CN107732977B - AGC real-time scheduling method based on demand response - Google Patents

AGC real-time scheduling method based on demand response Download PDF

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CN107732977B
CN107732977B CN201710858036.7A CN201710858036A CN107732977B CN 107732977 B CN107732977 B CN 107732977B CN 201710858036 A CN201710858036 A CN 201710858036A CN 107732977 B CN107732977 B CN 107732977B
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demand response
power
load
time
agc
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CN107732977A (en
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宁佳
汤奕
高丙团
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Southeast University
Liyang Research Institute of Southeast University
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Liyang Research Institute of Southeast University
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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
    • 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
    • H02J3/386—
    • 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
    • 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
    • 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
    • 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

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Abstract

The invention discloses an AGC real-time scheduling method based on demand response. In actual operation, the prediction error value of the wind power output is known, the demand response potential of the current intelligent household appliance and the positive and negative spare consumption of the thermal power plant are calculated, the safety constraint of a power grid is considered, and the maximization of wind power consumption is realized while the wind power prediction error is relieved under the coordination control of the intelligent household appliance and AGC. If the power balance of the system cannot be realized under the combined action of the intelligent household appliance and the AGC, when the power of the power grid is in negative fluctuation, the comfort level interval of the intelligent household appliance is properly expanded on the premise of meeting the user requirements, and the load shedding is carried out if the power grid cannot be realized; and when the power of the power grid fluctuates positively, abandoning the wind and calculating the abandoning wind quantity. According to the method, the intelligent household appliances are fully utilized to participate in response, the comfort level of the user to the intelligent household appliances and the response times of the intelligent household appliances are considered, and the influence of wind power prediction errors is relieved.

Description

AGC real-time scheduling method based on demand response
Technical Field
The invention belongs to the technical field of real-time scheduling, and particularly relates to an AGC (automatic gain control) real-time scheduling method based on demand response.
Background
At present, the predicted error before the wind power plant outputs for 1h can reach 10% -15%, the randomness and uncertainty of wind power increase the system net load fluctuation amplitude and speed, and an Automatic Generation Control (AGC) unit is mainly used for bearing adjustment tasks on a real-time scheduling time scale.
At present, the output reference point of an AGC unit in a power grid is determined by a dispatching center through ultra-short-term load and wind power prediction information in the process of making a dispatching plan, each unit base point and participation factors are the result of optimized distribution of the planned total output, rolling optimization is performed every 15min, and the time period is kept unchanged. If the output deviation of the wind power is expected to be large within 15min and the slope is continuously climbed in a single direction, the total output and the net load of the AGC unit plan generate large deviation, and if the original plan is not customized in time to make corresponding adjustment, the rapid adjusting pressure and the operating cost of the AGC unit are increased.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides an AGC real-time scheduling method based on demand response, which makes full use of participation of intelligent household appliances in response, considers the comfort level of a user to the intelligent household appliances and the response times of the intelligent household appliances, and relieves the influence of wind power prediction errors.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: an AGC real-time scheduling method based on demand response specifically comprises the following steps:
(1) the local controller obtains the predicted output of the wind power plant and the load, and planning and scheduling the output of the generator by adopting a direct current optimization power flow algorithm;
(2) the local controller transmits the prediction error of the output of the wind power plant to the copy controller of the generator, and the wind power plant, the generator and the copy controller of the load carry out information interaction with the local controller;
(3) and establishing a demand response and AGC scheduling mathematical model according to the prediction error of the output of the wind power plant, the positive and negative spare capacity of the generator, the real-time output of the load and the demand response potential, and outputting the result.
In the step (3), the specific calculation process of the mathematical model is as follows:
(1) calculating the load fluctuation amount and the demand response potential of the intelligent household appliance;
(2) considering various constraints, optimizing the output and the demand response quantity of the generator, judging whether the generator is converged, if the generator is not converged, continuing the step (3), and if the generator is converged, continuing the step (4);
(3) when the system power is in negative fluctuation, increasing the comfort interval of the user to the responsive load; when the comfort level range which can be accepted by a user is exceeded, a load shedding measure is adopted; when the system power is in positive fluctuation, adopting a wind abandoning measure; then returning to the step (2);
(4) and outputting an optimization result and updating the running state of the intelligent household appliance.
The mathematical model in the step (3) takes maximum wind power consumption as an objective function, and the multiple constraints comprise active power balance constraint, unit capacity constraint, unit climbing and descending rate constraint, tide line constraint, demand response capacity constraint and load comfort constraint.
The demand response potential calculation method in the demand response capacity constraint comprises the following steps:
when demand response is a load increase:
DPAC(t+1)＝0
Figure GDA0002797480150000021
Figure GDA0002797480150000022
when the demand response is a load reduction:
Figure GDA0002797480150000023
Figure GDA0002797480150000024
Figure GDA0002797480150000025
wherein DPAC(t+1)、DPWH(t +1) and DPEV(t +1) the demand response potential states of the air conditioner, the water heater and the electric automobile at the moment of t +1 respectively, wherein "1" represents that the household appliance has the demand response potential,"0" indicates that the household appliance has no demand response potential, T is a time period, TACIt is the temperature in the room that is,
Figure GDA0002797480150000026
is a set value of the air conditioner,
Figure GDA0002797480150000027
is a temperature interval of the air conditioner,
Figure GDA0002797480150000028
upper limit value of comfortable room temperature, TWHThe water temperature of the water heater is controlled,
Figure GDA0002797480150000029
is the set value of the water temperature of the water heater,
Figure GDA00027974801500000210
is the water temperature interval of the water heater,
Figure GDA00027974801500000211
lower limit of comfortable water temperature, NEV(t) is the connection state of the electric automobile at the time t, 1 represents that the electric automobile is connected with the charging pile, 0 represents that the electric automobile is not connected with the charging pile, SOC (t +1) is the charge state of the electric automobile at the time t +1, and PEVRated power (kW) of the electric vehicle, T is end of charge time of the electric vehicle, QEVThe total battery capacity of the electric vehicle, eta is the charging efficiency, SOCminThe minimum state of charge required by the electric automobile at the end of charging is obtained.
The intelligent demand response potential of a single load aggregator is calculated as:
Figure GDA00027974801500000212
wherein, DRPtotal(t +1) is the intelligent demand response potential of a single load aggregator at time t +1, N1The number of the air conditioners is equal to that of the air conditioners,
Figure GDA0002797480150000031
rated power (kW) for the ith air conditioner,
Figure GDA0002797480150000032
for the demand response potential state of the ith air conditioner at time t, N2The number of the water heaters is the same as the number of the water heaters,
Figure GDA0002797480150000033
rated power (kW) for the jth water heater,
Figure GDA0002797480150000034
for the demand response potential status of the jth water heater at time t, N3The number of the electric automobiles is the same as the number of the electric automobiles,
Figure GDA0002797480150000035
rated power (kW) for the kth electric vehicle,
Figure GDA0002797480150000036
the demand response potential state of the kth electric vehicle at the moment t.
The working principle is as follows: the invention provides a demand response and AGC real-time coordinated dispatching method for relieving wind power prediction error influence. In actual operation, the prediction error value of the wind power output is known, the demand response potential of the current intelligent household appliance and the positive and negative spare consumption of the thermal power plant are calculated, the safety constraint of a power grid is considered, and the maximization of wind power consumption is realized while the wind power prediction error is relieved under the coordination control of the intelligent household appliance and AGC. If the power balance of the system cannot be realized under the combined action of the intelligent household appliance and the AGC, when the power of the power grid is in negative fluctuation, the comfort level interval of the intelligent household appliance is properly expanded on the premise of meeting the user requirements, and the load shedding is carried out if the power grid cannot be realized; and when the power of the power grid fluctuates positively, abandoning wind and calculating the abandoning wind quantity. According to the method, the slope climbing rate constraint and the power flow line constraint of the generator and the real-time demand response potential of the intelligent household appliance are considered, the intelligent household appliance and the AGC are coordinated and controlled in real time, and the influence caused by wind power prediction errors is relieved.
Has the advantages that: under the conditions of high wind power permeability and high prediction error, the method utilizes the demand response potential of the intelligent household appliance, considers the generator climbing rate constraint and the power flow line constraint, and realizes real-time scheduling to meet the power balance. The method can relieve the influence caused by wind power prediction errors and can provide auxiliary decisions for different operation conditions.
Drawings
FIG. 1 is a schematic diagram of the real-time AGC scheduling method based on demand response of the present invention;
FIG. 2 is a flow chart of the AGC real-time scheduling method based on demand response of the present invention;
FIG. 3 is a diagram of a system simulation circuit implementing an example;
FIG. 4 is a graph of load versus day;
FIG. 5 is a graph of actual wind power output, predicted output, and predicted error;
FIG. 6 is a graph of generator output variation and system imbalance power without regard to demand response;
FIG. 7 is a graph comparing the maximum capacity of the system for balanced power with and without a demand response;
FIG. 8 is a graph showing the results of the system outputs under the constraint of whether the climbing rate is considered;
FIG. 9 is a graph showing the relationship between the wind curtailment rate and the change of the demand response rate;
FIG. 10 is a graph of load shedding as a function of wind power ratio and demand response ratio.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
In order to solve the problems existing under the conditions of high wind power permeability and high prediction error, demand response resources can be adopted to participate in the operation control of the power grid. Compared with the conventional power generation resource, the system can quickly respond to scheduling and control commands without the regulating inertia of the conventional generator set under the condition that the condition allows; in addition, as the distribution of the demand response resources is dispersed, the optimization and aggregation can be carried out from the network and the geographical level, thereby being beneficial to making an accurate control scheme under the emergency condition. Therefore, the invention provides a demand response and AGC real-time coordination scheduling method for relieving wind power prediction error influence.
Fig. 1 is a control schematic diagram of a demand response and AGC real-time coordinated scheduling method for alleviating the influence of wind power prediction errors, and fig. 2 is a flow chart of the demand response and AGC real-time coordinated scheduling method for alleviating the influence of wind power prediction errors.
The method specifically comprises the following steps:
(1) and the dispatching center acquires the predicted output of the wind power plant and the load from the prediction center, and planning and dispatching the output of the thermal power plant by adopting a direct current optimization power flow algorithm on the basis.
(2) In the real-time operation process, the dispatching center issues the prediction error of the output of the wind power plant to the copy controller of the thermal power plant, and the wind power plant, the thermal power plant and the copy controller of the load can perform information interaction with the local controller.
(3) And establishing a scheduling mathematical model of demand response and AGC according to the prediction error of the output of the wind power plant, the positive and negative spare capacity of the thermal power plant, the real-time output of the load, the demand response potential and other information, and outputting the result.
In the step (3), the specific calculation process of the mathematical model is as follows:
(1) calculating the load fluctuation amount and the demand response potential of the intelligent household appliance;
(2) considering various constraints, optimizing the output and the demand response quantity of the generator, judging whether the generator is converged, if the generator is not converged, continuing the step (3), and if the generator is converged, continuing the step (4);
(3) when the system power is in negative fluctuation, increasing the comfort interval of the user to the responsive load; when the comfort level range which can be accepted by a user is exceeded, a load shedding measure is adopted; when the system power is in positive fluctuation, adopting a wind abandoning measure; then returning to the step (2);
(4) and outputting an optimization result and updating the running state of the intelligent household appliance.
The method takes maximum absorption wind power as a target function in a mathematical model, takes the power balance, the climbing rate and the like in each time period as constraint conditions, and is specifically represented as follows:
Figure GDA0002797480150000041
constraint conditions are as follows:
A. active power balance constraint:
Figure GDA0002797480150000042
in the formula, NgThe number of the generators; n is a radical ofLThe number of users of the load; pgiThe output of the ith generator; pwindThe predicted power of the wind power is obtained; delta PwThe fluctuation quantity of the wind power is obtained; pljThe power of the jth home total load.
B. And (3) unit capacity constraint:
Pgi,min≤Pgi≤Pgi,max
in the formula, Pgi,minAnd Pgi,maxThe minimum and maximum output limits of the unit i are respectively.
C. Unit climbing and descending rate constraint:
Figure GDA0002797480150000051
in the formula, Pgi,tThe active output of the unit i at the moment t; Δ T is the time interval; ru,iAnd Rd,iThe active output rise and fall speeds of the unit i are respectively unit MW/min.
D. And (3) restraining a tidal current circuit:
Figure GDA0002797480150000052
in the formula (I), the compound is shown in the specification,
Figure GDA0002797480150000053
is the active power flow on the l line;
Figure GDA0002797480150000054
is the maximum active power on the l lines.
E. Demand response capacity constraint:
when demand response is a load increase:
Pj0≤Plj≤Pj0+DRPj
when the demand response is a load reduction:
Pj0-DRPj≤Plj≤Pj0
in the formula, Pj0Initial power of total load of j users; DRPjThe demand response potential of the user to respond to the load is j.
F. And (3) load comfort degree restraint:
Figure GDA0002797480150000055
Figure GDA0002797480150000056
Figure GDA0002797480150000057
in the formula, CAC(t)，CWH(t) and CEV(t) comfort level indexes of an air conditioner, a water heater and an electric automobile at the moment t respectively;
Figure GDA0002797480150000058
and
Figure GDA0002797480150000059
the upper limits of the room temperature and the water temperature are respectively;
Figure GDA00027974801500000510
and
Figure GDA00027974801500000511
lower limits for room temperature and water temperature, respectively; t isAC(t) is the room temperature at time t; t isWH(t) the water temperature at time t; n (t) is the total charging times of the electric automobile within t time; SOC (t) is the state of charge of the electric vehicle at time t; SOCminAt least the SOC that needs to be reached is expected for the electric vehicle.
The system optimization control method has the possibility of non-convergence, and when the system power is in negative fluctuation, the comfort degree interval of a user to the responsive load (an air conditioner, a water heater and an electric automobile) can be increased, namely the upper limit and the lower limit of the room temperature and the water temperature and the charging frequency constraint of the electric automobile are improved. And when the comfort range accepted by the user is exceeded, adopting a load shedding measure. When the power of the system fluctuates positively, a wind abandon measure is adopted.
The total load power prediction of the user is error-free, but because the load participates in the demand response before, the current load power fluctuates:
ΔPlj＝Plj-Plj0
in the formula,. DELTA.PljThe load fluctuation amount of j users; pljThe current load after the previous demand response occurs; plj0And the load of j users is predicted.
A demand response potential calculation method in a demand response capacity constraint:
when demand response is a load increase:
DPAC(t+1)＝0
Figure GDA0002797480150000061
Figure GDA0002797480150000062
when the demand response is a load reduction:
Figure GDA0002797480150000063
Figure GDA0002797480150000064
Figure GDA0002797480150000065
wherein DPAC(t+1)、DPWH(t +1) and DPEV(T +1) the demand response potential states of the air conditioner, the water heater and the electric automobile at the time T +1 respectively (1 'represents that the household appliance has demand response potential, 0' represents that the household appliance has no demand response potential), T is a time period, and T isACIt is the temperature in the room that is,
Figure GDA0002797480150000066
is a set value of the air conditioner,
Figure GDA0002797480150000067
is a temperature interval of the air conditioner,
Figure GDA0002797480150000068
upper limit value of comfortable room temperature, TWHThe water temperature of the water heater is controlled,
Figure GDA0002797480150000069
is the set value of the water temperature of the water heater,
Figure GDA00027974801500000610
is the water temperature interval of the water heater,
Figure GDA00027974801500000611
lower limit of comfortable water temperature, NEV(t) is the connection state of the electric automobile at the time t (1 indicates that the electric automobile is connected with the charging pile, 0 indicates that the electric automobile is not connected with the charging pile), and the SOC (t +1) is the electric automobileState of charge, P, at time t +1 of the vehicleEVRated power (kW) of the electric vehicle, T is end of charge time of the electric vehicle, QEVThe total battery capacity of the electric vehicle, eta is the charging efficiency, SOCminThe minimum state of charge required by the electric automobile at the end of charging is obtained.
Calculating the intelligent demand response potential of a single load aggregator according to the demand response state of each intelligent household appliance, wherein the intelligent demand response potential is as follows:
Figure GDA0002797480150000071
wherein, DRPtotal(t +1) is the intelligent demand response potential of a single load aggregator at time t +1, N1The number of the air conditioners is equal to that of the air conditioners,
Figure GDA0002797480150000072
rated power (kW) for the ith air conditioner,
Figure GDA0002797480150000073
for the demand response potential state of the ith air conditioner at time t, N2The number of the water heaters is the same as the number of the water heaters,
Figure GDA0002797480150000074
rated power (kW) for the jth water heater,
Figure GDA0002797480150000075
for the demand response potential status of the jth water heater at time t, N3The number of the electric automobiles is the same as the number of the electric automobiles,
Figure GDA0002797480150000076
rated power (kW) for the kth electric vehicle,
Figure GDA0002797480150000077
the demand response potential state of the kth electric vehicle at the moment t.
Fig. 3 shows a simulation circuit diagram of an implementation example, and the system comprises 34 transmission lines and 17 node loads, wherein a wind power plant is located at the node 20. The daily curve of the load is shown in fig. 4, the parameters of the node load are shown in table 1, and the parameters of the intelligent household appliance are shown in table 2.
TABLE 1
Figure GDA0002797480150000078
TABLE 2
Figure GDA0002797480150000079
Fig. 5 shows the curves of the actual output, the predicted output and the prediction error of the wind power, and the wind power installation capacity at this time is 530 MW. To illustrate the deficiency of real-time scheduling with generator AGC alone in high wind prediction error conditions, fig. 6 compares the imbalance power of the system with the generator output variation. FIG. 7 shows the ability of the system to mitigate the effects of wind power prediction errors after adding a demand response. Fig. 8 illustrates the difference between demand response and generator contribution with and without the ramp rate constraint. Table 2 lists the power on each line with and without the current line constraints, and it can be seen that the power on lines 14-16 is 405.49MW, exceeding the power limit of 400MW, if the line constraints are not taken into account.
TABLE 3
Line Power quota (MW) Line power (MW) without consideration of line constraints Line power (MW) with line constraints taken into account
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
The abandoned air quantity and the cutting load quantity are increased along with the increase of the wind power proportion, and are reduced after the demand response is added, and are further reduced along with the increase of the demand response proportion. The abandoned wind quantity and the load shedding quantity under different wind power ratios and demand response ratios are shown in the table 4. Fig. 9 is a relation curve of the abandoned air quantity with the change of the wind power proportion and the demand response proportion, and fig. 10 is a relation curve of the cut load quantity with the change of the wind power proportion and the demand response proportion.
TABLE 4
Figure GDA0002797480150000091

Claims (4)

1. An AGC real-time scheduling method based on demand response is characterized in that: the method specifically comprises the following steps:
(1) the local controller obtains the wind power field and the load predicted output, and planning and scheduling the output of the generator by adopting a direct current optimization power flow algorithm;
(2) the local controller transmits the prediction error of the output of the wind power plant to the copy controller of the generator, and the wind power plant, the generator and the copy controller of the load carry out information interaction with the local controller;
(3) establishing a demand response and AGC scheduling mathematical model according to a prediction error of the output of the wind power plant, positive and negative spare capacity of a generator, and real-time output and demand response potential of a load, and outputting a result;
the specific calculation process of the mathematical model is as follows:
(3.1) calculating the load fluctuation amount and the demand response potential of the intelligent household appliance;
(3.2) considering various constraints, optimizing the output and the demand response quantity of the generator, judging whether the convergence is carried out, continuing the step (3.3) if the convergence is not carried out, and continuing the step (3.4) if the convergence is carried out;
(3.3) when the system power fluctuates negatively, increasing the comfort interval of the user to the responsive load; when the comfort level range which can be accepted by a user is exceeded, a load shedding measure is adopted; when the system power is in positive fluctuation, adopting a wind abandoning measure; then returning to the step (3.2);
and (3.4) outputting an optimization result and updating the running state of the intelligent household appliance.
2. The real-time AGC scheduling method based on demand response as claimed in claim 1, wherein: the mathematical model in the step (3) takes maximum wind power consumption as an objective function, and the multiple constraints comprise active power balance constraint, unit capacity constraint, unit climbing and descending rate constraint, tide line constraint, demand response capacity constraint and load comfort constraint.
3. The method for real-time scheduling AGC based on demand response according to claim 2, wherein: the demand response potential calculation method in the demand response capacity constraint comprises the following steps:
when demand response is a load increase:
DPAC(t+1)＝0
Figure FDA0002797480140000011
Figure FDA0002797480140000012
when the demand response is a load reduction:
Figure FDA0002797480140000013
Figure FDA0002797480140000021
Figure FDA0002797480140000022
wherein DPAC(t+1)、DPWH(t +1) and DPEV(T +1) the demand response potential states of the air conditioner, the water heater and the electric automobile at the time T +1 respectively, wherein '1' represents that the household appliance has demand response potential, and '0' represents that the household appliance does not have demand response potential, T is a time period, and T isACIt is the temperature in the room that is,
Figure FDA0002797480140000023
is a set value of the air conditioner,
Figure FDA0002797480140000024
is a temperature interval of the air conditioner,
Figure FDA0002797480140000025
upper limit value of comfortable room temperature, TWHHot waterThe temperature of the water in the water heater,
Figure FDA0002797480140000026
is the set value of the water temperature of the water heater,
Figure FDA0002797480140000027
is the water temperature interval of the water heater,
Figure FDA0002797480140000028
lower limit of comfortable water temperature, NEV(t) is the connection state of the electric automobile at the time t, 1 represents that the electric automobile is connected with the charging pile, 0 represents that the electric automobile is not connected with the charging pile, SOC (t +1) is the charge state of the electric automobile at the time t +1, and PEVRated power of the electric automobile, unit is kW, T is the charging ending time of the electric automobile, and QEVThe total battery capacity of the electric vehicle, eta is the charging efficiency, SOCminThe minimum state of charge required by the electric automobile at the end of charging is obtained.
4. The method of claim 3, wherein the AGC real-time scheduling method based on demand response comprises: the intelligent demand response potential of a single load aggregator is calculated as:
Figure FDA0002797480140000029
wherein, DRPtotal(t +1) is the intelligent demand response potential of a single load aggregator at time t +1, N1The number of the air conditioners is equal to that of the air conditioners,
Figure FDA00027974801400000210
is the rated power of the ith air conditioner, and has the unit of kW,
Figure FDA00027974801400000211
for the demand response potential state of the ith air conditioner at time t, N2The number of the water heaters is the same as the number of the water heaters,
Figure FDA00027974801400000212
is the rated power of the jth water heater, and has the unit of kW,
Figure FDA00027974801400000213
for the demand response potential status of the jth water heater at time t, N3The number of the electric automobiles is the same as the number of the electric automobiles,
Figure FDA00027974801400000214
is the rated power of the kth electric automobile, and has the unit of kW,
Figure FDA00027974801400000215
the demand response potential state of the kth electric vehicle at the moment t.
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