to calculate the result, t k Get
Figure BDA0002726401240000073
(indicating that the kth virtual power generation unit is put into the process of the r round calling); on the contrary, delta k And t k All are taken as 0;
(5-8) judging the size relation between R and R by the virtual power plant control center, and if R is less than R, enabling R = R +1, and returning to (5-4); otherwise, turning to (5-9);
and (5-9) the virtual power plant control center feeds back to a power grid dispatching department, issues peak clipping confirmation instructions to each virtual power generation unit, and carries out peak clipping operation when a peak clipping period comes.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention;
FIG. 2 is a schematic diagram of a virtual energy storage model of a central air conditioner according to the present invention;
FIG. 3 is a schematic view of a virtual power generation unit according to the present invention;
FIG. 4 is a diagram of a multi-level operation control architecture for a virtual power plant participating in peak shaving of a power grid in accordance with the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
Fig. 1 shows a virtual power plant operation method based on local autonomous optimization of a central air conditioner, and the whole implementation process is specifically described below.
The method comprises the following steps: and constructing a system virtual energy storage model according to the thermal inertia of the central air-conditioning system.
(1-1) virtual energy storage charging and discharging power of the system:
Figure BDA0002726401240000074
wherein: n is the number of the power equipment; n is the total number of the electrical equipment in the central air-conditioning system; p t ES Virtual energy storage power for the central air-conditioning system; p is n,t Real-time power is provided for the power equipment; p n,0 The power is the reference power of the electrical equipment.
(1-2) virtual stored energy variation:
Figure BDA0002726401240000075
Figure BDA0002726401240000076
Figure BDA0002726401240000081
wherein:
Figure BDA0002726401240000082
change of the virtual stored energy in the t period;
Figure BDA0002726401240000083
energy is stored virtually for the current system.
(1-3) virtual energy storage state of charge:
Figure BDA0002726401240000084
Figure BDA0002726401240000085
wherein: VSOC t Storing the current state of charge for the virtual energy storage; e ES,max Is the virtual maximum stored energy.
Step two: and evaluating the adjustment potential of the central air conditioning system based on the virtual energy storage characteristics.
(2-1) minimizing an electric power value of each device for a prescribed period in the future.
(a) End fan power minimization
Figure BDA0002726401240000086
S＝{fa,ra,ma,rla} (8)
Constraint conditions are as follows:
(1) and (5) limiting the air quantity of the tail end fan.
Figure BDA0002726401240000087
Figure BDA0002726401240000088
Figure BDA0002726401240000089
(2) And (5) cold quantity utilization restraint of a user area. The minimum cooling capacity actually taken by the fan coil of each user area is calculated according to the following formula.
Figure BDA00027264012400000810
Figure BDA00027264012400000811
Wherein: outdoor temperature T t out Considered a constant in a short time; indoor temperature
Figure BDA00027264012400000812
Taking the highest temperature T tolerable by users in the area l a,in,max (ii) a Because the chilled water system has thermal inertia and timely ductility, the water temperature at the inlet of the fan coil
Figure BDA00027264012400000813
The change speed is far less than the change speed of the fan power, so that the water temperature at the inlet at the current moment is taken
Figure BDA00027264012400000814
(b) Minimization of refrigerating machine power
By adjusting the outlet water temperature of the chilled water to the upper limit T of the outlet water temperature allowed by the refrigerator w,ch,out* The minimization of the power of the refrigerating machine is realized, as shown in the following formula.
Figure BDA00027264012400000815
Wherein: t is a unit of w,ch,in* The temperature of the outlet water of the chilled water is adjusted to T w,ch,out* Then, the system reaches the inlet water temperature of the refrigerator at the new steady state.
In the actual regulation and control process, the chilled water outlet water temperature set value T can be set in consideration of the cold quantity requirement of a user area w,sp Up-regulated to [ T w,ch,out0 ,T w,ch,out* ]A value within the range.
(2-2) the system can continuously provide the maximum adjustable capacity as the sustainable time of the adjustment potential, and the specific calculation process is as follows.
(a) Indoor temperature from T l a,in0 Up to T l a,in,max In the process, the total cold energy released by the air-conditioning room is the maximum energy storage of the virtual battery in the current state of the room, and the specific expression is as follows:
Figure BDA0002726401240000091
(b) In analogy to lithium batteries, reducing the amount of cold input to a user area is equivalent to discharging a virtual battery of the user area, wherein the reduced amount of cold input is equivalent to the discharge power of the virtual battery, as shown in the following equation.
Figure BDA0002726401240000092
In the formula:T l a,in' to reach the room temperature at the new steady state.
(c) Due to T l a,in' >T l a,in0 Deviates from the actual temperature set value of the user, so the user can only set the room temperature to be T l a ,in' The user's tolerable duration is considered to be the maximum discharge duration τ' of the virtual battery. If the cold input of the user area starts to be reduced at the time t, the cold energy stored in the area at the time t + tau' is reduced
Figure BDA0002726401240000093
When the release is completed, the energy conservation expression in the process is shown as the following formula.
Figure BDA0002726401240000094
From this, the sustainable time expression can be derived as follows:
Figure BDA0002726401240000095
step three: and constructing a virtual power generation unit of the central air-conditioning system based on an internal resource coordination control mechanism.
(3-1) according to the power regulation characteristic of the central air-conditioning system, establishing a system internal resource coordination control mechanism combining global control and local control, regulating the temperature of a cold source and the air quantity of a tail end fan, regulating and controlling the power of the central air-conditioning system, and prolonging the sustainable time of the maximum adjustable capacity of the system.
And (3-2) calculating the adjustable electric quantity gap of the refrigerator and the maximum adjustable electric quantity provided by the tail end fan cluster.
(a) The maximum adjustable electric quantity of the fan is measured by the scene with the minimum requirement on the cold quantity of each user area, and the power of the tail end fan is
Figure BDA0002726401240000096
Sustainable time of sceneIs between tau * Therefore, the maximum adjustable electric quantity total value of the fans at the tail ends of all user areas is calculated as follows:
Figure BDA0002726401240000097
(b) Calculating the maximum adjustable electric quantity of the refrigerator as follows:
Figure BDA0002726401240000098
wherein: p 0 F (t) is a dynamic power expression of the refrigerator.
(c) With P 0 -P 1 As the maximum reducible power of the refrigerator, the reducible power cut in the virtual power generation unit is calculated as follows:
ΔE＝(P 0 -P 1 )T * -E e,ch (21)
wherein: p is 1 The minimum value of the power of the refrigerator corresponding to the maximum value of the outlet water temperature of the refrigerator is obtained.
(3-3) according to Delta E and E e,f The virtual power generation unit of the central air conditioner is constructed under two conditions.
(a) When E is e,f When the power is more than or equal to delta E, calculating the maximum reducible power delta P max The following were used:
Figure BDA0002726401240000101
(b) When E is e,f <When delta E is reached, in order to fully compensate the electric quantity reducible gap of the refrigerator by the electric quantity reducible of the tail end fan, the water outlet temperature set value of the chilled water of the refrigerator is required to be
Figure BDA0002726401240000102
Up-regulated to [ T w,ch,out0 ,T w,ch,out* ]A certain value within the range. To maximize the reducible power Δ P of the central air conditioning system max As a target, to
Figure BDA0002726401240000103
The values are optimized.
Figure BDA0002726401240000104
Constraint conditions are as follows:
Figure BDA0002726401240000105
ΔE＝ΔP max T * -E e,ch (25)
E e,f ≥ΔE (26)
(3-4) the sustainable time length generated by the central air-conditioning system control center is T * The virtual power generation unit reduces the maximum power of the system by delta P max (i.e., virtual generated power P) VGU ) And reporting to a virtual power plant control center, and performing unified regulation and control management by the virtual power plant control center.
Step four: and carrying out normalized calling on the virtual power generation unit by adopting a local autonomous optimization method.
(4-1) Global one-time optimization
(a) Cold source temperature set value optimization
The refrigerator agent takes the difference between the maximum reduction amount of the refrigerator power and the virtual power generation unit as a target, and carries out optimization decision on the outlet water temperature set value of the chilled water, and the specific process is as follows.
Figure BDA0002726401240000106
ΔP ch,max ＝P ch0 -P ch,min (28)
Wherein: t is w,sp The temperature set value of the cold source is set; delta P ch,max For maximum power of refrigerator in whole virtual power generation periodReducing; p is VGU Generating power for the virtual power generation unit; p ch0 The initial power of the refrigerator; p ch,min Is the minimum power of the refrigerator in the entire virtual power generation period.
Constraint conditions are as follows:
the temperature setting value of the cold source is within the range of the outlet water temperature allowed by the refrigerator.
T w,sp,min ≤T w,sp ≤T w,sp,max (29)
In the formula: t is a unit of w,sp,min And T w,sp,max The minimum value and the maximum value of the set value of the cold source temperature are respectively.
(b) Regional virtual energy storage calling
The central air-conditioning system control center regards each user area as an independent and uniformly packaged virtual battery, reduces calling of area virtual stored energy as far as possible on the basis of making up a gap of the overall virtual generated power of the system, namely maximizes the area virtual charge state, and optimizes the virtual generated power of each area, and the specific process is as follows.
Figure BDA0002726401240000111
Wherein:
Figure BDA0002726401240000112
generating power for the regional virtual battery; delta P t VGU A virtual power generation unit power gap; VSOC t,l A regional virtual battery state of charge; omega P And ω S The weight coefficients of the two optimization terms are respectively.
Constraint conditions are as follows:
1) And (4) energy balance constraint. The virtual generator unit power notch is the difference between the overall virtual generator power demand and the refrigerator power reduction.
ΔP t VGU ＝P VGU -N ch ΔP t ch (31)
2) And (5) virtual generated power constraint. The area virtual power generation power is a positive value and does not exceed the maximum discharge power of the area virtual energy storage.
Figure BDA0002726401240000113
In the formula: p l VES,max The maximum discharge power of the regional virtual energy storage.
3) Virtual stored energy constraints. The regional virtual energy storage needs to satisfy the following state transition process.
Figure BDA0002726401240000114
Figure BDA0002726401240000115
Wherein:
Figure BDA0002726401240000116
the maximum energy storage capacity of the regional virtual energy storage;
Figure BDA0002726401240000117
the current stored energy is the virtual stored energy of the region; Δ t is a unit period duration.
4) Virtual state of charge constraints. The virtual state of charge is the ratio of the current virtual stored energy to the total stored energy, and is required to be within a specified range.
Figure BDA0002726401240000118
0≤VSOC t,l 1 (36) wherein: VSOC t,l And the virtual state of charge of the regional virtual energy storage.
(4-2) local quadratic optimization
After each regional agent receives the virtual power generation power requirement, the regional agents perform virtual power generation power allocation in each regional fan on the premise of maximizing user comfort degree through local autonomous optimization to generate an optimal air volume control signal, and the specific process is as follows.
Figure BDA0002726401240000119
Figure BDA00027264012400001110
Wherein: beta is a t,l For the comfort level of regional users, the specific calculation mode is shown in section 2.2.6;
Figure BDA00027264012400001111
a decision variable vector in a local autonomous optimization problem;
Figure BDA00027264012400001112
and
Figure BDA00027264012400001113
the air supply amount, the fresh air amount, the return air amount and the exhaust air amount are respectively expressed.
Constraint conditions are as follows:
1) And (4) power balance constraint. The regional virtual power generation power is the difference between the initial value and the current value of the regional fan power.
Figure BDA00027264012400001114
2) And (5) deciding variable constraints. The decision variables have to be between their upper and lower limits.
Figure BDA00027264012400001115
Step five: and forming a virtual power plant participating power grid peak shaving operation method based on the standardized calling of the virtual power generation unit.
(5-1) the virtual power plant control center receives a peak clipping instruction issued by a power grid dispatching departmentThen, according to the total peak clipping time length T dur Determining the virtual power generation time length T of each virtual power generation unit * Dividing the calling process of the virtual power generation unit into R = T dur /T * Wheel to virtual generation time period T * Sending the data to each central air-conditioning system control center;
(5-2) the virtual power plant control center determines a peak clipping capacity margin gamma belongs to [0,1] according to historical response statistics conditions of each virtual power generation unit;
(5-3) the virtual power plant control center records the number of calling turns of the virtual power generation unit as r =1;
(5-4) each central air-conditioning system control center generates electricity according to the virtual electricity generation time length T * According to the requirements, local autonomous optimization is carried out, a virtual power generation unit is constructed, and a parameter set { P } of the virtual power generation unit is obtained VGU ,T idle Reporting to a virtual power plant control center, wherein T idle The idle time of the virtual power generation unit;
(5-5) the virtual power plant control center enables the virtual power generation units to be in accordance with T idle The parameters are sorted from big to small, T idle In the case of the same parameters, according to Δ P max Sequencing from big to small to form a virtual power generation unit sequence;
(5-6) the virtual power plant control center selects the first N virtual power generation units from the sequence to participate in power grid peak clipping, wherein N meets the following conditions:
Figure BDA0002726401240000121
δ k ＝1,k＝1,2,…,N-1 (42)
Figure BDA0002726401240000122
(5-7) the virtual power plant control center issues a calling coefficient delta to each virtual power generation unit in the sequence k And a throw-in time t k If the virtual power generation unit is included in the current call sequence, δ k Taking the calculation result of the above formula, the method,t k get
Figure BDA0002726401240000123
(indicating that the kth virtual power generation unit is put into the process of the r round calling); on the contrary, delta k And t k All are taken as 0;
(5-8) judging the size relation between R and R by the virtual power plant control center, and if R is less than R, enabling R = R +1, and returning to (5-4); otherwise, turning to (5-9);
and (5-9) the virtual power plant control center feeds back to a power grid dispatching department, issues peak clipping confirmation instructions to each virtual power generation unit, and carries out peak clipping operation when a peak clipping period comes.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (5)

1. A virtual power plant operation method based on local autonomous optimization of a central air conditioner is characterized by comprising the following steps: the method comprises the following steps:
(1) Constructing a system virtual energy storage model according to the thermal inertia of the central air-conditioning system;
(2) Evaluating the adjustment potential of the central air conditioning system based on the virtual energy storage characteristic;
(3) Constructing a virtual power generation unit of a central air-conditioning system based on an internal resource coordination control mechanism;
(4) Carrying out normalized calling on the virtual power generation unit by adopting a local autonomous optimization method;
(5) A virtual power plant participating power grid peak shaving operation method is formed based on the standardized calling of the virtual power generation unit;
in the step (2), the adjustment potential evaluation of the central air conditioning system is performed based on the virtual energy storage characteristics, and specifically comprises the following steps:
(2-1) minimizing an electric power value of each device for a prescribed period in the future;
(a) End fan power minimization
Figure FDA0003909285340000011
S＝{fa,ra,ma,rla} (8)
Constraint conditions are as follows:
(1) and (3) limiting the air quantity of the tail end fan:
Figure FDA0003909285340000012
Figure FDA0003909285340000013
Figure FDA0003909285340000014
(2) the user area cold quantity taking constraint is that the minimum cold quantity calculation formula actually taken by the fan coil of each user area is as follows:
Figure FDA0003909285340000015
Figure FDA0003909285340000016
wherein: outdoor temperature T t out Considered a constant in a short time; indoor temperature
Figure FDA0003909285340000017
Taking the highest temperature T tolerable by the users in the area l a,in,max (ii) a Because the chilled water system has thermal inertia and timely ductility, the water temperature at the inlet of the fan coil
Figure FDA0003909285340000018
The change speed is far less than the change speed of the fan power, so that the water temperature at the inlet at the current moment is taken
Figure FDA0003909285340000019
(b) Minimization of refrigerating machine power
By adjusting the outlet water temperature of the chilled water to the upper limit T of the outlet water temperature allowed by the refrigerator w,ch,out* The minimization of the power of the refrigerating machine is realized as shown in the following formula:
Figure FDA00039092853400000110
wherein: t is w,ch,in* The temperature of the outlet water of the chilled water is adjusted to T w,ch,out* Then, the system reaches the inlet water temperature of the refrigerator when in a new stable state;
in the actual regulation and control process, the chilled water outlet water temperature set value T can be set in consideration of the cold quantity requirement of a user area w,sp Up-regulated to [ T w,ch,out0 ,T w,ch,out* ]A value within a range;
(2-2) the system can continuously provide the maximum adjustable capacity as the sustainable time of the adjustment potential, and the specific calculation process is as follows:
(a) Indoor temperature from T l a,in0 Up to T l a,in,max In the process, the total cold energy released by the air-conditioning room is the maximum energy storage of the virtual battery in the current state of the room, and the specific expression is as follows:
Figure FDA0003909285340000021
(b) Similar to a lithium battery, reducing the amount of cold input into the user area is equivalent to discharging a virtual battery in the user area, wherein the reduced amount of cold input is equivalent to the discharge power of the virtual battery, as shown in the following formula:
Figure FDA0003909285340000022
in the formula: t is a unit of l a,in' To reach the indoor temperature at the new steady state;
(c) Due to T l a,in' >T l a,in0 Deviates from the actual temperature set value of the user, so the user can only set the room temperature to be T l a,in' The state of (1) is endured for a certain time, the endurable time of the user is considered as the maximum discharge time tau 'of the virtual battery, if the cold input of the user area starts to be reduced at the moment of t, the cold energy stored in the area at the moment of t + tau' is used for cooling the virtual battery
Figure FDA0003909285340000023
When the release is completed, the energy conservation expression in the process is shown as the following formula:
Figure FDA0003909285340000024
the sustainable time expression is derived as follows:
Figure FDA0003909285340000025
2. the virtual power plant operation method based on the local autonomous optimization of the central air conditioner according to claim 1, characterized in that: in the step (1), a system virtual energy storage model is constructed according to the thermal inertia of the central air-conditioning system, and the method specifically comprises the following steps:
(1-1) virtual energy storage charging and discharging power of the system:
Figure FDA0003909285340000026
wherein: n is the number of the power equipment; n is the electric power in the central air-conditioning systemTotal number of devices; p is t ES Virtual energy storage power is provided for the central air-conditioning system; p is n,t Real-time power is provided for the power equipment; p is n,0 Reference power for the electrical equipment;
(1-2) virtual stored energy variation:
Figure FDA0003909285340000027
Figure FDA0003909285340000028
Figure FDA0003909285340000029
wherein:
Figure FDA00039092853400000212
change of the virtual stored energy in the t period; e t ES Virtually storing energy for the current system;
(1-3) virtual energy storage state of charge:
Figure FDA00039092853400000210
Figure FDA00039092853400000211
wherein: VSOC t Storing the current state of charge for the virtual energy storage; e ES,max Is the virtual maximum energy storage.
3. The virtual power plant operation method based on the local autonomous optimization of the central air conditioner according to claim 1, characterized in that: in the step (3), the virtual power generation unit of the central air-conditioning system is constructed based on an internal resource coordination control mechanism, which specifically comprises the following steps:
(3-1) establishing a system internal resource coordination control mechanism combining global control and local control according to the power regulation characteristic of the central air-conditioning system, regulating the temperature of a cold source and the air quantity of a tail end fan, regulating and controlling the power of the central air-conditioning system, and prolonging the sustainable time of the maximum adjustable capacity of the system;
(3-2) calculating the adjustable electric quantity gap of the refrigerator and the maximum adjustable electric quantity which can be provided by the tail end fan cluster;
(a) The maximum adjustable electric quantity of the fan is measured by the scene with the minimum requirement on the cold quantity of each user area, and the power of the tail end fan is
Figure FDA0003909285340000031
The duration of the scene is tau * And calculating the total maximum adjustable electric quantity value of the end fans of all user areas according to the following formula:
Figure FDA0003909285340000032
(b) Calculating the maximum adjustable electric quantity of the refrigerator as follows:
Figure FDA0003909285340000033
wherein: p is 0 F (t) is a power reference value of the refrigerator, and f (t) is a dynamic power expression of the refrigerator;
(c) With P 0 -P 1 As the maximum reducible power of the refrigerator, the reducible power cut in the virtual power generation unit is calculated as follows:
ΔE＝(P 0 -P 1 )T * -E e,ch (21)
wherein: p 1 The corresponding refrigerator power minimum value when the temperature of the outlet water of the refrigerator is adjusted up to the maximum value;
(3-3) according to Delta E and E e,f The relationship between the size of the first and the second,constructing a virtual power generation unit of a central air conditioner under two conditions;
(a) When E is e,f When the power is more than or equal to delta E, calculating the maximum reducible power delta P max The following:
Figure FDA0003909285340000034
(b) When E is e,f <Delta E, in order to fully compensate the cut-off of the electric quantity of the refrigerator by the electric quantity cut-off of the tail end fan, the outlet water temperature of the chilled water of the refrigerator is set to be
Figure FDA0003909285340000035
Up-regulated to [ T w,ch,out0 ,T w,ch,out* ]A value within the range to maximize the curtailable power Δ P of the central air conditioning system max As a target, to
Figure FDA0003909285340000036
And (4) optimizing the value:
Figure FDA0003909285340000037
constraint conditions are as follows:
Figure FDA0003909285340000038
ΔE＝ΔP max T * -E e,ch (25)
E e,f ≥ΔE (26)
(3-4) the sustainable time length generated by the central air-conditioning system control center is T * The virtual power generation unit of (2) reduces the maximum power of the system by delta P max I.e. virtual generated power P VGU And reporting to a virtual power plant control center, and performing unified regulation and control management by the virtual power plant control center.
4. The virtual power plant operation method based on the local autonomous optimization of the central air-conditioning system according to claim 1, characterized in that: in the step (4), a local autonomous optimization method is adopted to carry out standardized calling on the virtual power generation unit, and the method specifically comprises the following steps:
(4-1) Global Primary optimization
(a) Cold source temperature set value optimization
The refrigerator agent takes the difference between the maximum reduction amount of the refrigerator power and the virtual power generation unit as a target, and carries out optimization decision on the outlet water temperature set value of the chilled water, and the specific process is as follows:
Figure FDA0003909285340000041
ΔP ch,max ＝P ch0 -P ch,min (28)
wherein: t is w,sp Is a cold source temperature set value; delta P ch,max Reducing the maximum power of the refrigerator in the whole virtual power generation period; p VGU Generating power for the virtual power generation unit; p ch0 The initial power of the refrigerator; p is ch,min The minimum power of the refrigerator in the whole virtual power generation period;
constraint conditions are as follows:
the temperature setting value of the cold source is within the range of the outlet water temperature allowed by the refrigerator,
T w,sp,min ≤T w,sp ≤T w,sp,max (29)
in the formula: t is a unit of w,sp,min And T w,sp,max Respectively the minimum value and the maximum value of the cold source temperature set value;
(b) Regional virtual energy storage call
The central air-conditioning system control center regards each user area as an independent and uniformly packaged virtual battery, reduces calling of area virtual stored energy as far as possible on the basis of making up a system overall virtual generated power gap, namely maximizes the area virtual charge state, and optimizes virtual generated power of each area, and the specific process is as follows:
Figure FDA0003909285340000042
wherein:
Figure FDA0003909285340000043
generating power for the regional virtual battery; delta P t VGU A virtual power generation unit power gap; VSOC t,l A regional virtual battery state of charge; omega P And omega S The weight coefficients of the two optimization terms are respectively;
constraint conditions are as follows:
1) And (3) energy balance constraint, wherein a virtual power generation unit power notch is the difference between the overall virtual power generation power requirement and the power reduction amount of the refrigerator:
ΔP t VGU ＝P VGU -N ch ΔP t ch (31)
2) And (3) virtual generated power constraint, wherein the regional virtual generated power is a positive value and does not exceed the maximum discharge power of regional virtual energy storage:
Figure FDA0003909285340000044
in the formula: p l VES,max Maximum discharge power for regional virtual energy storage;
3) The virtual stored energy is restricted, and the regional virtual stored energy needs to satisfy the following state transition process:
Figure FDA0003909285340000045
Figure FDA0003909285340000046
wherein:
Figure FDA0003909285340000051
the maximum energy storage capacity of the regional virtual energy storage;
Figure FDA0003909285340000052
the current stored energy is the virtual stored energy of the region; Δ t is a unit time period duration;
4) And (3) virtual state of charge constraint, wherein the virtual state of charge is the proportion of the current virtual stored energy to the total stored energy and needs to be within a specified range:
Figure FDA0003909285340000053
0≤VSOC t,l ≤1 (36)
wherein: VSOC t，l A virtual state of charge for regional virtual energy storage;
(4-2) local quadratic optimization
After each regional agent receives the virtual power generation power requirement, the regional agents perform virtual power generation power sharing in each fan in the region through local autonomous optimization on the premise of maximizing the comfort degree of a user to generate an optimal air volume control signal, and the specific process is as follows:
Figure FDA0003909285340000054
Figure FDA0003909285340000055
wherein: beta is a t,l For the comfort level of regional users, the specific calculation mode is shown in section 2.2.6;
Figure FDA0003909285340000056
decision-making in optimizing problems for local autonomyA vector of quantities;
Figure FDA0003909285340000057
and
Figure FDA0003909285340000058
respectively representing the air supply quantity, the fresh air quantity, the return air quantity and the exhaust air quantity,
constraint conditions are as follows:
1) And (3) power balance constraint, wherein the regional virtual generated power is the difference between the initial value and the current value of the regional fan power:
Figure FDA0003909285340000059
2) And (4) constraint of decision variables, wherein the decision variables are required to be between upper and lower limits:
Figure FDA00039092853400000510
5. the virtual power plant operation method based on the local autonomous optimization of the central air-conditioning system according to claim 1, characterized in that: in the step (5), the method for participating in the peak shaving operation of the power grid by the virtual power plant is formed based on the standardized calling of the virtual power generation unit, and specifically comprises the following steps:
(5-1) after the virtual power plant control center receives a peak clipping instruction issued by a power grid dispatching department, the virtual power plant control center performs peak clipping according to the total peak clipping duration T dur Determining the virtual power generation time length T of each virtual power generation unit * Dividing the calling process of the virtual power generation unit into R = T dur /T * Wheel to virtual generation time period T * Sending the data to each central air-conditioning system control center;
(5-2) the virtual power plant control center determines the peak clipping capacity margin gamma belongs to [0,1] according to the historical response statistics condition of each virtual power generation unit;
(5-3) the virtual power plant control center records the number of calling turns of the virtual power generation unit as r =1;
(5-4) each central air-conditioning system control center generates electricity according to the virtual electricity generation time length T * According to the requirements, local autonomous optimization is carried out, a virtual power generation unit is constructed, and a parameter set { P } of the virtual power generation unit is obtained VGU ,T idle Reporting to a virtual power plant control center, wherein T idle The idle time of the virtual power generation unit;
(5-5) the virtual power plant control center enables the virtual power generation units to be in accordance with T idle The parameters are sorted from large to small, T idle In the case of the same parameters, according to Δ P max Sequencing from big to small to form a virtual power generation unit sequence;
(5-6) the virtual power plant control center selects the first N virtual power generation units from the sequence to participate in power grid peak clipping, wherein N meets the following conditions:
Figure FDA0003909285340000061
δ k ＝1,k＝1,2,…,N-1 (42)
Figure FDA0003909285340000062
(5-7) the virtual power plant control center issues a calling coefficient delta to each virtual power generation unit in the sequence k And a throw-in time t k If the virtual generation unit is included in the call sequence of the current round, δ k Taking the above formula to calculate the result, t k Get the
Figure FDA0003909285340000063
The kth virtual power generation unit is put into the process of the r-th round of calling; on the contrary, delta k And t k All are taken as 0;
(5-8) judging the size relation between R and R by the virtual power plant control center, and if R is less than R, enabling R = R +1, and returning to (5-4); otherwise, turning to (5-9);
and (5-9) the virtual power plant control center feeds back to a power grid dispatching department, issues peak clipping confirmation instructions to each virtual power generation unit, and performs peak clipping operation when a peak clipping period comes.
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* Cited by examiner, † Cited by third party
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CN115639789A (en) * 2022-09-09 2023-01-24 南方电网科学研究院有限责任公司 Virtual power plant regulation and control method, device, system, equipment and readable storage medium
CN117200176B (en) * 2023-07-26 2024-04-30 国网安徽省电力有限公司芜湖供电公司 Demand side control method and system based on new energy multi-load cooperative control
CN117151398B (en) * 2023-09-01 2024-08-23 深圳市科中云技术有限公司 Central air conditioner regulation and control method and system based on virtual power plant
CN117439276B (en) * 2023-12-21 2024-04-16 深圳前海中碳综合能源科技有限公司 Virtual power plant demand side management and control system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109934470A (en) * 2019-02-27 2019-06-25 上海交通大学 It polymerize the information physical modeling and control method of extensive air conditioner load
CN110322056A (en) * 2019-06-19 2019-10-11 河海大学 It is a kind of meter and central air conditioner system the random ADAPTIVE ROBUST Optimization Scheduling of virtual plant
CN111474900A (en) * 2019-11-21 2020-07-31 国网四川省电力公司经济技术研究院 Regional comprehensive energy system operation method and system based on virtual power plant

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109934470A (en) * 2019-02-27 2019-06-25 上海交通大学 It polymerize the information physical modeling and control method of extensive air conditioner load
CN110322056A (en) * 2019-06-19 2019-10-11 河海大学 It is a kind of meter and central air conditioner system the random ADAPTIVE ROBUST Optimization Scheduling of virtual plant
CN111474900A (en) * 2019-11-21 2020-07-31 国网四川省电力公司经济技术研究院 Regional comprehensive energy system operation method and system based on virtual power plant

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Coordinated Scheduling of Virtual Energy Storage System for Optimal Microgrid Operation;Chandra Prakash Barala 等;《2020 IEEE 9th Power India International Conference (PIICON)》;20200610;全文 *
上海市黄浦区商业建筑虚拟电厂典型应用;屠盛春 等;《电力需求侧管理》;20200120;第22卷(第01期);52-57 *
基于需求侧响应的空调负荷虚拟储能模型研究;王怡岚 等;《电网技术》;20170205;第41卷(第02期);394-401 *
虚拟电厂背景下的空调负荷控制策略;杨德玲 等;《电力需求侧管理》;20200120;第22卷(第01期);48-51 *
黄浦区商业建筑虚拟电厂规范化建设方法研究;黄浦区发展改革委员会 等;《上海节能》;20180228(第02期);89-95 *

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