CN107732977A - A kind of AGC real-time scheduling methods based on demand response - Google Patents
A kind of AGC real-time scheduling methods based on demand response Download PDFInfo
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H02J3/386—
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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
- 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]
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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
- 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
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- 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
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- 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
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- 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
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- 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
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Abstract
The invention discloses a kind of AGC real-time scheduling methods based on demand response, and control centre first contributes according to the wind power plant and load of prediction, and programming dispatching is carried out to generator output using direct current optimal power flow algorithm.In actual motion, the prediction error value of known wind power output, the positive and negative reserve level of demand response potentiality and thermal power plant of current intelligent appliance is calculated, considers the security constraint of power network, the maximization of wind electricity digestion is realized while alleviating wind-powered electricity generation prediction error under intelligent appliance and AGC coordination control.If under intelligent appliance and AGC collective effect, the power-balance of system can not be realized, then when grid power is negative variation, meet the appropriate comfort level section for expanding intelligent appliance under the premise of user's request, can not still realize, carry out cutting load；When grid power is positive fluctuation, then abandons wind and calculate and abandon air quantity.The present invention makes full use of intelligent appliance to participate in response, considers user to the comfort level of intelligent appliance and the response times of intelligent appliance, alleviating wind-powered electricity generation prediction error influences.
Description
Technical field
The invention belongs to Real-Time Scheduling technical field, more particularly to a kind of AGC real-time scheduling methods based on demand response.
Background technology
Currently, up to 10%~15%, the randomness and uncertainty of wind-powered electricity generation increase the error predicted before output of wind electric field 1h
Big system net load fluctuation amplitude and speed, mainly by Automatic Generation Control (automatic in Real-Time Scheduling time scale
Generation control, AGC) unit undertakes regulation task.
At present, AGC unit outputs datum mark passes through ultra-short term by control centre in operation plan formulation process in power network
Load and wind-powered electricity generation information of forecasting determine that each unit basic point and the participation factor are to plan the result of gross capability optimization distribution, every 15min
A rolling optimization is done, keeps constant within the period.If the output deviation expection in 15min of wind power is larger, and unidirectionally
Lasting climbing, AGC unit plan gross capabilities will produce relatively large deviation with net load, and if customizing make not in time accordingly in the original plan
Adjustment, the quick regulation pressure and operating cost of AGC units will be increased.
The content of the invention
Goal of the invention：For problem above, the present invention proposes a kind of AGC real-time scheduling methods based on demand response, filled
Divide and participate in response using intelligent appliance, consider user to the comfort level of intelligent appliance and the response times of intelligent appliance, alleviation wind
Electricity prediction error influences.
Technical scheme：To realize the purpose of the present invention, the technical solution adopted in the present invention is：One kind is based on demand response
AGC real-time scheduling methods, specifically include following steps：
(1) local controller obtains wind power plant and the prediction of load is contributed, using direct current Optimal Power Flow algorithm to generator
Contribute and carry out programming dispatching；
(2) the prediction error of output of wind electric field is handed down to the secondary local controller of generator by local controller, wind power plant,
The secondary local controller of generator and load carries out information exchange with local controller；
(3) according to the prediction error of output of wind electric field, the positive and negative spare capacity of generator, the real-time output and demand of load
Potentiality are responded, establish demand response and AGC scheduling mathematic models, and output result.
In step (3), the specific calculating process of the mathematical modeling is：
(1) calculated load undulate quantity and the demand response potentiality of intelligent appliance；
(2) consider a variety of constraints, optimize generator output and demand response amount, judge whether to restrain, do not restrain, continue
Step (3), convergence then continue step (4)；
(3) when system power is negative variation, then the comfort level section of load can be responded by increasing user couple；When beyond user
During receptible comfort level section, using cutting load measure；When system power is positive fluctuation, using abandoning wind measure；Then return
To step (2)；
(4) optimum results are exported, update the running status of intelligent appliance.
For mathematical modeling in step (3) to maximize consumption wind-powered electricity generation as object function, a variety of constraints include wattful power
Rate Constraints of Equilibrium, unit capacity constraint, unit climbing and descending grade rate constraint, the constraint of trend circuit, demand response capacity-constrained
Constrained with load comfort level.
Demand response potentiality computational methods in the demand response capacity-constrained are：
When demand response is load increase：
DPAC(t+1)=0
When demand response is that load is reduced：
Wherein, DPAC(t+1)、DPWHAnd DP (t+1)EV(t+1) it is respectively air-conditioning, water heater and electric automobile at the t+1 moment
Demand response potentiality state (" 1 " represent household electrical appliances have demand response potentiality, " 0 " represent household electrical appliances there is no demand response potentiality), t is
Period, TACFor indoor temperature,For the setting value of air-conditioning,For the temperature range of air-conditioning,For comfortable room temperature
Higher limit, TWHWater heater temperature,For the water temperature setting value of water heater,For the water temperature section of water heater,It is easypro
The lower limit of suitable water temperature, NEV(t) for electric automobile t connection status (" 1 " represent electric automobile connection has gone up charging pile,
" 0 " represent electric automobile do not connect charging pile), SOC (t+1) be the electric automobile t+1 moment state-of-charge, PEVFor electronic vapour
The rated power (kW) of car, T are the electric automobile complete charge time, QEVFor the battery total capacity of electric automobile, η is imitated for charging
Rate, SOCminThe minimum state-of-charge required for electric automobile in charging finishing time.
The intelligent demand response potentiality of single Load aggregation business are calculated such as formula：
Wherein, DRPtotal(t+1) for single Load aggregation business in the intelligent demand response potentiality at t+1 moment, N1For air-conditioning
Number,For the rated power (kW) of i-th of air-conditioning,For i-th of air-conditioning t demand response potentiality shape
State, N2For the number of water heater,For the rated power (kW) of j-th of water heater,It is j-th of water heater in t
Demand response potentiality state, N3For the number of electric automobile,For the rated power (kW) of k-th of electric automobile,For
Demand response potentiality state of k-th of electric automobile in t.
Operation principle：Alleviate demand response and the AGC real time coordinations that wind-powered electricity generation prediction error influences the invention provides a kind of
Dispatching method, first control centre are contributed according to the wind power plant and load prediction obtained, using direct current optimal power flow algorithm to hair
Motor, which is contributed, carries out programming dispatching.In actual motion, it is known that the prediction error value of wind power output, calculate current intelligent appliance
The positive and negative reserve level of demand response potentiality and thermal power plant, the security constraint of power network is considered, under intelligent appliance and AGC coordination control
The maximization of wind electricity digestion is realized while alleviating wind-powered electricity generation prediction error.If under intelligent appliance and AGC collective effect, Wu Fashi
The power-balance of existing system, then when grid power is negative variation, meet suitably to expand intelligent appliance under the premise of user's request
It comfort level section, can not still realize, carry out cutting load；When grid power is positive fluctuation, then carry out abandoning wind and calculating abandoning air quantity.
The present invention considers that the climbing rate constraint of generator, trend circuit constrain and the real-time requirement of intelligent appliance responds potentiality, in real time association
Regulation and control intelligent appliance processed and AGC, alleviate the influence of wind-powered electricity generation prediction error band.
Beneficial effect：The present invention is rung under high wind-powered electricity generation permeability and high prediction error condition using the demand of intelligent appliance
Potentiality are answered, the constraint of generator climbing rate and the constraint of trend circuit is considered, realizes Real-Time Scheduling to meet power-balance.Institute's extracting method
The influence of wind-powered electricity generation prediction error band can be alleviated, and aid decision can be provided for different operating conditions.
Brief description of the drawings
Fig. 1 is the schematic diagram of the AGC real-time scheduling methods of the invention based on demand response；
Fig. 2 is the flow chart of the AGC real-time scheduling methods of the invention based on demand response；
Fig. 3 is the system emulation line map for realizing example；
Fig. 4 is load day curve map；
Fig. 5, which is that wind-powered electricity generation is actual, to contribute, predicts and contribute and predict error curve diagram；
Fig. 6 is not consider generator output changing value and system imbalance power curve map under demand response；
Fig. 7 is that the system under demand response that whether there is is the maximum capacity comparison diagram of balance power；
Fig. 8 is whether to consider that the lower system of climbing rate constraint is respectively contributed result figure；
Fig. 9 is to abandon the graph of relation that air quantity changes with wind-powered electricity generation ratio and demand response ratio；
Figure 10 is to cut the graph of relation that load changes with wind-powered electricity generation ratio and demand response ratio.
Embodiment
Technical scheme is further described with reference to the accompanying drawings and examples.
To solve the problems, such as to exist under high wind-powered electricity generation permeability and high prediction error condition, demand response resource can be used to participate in
Operation of power networks controls.Compared with conventional power generation usage resource, in the case of conditions permit, it can respond rapidly to dispatch and control refers to
Order, without the regulation inertia of conventional power generation usage unit；In addition, can be from because the distribution of demand response resource is more scattered
Network and geographical aspect optimize polymerization, so as to be advantageous in case of emergency formulate the scheme of being precisely controlled.Therefore the present invention
Propose a kind of demand response and AGC real time coordination dispatching methods alleviated wind-powered electricity generation prediction error and influenceed.
It is the demand response and AGC real time coordinations tune that alleviation wind-powered electricity generation prediction error of the present invention influences as shown in Figure 1
Degree method control principle drawing, it is to alleviate demand response and the AGC real time coordination dispatching parties that wind-powered electricity generation prediction error influences as shown in Figure 2
The flow chart of method.
The method of the present invention specifically includes following steps：
(1) control centre obtains wind power plant and the prediction output of load from pre- measured center, excellent using direct current on this basis
Change power flow algorithm and programming dispatching is carried out to the output in thermal power plant.
(2) during real time execution, the prediction error of output of wind electric field is handed down to thermal power plant by control centre
Secondary local controller, the secondary local controller of wind power plant, thermal power plant and load can enter row information friendship with local controller
Mutually.
(3) according to the prediction error of output of wind electric field, the positive and negative spare capacity in thermal power plant, the real-time output of load and
The information such as demand response potentiality, establish demand response and AGC scheduling mathematic model, and output result.
In step (3), the specific calculating process of mathematical modeling is：
(1) calculated load undulate quantity and the demand response potentiality of intelligent appliance；
(2) consider a variety of constraints, optimize generator output and demand response amount, judge whether to restrain, do not restrain, continue
Step (3), convergence then continue step (4)；
(3) when system power is negative variation, then the comfort level section of load can be responded by increasing user couple；When beyond user
During receptible comfort level section, using cutting load measure；When system power is positive fluctuation, using abandoning wind measure；Then return
To step (2)；
(4) optimum results are exported, update the running status of intelligent appliance.
To maximize consumption wind-powered electricity generation as object function in mathematical modeling, to meet each period power-balance, climbing rate
Deng for constraints, it is embodied as：
Constraints：
A. active power balance constraint：
In formula, NgFor the number of generator；NLFor the number of users of load；PgiFor the output of i-th generator；PwindFor wind
The pre- power scale of electricity；ΔPwFor the undulate quantity of wind-powered electricity generation；PljFor the power of j-th of family's total load.
B. unit capacity constrains：
Pgi,min≤Pgi≤Pgi,max
In formula, Pgi,minAnd Pgi,maxRespectively unit i minimum and maximum output limit value.
C. unit climbing and descending grade rate constraint：
In formula, Pgi,tFor t unit i active power output；Δ T is time interval；Ru,iAnd Rd,iRespectively unit i's has
Work(output raising and lowering speed, unit MW/min.
D. trend circuit constrains：
In formula,For the effective power flow on l circuits；For the maximum active power on l circuits.
E. demand response capacity-constrained：
When demand response is load increase：
Pj0≤Plj≤Pj0+DRPj
When demand response is that load is reduced：
Pj0-DRPj≤Plj≤Pj0
In formula, Pj0For the initial power of j user's total load；DRPjThe demand response potentiality of load can be responded for j user.
F. load comfort level constrains：
In formula, CAC(t), CWHAnd C (t)EV(t) be respectively t air-conditioning, water heater and electric automobile comfort level index；WithThe respectively upper limit of room temperature and water temperature；WithRespectively under room temperature and water temperature
Limit；TAC(t) it is the room temperature of t；TWH(t) it is the water temperature of t；N (t) is total charging times in the electric automobile t times；
SOC (t) is the state-of-charge of electric automobile t；SOCminThe SOC at least needing expeced time to reach for electric automobile.
There is not convergent possibility in system optimized control method, when system power is negative variation, then can increase user
The comfort level section of load (air-conditioning, water heater and electric automobile) pair can be responded, that is, improves the bound and electricity of room temperature and water temperature
The charging times constraint of electrical automobile.When comfort level section receptible beyond user, using cutting load measure.Work as system power
For positive fluctuation when, using abandoning wind measure.
User's total load power prediction is error free, but take part in demand response before because of load, then current loads power
It can fluctuate：
ΔPlj=Plj-Plj0
In formula, Δ PljFor the load fluctuation amount of j user；PljFor the current load amount after demand response occurs before；Plj0
For the predicted load of j user.
Demand response potentiality computational methods in demand response capacity-constrained：
When demand response is load increase：
DPAC(t+1)=0
When demand response is that load is reduced：
Wherein, DPAC(t+1)、DPWHAnd DP (t+1)EV(t+1) it is respectively air-conditioning, water heater and electric automobile at the t+1 moment
Demand response potentiality state (" 1 " represent household electrical appliances have demand response potentiality, " 0 " represent household electrical appliances there is no demand response potentiality), t is
Period, TACFor indoor temperature,For the setting value of air-conditioning,For the temperature range of air-conditioning,For comfortable room temperature
Higher limit, TWHWater heater temperature,For the water temperature setting value of water heater,For the water temperature section of water heater,It is easypro
The lower limit of suitable water temperature, NEV(t) for electric automobile t connection status (" 1 " represent electric automobile connection has gone up charging pile,
" 0 " represent electric automobile do not connect charging pile), SOC (t+1) be the electric automobile t+1 moment state-of-charge, PEVFor electronic vapour
The rated power (kW) of car, T are the electric automobile complete charge time, QEVFor the battery total capacity of electric automobile, η is imitated for charging
Rate, SOCminThe minimum state-of-charge required for electric automobile in charging finishing time.
According to the demand response state of each intelligent appliance, the intelligent demand response that single Load aggregation business is calculated is dived
Power, such as formula：
Wherein, DRPtotal(t+1) for single Load aggregation business in the intelligent demand response potentiality at t+1 moment, N1For air-conditioning
Number,For the rated power (kW) of i-th of air-conditioning,For i-th of air-conditioning t demand response potentiality shape
State, N2For the number of water heater,For the rated power (kW) of j-th of water heater,It is j-th of water heater in t
Demand response potentiality state, N3For the number of electric automobile,For the rated power (kW) of k-th of electric automobile,
For k-th of electric automobile t demand response potentiality state.
Fig. 3 show the simulated line figure for realizing example, and the system includes 34 transmission lines of electricity and 17 node loads, its
Middle wind power plant is located at node 20.Load day curve as shown in figure 4, the parameter of node load is as shown in table 1, intelligent appliance parameter
As shown in table 2.
Table 1
Table 2
It is illustrated in figure 5 the actual output of wind-powered electricity generation, prediction is contributed and prediction error curve, wind-powered electricity generation installed capacity now are
530MW.In order to illustrate high wind-powered electricity generation predict error condition under, only rely on generator AGC carry out Real-Time Scheduling deficiency, Fig. 6 pairs
The imbalance power of system and the curve map of generator output change are compared.System is delayed after Fig. 7 illustrates addition demand response
Solve the ability that wind-powered electricity generation prediction error influences.Fig. 8 illustrates whether there is the difference of the lower demand response of climbing rate constraint and generator output.
Table 2 lists the power whetheing there is under the constraint of trend circuit on each circuit, if not considering as seen from table, circuit constraint can cause circuit
14-16 power is 405.49MW, beyond power allowances 400MW.
Table 3
| Circuit | Power allowances (MW) | Do not consider that circuit constrains line power (MW) | Consider circuit constraint line power (MW) |
| 1-2 | 140 | 21.88 | 21.45 |
| 1-3 | 140 | -58.96 | -58.04 |
| 1-5 | 140 | 12.76 | 12.26 |
| 2-4 | 140 | -6.72 | -6.96 |
| 2-6 | 140 | 14.46 | 14.27 |
| 3-9 | 140 | 35.81 | 34.98 |
| 3-24 | 408 | -257.13 | -253.79 |
| 4-9 | 140 | -73.72 | -72.44 |
| 5-10 | 140 | -51.92 | -51.02 |
| 6-10 | 140 | -108.88 | -107.55 |
| 7-8 | 140 | -27.00 | -27.00 |
| 8-9 | 140 | -104.33 | -103.39 |
| 8-10 | 140 | -75.95 | -75.37 |
| 9-11 | 408 | -145.52 | -143.76 |
| 9-12 | 408 | -153.56 | -152.42 |
| 10-11 | 408 | -201.36 | -198.90 |
| 10-12 | 408 | -209.41 | -207.56 |
| 11-13 | 400 | -115.47 | -115.36 |
| 11-14 | 400 | -231.40 | -227.30 |
| 12-13 | 400 | -101.29 | -100.09 |
| 12-23 | 400 | -261.67 | -259.90 |
| 13-23 | 400 | -236.49 | -235.17 |
| 14-16 | 400 | -405.49 | -400 |
| 15-16 | 400 | 88.09 | 86.98 |
| 15-21 | 400*2 | -237.51*2 | -235.28*2 |
| 15-24 | 400 | 257.13 | 253.79 |
| 16-17 | 400 | -347.17 | -342.54 |
| 16-19 | 400 | 77.30 | 71.17 |
| 17-18 | 400 | -205.58 | -200.99 |
| 17-22 | 400 | -141.59 | -141.55 |
| 18-21 | 400*2 | -46.70*2 | -48.94*2 |
| 19-20 | 400*2 | -43.92*2 | -46.20*2 |
| 20-23 | 400*2 | -25.60*2 | -27.15*2 |
| 21-22 | 400 | -158.41 | -158.45 |
With the increase of wind-powered electricity generation ratio, abandon air quantity and cut load increase, after demand response adds, abandon air quantity and cut negative
Lotus amount can be reduced, and increasing with demand response ratio, further be reduced.If table 4 is different wind-powered electricity generation ratios and demand response
Air quantity and cutting load amount are abandoned under ratio.Fig. 9 is to abandon the relation curve that air quantity changes with wind-powered electricity generation ratio and demand response ratio, Figure 10
To cut the relation curve that load changes with wind-powered electricity generation ratio and demand response ratio.
Table 4
Claims (5)
- A kind of 1. AGC real-time scheduling methods based on demand response, it is characterised in that：Specifically include following steps：(1) local controller obtains wind power plant and the prediction of load is contributed, using direct current Optimal Power Flow algorithm to generator output Carry out programming dispatching；(2) the prediction error of output of wind electric field is handed down to the secondary local controller of generator, wind power plant, generating by local controller The secondary local controller of machine and load carries out information exchange with local controller；(3) according to the prediction error of output of wind electric field, the positive and negative spare capacity of generator, the real-time output of load and demand response Potentiality, establish demand response and AGC scheduling mathematic models, and output result.
- 2. the AGC real-time scheduling methods according to claim 1 based on demand response, it is characterised in that：In step (3), The specific calculating process of the mathematical modeling is：(1) calculated load undulate quantity and the demand response potentiality of intelligent appliance；(2) consider a variety of constraints, optimize generator output and demand response amount, judge whether to restrain, do not restrain, continue step (3), convergence then continues step (4)；(3) when system power is negative variation, then the comfort level section of load can be responded by increasing user couple；It can be connect when beyond user During the comfort level section received, using cutting load measure；When system power is positive fluctuation, using abandoning wind measure；It is then return to step Suddenly (2)；(4) optimum results are exported, update the running status of intelligent appliance.
- 3. the AGC real-time scheduling methods according to claim 2 based on demand response, it is characterised in that：In step (3) For mathematical modeling to maximize consumption wind-powered electricity generation as object function, a variety of constraints include active power balance constraint, unit capacity Constraint, unit climbing and descending grade rate constraint, the constraint of trend circuit, demand response capacity-constrained and the constraint of load comfort level.
- 4. the AGC real-time scheduling methods according to claim 3 based on demand response, it is characterised in that：The demand is rung The demand response potentiality computational methods in capacity-constrained are answered to be：When demand response is load increase：DPAC(t+1)=0When demand response is that load is reduced：Wherein, DPAC(t+1)、DPWHAnd DP (t+1)EV(t+1) it is respectively the need of air-conditioning, water heater and electric automobile at the t+1 moment Response potentiality state (" 1 " represents that household electrical appliances there are demand response potentiality, and " 0 " represents that household electrical appliances do not have demand response potentiality) is sought, t is the time Section, TACFor indoor temperature,For the setting value of air-conditioning,For the temperature range of air-conditioning,For the upper limit of comfortable room temperature Value, TWHWater heater temperature,For the water temperature setting value of water heater,For the water temperature section of water heater,For comfortable water The lower limit of temperature, NEV(t) for electric automobile, in the connection status of t, (" 1 " represents that charging pile has been gone up in electric automobile connection, " 0 " table Show that electric automobile does not connect charging pile), SOC (t+1) be the electric automobile t+1 moment state-of-charge, PEVFor electric automobile Rated power (kW), T are the electric automobile complete charge time, QEVFor the battery total capacity of electric automobile, η is charge efficiency, SOCminThe minimum state-of-charge required for electric automobile in charging finishing time.
- 5. the AGC real-time scheduling methods according to claim 4 based on demand response, it is characterised in that：Single load gathers The intelligent demand response potentiality for closing business are calculated such as formula：<mrow> <msub> <mi>DRP</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> </munderover> <msubsup> <mi>P</mi> <mrow> <mi>A</mi> <mi>C</mi> </mrow> <mi>i</mi> </msubsup> <mo>&CenterDot;</mo> <msubsup> <mi>DP</mi> <mrow> <mi>A</mi> <mi>C</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mn>2</mn> </msub> </munderover> <msubsup> <mi>P</mi> <mrow> <mi>W</mi> <mi>H</mi> </mrow> <mi>j</mi> </msubsup> <mo>&CenterDot;</mo> <msubsup> <mi>DP</mi> <mrow> <mi>W</mi> <mi>H</mi> </mrow> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>k</mi> </msub> </munderover> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>k</mi> </msubsup> <mo>&CenterDot;</mo> <msubsup> <mi>DP</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>k</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>Wherein, DRPtotal(t+1) for single Load aggregation business in the intelligent demand response potentiality at t+1 moment, N1For of air-conditioning Number,For the rated power (kW) of i-th of air-conditioning,It is i-th of air-conditioning in the demand response potentiality state of t, N2 For the number of water heater,For the rated power (kW) of j-th of water heater,For j-th of water heater t demand Respond potentiality state, N3For the number of electric automobile,For the rated power (kW) of k-th of electric automobile,For k-th Demand response potentiality state of the electric automobile in t.
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