CN107069773B - Load smooth control method based on demand side resource unified state model - Google Patents

Load smooth control method based on demand side resource unified state model Download PDF

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CN107069773B
CN107069773B CN201710116150.2A CN201710116150A CN107069773B CN 107069773 B CN107069773 B CN 107069773B CN 201710116150 A CN201710116150 A CN 201710116150A CN 107069773 B CN107069773 B CN 107069773B
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穆云飞
王明深
贾宏杰
张亚朋
余晓丹
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Tianjin 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
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Abstract

The invention discloses a load smooth control method based on a demand side resource uniform state model. The proposed unified state model can describe the response characteristics of different types of demand side resources by using a unified mathematical expression; deducing a control matrix to realize real-time management and control on the output power of the resource at the demand side on the basis of considering the response sequence of the resource at the demand side; on the basis of considering the user energy comfort level, the proposed control strategy can realize the full absorption and absorption of renewable energy sources, ensure that the SOC of the electric automobile before going out meets the user requirements, and ensure that the indoor temperature of the temperature control load meets the user comfort level requirements; the control of the invention can obviously reduce the power fluctuation of the load, and the power fluctuation rate of the load is kept below a preset value of 10 percent.

Description

Load smooth control method based on demand side resource unified state model
Technical Field
The invention relates to a power transmission network planning method, in particular to a load smooth control method based on a demand side resource unified state model.
Background
In recent years, renewable energy sources such as wind energy and solar energy have attracted more and more attention worldwide by virtue of their renewable green color and environmental protection [1 ]. However, renewable energy Distributed Generation (DG) is characterized by random intermittency, thus introducing a great deal of uncertainty in the operation of the distribution grid. Causing the fluctuation of the load power of the power distribution network, and being worthy of attention, the serious change [2] of the load power of the power distribution network can obviously affect the stable operation of the power distribution network to generate profound influence [2] so as to limit the utilization, absorption and absorption of renewable energy sources [3 ].
The traditional generator is a common means for stabilizing the fluctuation of the load power of the power distribution network, and one of the traditional control strategies for smoothing the load curve of the power distribution network is to plan the traditional generator. However, the response speed of such a conventional generator is not enough to follow the output power variation of the DG distributed grid, and causes problems in that the generator is poor in operation economy and the generation efficiency is lowered [4 ]. Another approach to address this problem is to respond to load power fluctuations in the distribution grid by using an Energy Storage System (ESS), such as batteries, flywheel, etc. [5] [6] [5,6 ]. (ii) a With the energy storage system, the energy storage system ESS can be used to improve the reduction of the influence [7] [8] on the distribution of the distribution network voltage caused by the stochastic output of the distributed power sources in the distribution network. Reduction documents 7 and 8 the ESS was studied in order to reduce peak load [9] [10] peak loads in the distribution grid. (ii) a By dynamically adjusting the charging power output, the ESS energy storage system can effectively cope with and stabilize the power fluctuation [11], [12] of the power distribution network with the DG caused by the distributed power supply; . However, the adoption of large-scale ESS application energy storage systems would be uneconomical and impractical to significantly reduce the economics of renewable energy access.
With the rapid development of smart grids, there is also a growing interest in the flexibility of demand response becoming an important means of assisting the operation of power distribution networks. . The ability of different types of demand side resources to have a large total response potential is considerable [13, ] [14 ]. Taking the distributed power supply [15], the electric vehicle [16] and the temperature control load [17] as examples, under an effective control means, the resources can be proved to be effective demand side resources such as the distributed power supply [15], the Electric Vehicle (EV) [16] and the constant Temperature Control Load (TCL) [17] which demand response effective resources, and can serve as an ESS in a power distribution network, thereby assisting the safe and stable operation of the power grid. Thus, some demand side resources can provide various types of assistance to the power distribution grid. Considering the response capability of the electric automobile and the thermostatic control temperature control load, the resources can only slow down the influence of the power fluctuation of the renewable energy source on the voltage of the power distribution network; by controlling the charging and discharging process of the electric automobile, the electric automobile can effectively reduce the voltage fluctuation of the power distribution network with the distributed power supply and simultaneously coordinate the charging load of the electric automobile in real time on the basis of minimizing the power loss of the power distribution network [18 ] [19 ]. Electric vehicles rely on their rapid responsiveness to smooth load curves and load distribution that can be remodeled as an energy storage device and reduce load peaks to alleviate peak loads [20, ] [21 ]. (ii) a Temperature-controlled load thermostatically controlled loads, such as heat pumps, represented by heat pumps, can mitigate power fluctuations in a power distribution network with distributed power sources [22, ] [23] that stabilize power fluctuations caused by renewable energy sources in the power distribution network.
[ reference documents ]
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[15]Li N(2012)An evaluation of the HVAC load potential for providingload balancing service.IEEE Trans Smart Grid,3(3):1263-1270。
[16]Long C,E.A.Farrag M,Zhou C,et al(2013)Statistical quantificationof voltage violations in distribution networks penetrated by small windturbines and battery electric vehicles.IEEE Trans Power Syst,28(3):2403-2411。
[17]Wang D,Parkinson S,Miao W,et al(2012)Online voltage securityassessment considering comfort-constrained demand response control ofdistributed heat pump systems.Appl Energy,96:104-114。
[18]Luo X,Chan KW(2013)Real-time scheduling of electric vehiclescharging in low-voltage residential distribution systems to minimise powerlosses and improve voltage profile.IET Gener Transm Distrib,8(3):516-529。
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[21]H.Tehrani N,Wang P(2015)Probabilistic estimation of plug-inelectric vehicles charging load profile.Electr Power Syst Res,124:133-143。
[22]Mammoli A,Barsun H,Burnett R,et al(2012)Using high-speed demandresponse of building HVAC systems to smooth cloud-driven intermittency ofdistributed solar photovoltaic generation.Proc IEEE Power Eng Soc TransDistrib Conf,Orlando,FL,USA。
[23]Wang D,Ge S,Jia H,et al(2014)A demand response and batterystorage coordination algorithm for providing microgrid tie-line smoothingservices.IEEE Trans Sustain Energy,5(2):476-486。
[24]R.S.Rao,K.Ravindra,K.Satish,et al(2015)Power loss minimization indistribution system using network reconfiguration in the presence ofdistributed generation.IEEE Trans Power Syst,28(1):317-325。
[25]Yao E,Samadi P,W.S.Wong V,et al(2016)Residential demand sidemanagement under high penetration of rooftop photovoltaic units.IEEE TransSmart Grid,7(3):1597-1608。
[26]United Kingdom generic distribution system(UKGDS)(2006)Typicalload patterns,DG output data.Distributed Generation and SustainableElectrical Energy Centre,University of Strathclyde,Glasgow,2007。
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[28]Wang M,Mu Y,Jia H,et al(2015)A preventive control strategy forstatic voltage stability based on an efficient power plant model of electricvehicles.J Mod Power Syst Clean Energy,3(1):103-113。
[29]Yu T,Yao X,Wang M,et al(2016)A reactive power evaluation modelfor EV chargers considering travelling behaviors.Proc IEEE DRPT,Changsha,China。
[30]Wang D,Fan M,Jia H(2014)User comfort constraint demand responsefor residential thermostatically-controlled loads and efficient power plantmodeling.Proc of CSEE,34(13):2071-2077。
Disclosure of Invention
The existing current research results mainly aim at the participation of a specific type of demand side resource in system response, and documents make good contribution to the improvement of the power quality of a power distribution network of a single type of demand side resource. However, each type of demand-side resource has its own response characteristics and capability points. Therefore, it is of great importance to establish a unified mathematical model to describe the characteristics of different types of demand side resources and improve the response capability of the demand side resources, and the model can effectively mine the response capability of the demand side resource cluster. Meanwhile, the model can consider the response sequence of the demand side resource, in this way, all available power output control which is beneficial to realizing the demand side resource can be accurately controlled, and the response sequence is also considered when the unified model is established.
In order to solve the technical problem, the invention provides a load smooth control method based on a uniform state model of demand side resources, which comprises the following steps:
step one, establishing a uniform state model of demand side resources:
dividing the time of one day into M time intervals by taking a distributed power supply, an electric automobile and a temperature control load as demand side resources, wherein each interval time is delta t, namely M multiplied by delta t is 24 h;
the superscript i is used to refer to the resource type, and the distributed power supply, the electric vehicle and the temperature control load are represented by G, V and L (i belongs to { G, V, L }), respectively; the subscript j is used for indicating the number of a specific demand side resource in the distributed power supply G, the electric automobile V and the temperature control load L;
1-1) establishing a distributed power state model as follows:
upper limit of distributed power j output power
Figure BDA0001235402420000041
And
Figure BDA0001235402420000042
the lower limits are as follows:
Figure BDA0001235402420000043
in the formula (1), the reaction mixture is,
Figure BDA0001235402420000044
the maximum output power provided by the distributed power supply j in a real-time state at the moment t;
the state model of the distributed power supply is as follows:
Figure BDA0001235402420000045
in the formula (2), the reaction mixture is,
Figure BDA0001235402420000051
the accumulated quantity of the output electric energy of the distributed power supply j in a real-time state;
Figure BDA0001235402420000052
the output power of the distributed power supply j in a real-time state is within the upper limit and the lower limit respectively
Figure BDA0001235402420000053
And
Figure BDA00012354024200000525
is distributedThe accumulated amount of electric energy generated by the power supply j at rated output power is calculated according to the formula (3):
Figure BDA0001235402420000056
in the formula (3), the reaction mixture is,
Figure BDA0001235402420000057
is the rated output power in real time;
1-2) establishing an electric automobile state model, comprising the following steps:
lower limit of j power output of electric automobile
Figure BDA0001235402420000058
And lower limit
Figure BDA0001235402420000059
The following were used:
Figure BDA00012354024200000510
in the formula (4), the reaction mixture is,
Figure BDA00012354024200000511
in order to start the charging time,
Figure BDA00012354024200000512
in order to start the time of the trip,
Figure BDA00012354024200000513
and
Figure BDA00012354024200000514
rated charge and discharge powers, respectively;
Figure BDA00012354024200000515
in order to be the maximum output power,
Figure BDA00012354024200000516
is a positive value;
Figure BDA00012354024200000517
in order to achieve the minimum output power,
Figure BDA00012354024200000518
is a negative value;
normalized SOC value of electric vehicle j
Figure BDA00012354024200000519
The following were used:
Figure BDA00012354024200000520
in the formula (5), the reaction mixture is,
Figure BDA00012354024200000521
the real-time SOC value of the electric vehicle j is obtained;
Figure BDA00012354024200000522
the electric vehicle j is charged at the rated power up to the upper limit of the SOC,
Figure BDA00012354024200000523
discharging the electric vehicle j at rated power until reaching the lower limit of the SOC; when the electric automobile is charged, the SOC value rises to the SOC value required by the user
Figure BDA00012354024200000524
If so, stopping charging;
when the electric automobile is connected with the power distribution network, the j state model of the electric automobile is as follows:
Figure BDA0001235402420000061
in the formula (6), the reaction mixture is,
Figure BDA0001235402420000062
for real-time power output of electric vehicle j
Figure BDA0001235402420000063
For the corrected j battery capacity of the electric vehicle,
Figure BDA0001235402420000064
comprises the following steps:
Figure BDA0001235402420000065
in the formula (7), the reaction mixture is,
Figure BDA0001235402420000066
the actual battery capacity of the electric automobile;
Figure BDA0001235402420000067
and
Figure BDA0001235402420000068
respectively the charging efficiency and the discharging efficiency of the electric vehicle,
Figure BDA0001235402420000069
is the output power;
1-3) establishing a temperature control load state model
Upper limit of output power of temperature controlled load j
Figure BDA00012354024200000610
And lower limit
Figure BDA00012354024200000611
The following were used:
Figure BDA00012354024200000612
in the formula (8), the reaction mixture is,
Figure BDA00012354024200000613
rated power consumption;
Figure BDA00012354024200000614
in order to obtain a lower limit of the output power,
Figure BDA00012354024200000615
is a negative value;
Figure BDA00012354024200000616
in order to achieve the upper limit of the output power,
Figure BDA00012354024200000617
the value is 0;
normalized indoor temperature of temperature controlled load j
Figure BDA00012354024200000618
And outdoor temperature
Figure BDA00012354024200000619
Is represented as follows:
Figure BDA00012354024200000620
the state model of the temperature-controlled load is as follows:
Figure BDA0001235402420000071
in the formulae (9) and (10),
Figure BDA0001235402420000072
is the temperature of the room in question,
Figure BDA0001235402420000073
and
Figure BDA0001235402420000074
respectively the upper and lower limits of the temperature control threshold,
Figure BDA0001235402420000075
is the temperature of the outside of the room,
Figure BDA0001235402420000076
and
Figure BDA0001235402420000077
upper and lower limits of the indoor temperature, respectively; in that
Figure BDA0001235402420000078
During the time period, the temperature control load is in an on state and the indoor temperature rises
Figure BDA0001235402420000079
In the time period, the heat source equipment is in a turn-off state, and the indoor temperature is reduced; for temperature-controlled loads in the on state, the temperature is
Figure BDA00012354024200000710
Range-off, temperature in the case of temperature-controlled loads in the off state
Figure BDA00012354024200000711
Opening when the range is reached;
Figure BDA00012354024200000712
is normalized outdoor temperature, ajIs equal to
Figure BDA00012354024200000713
Wherein R isjAnd CjRespectively a thermal resistor and a capacitor;
Figure BDA00012354024200000714
to output power, when the temperature controlled load is in an on state,
Figure BDA00012354024200000715
the temperature controlled load in the off state,
Figure BDA00012354024200000716
1-4) establishing a unified state model, including
The numbers of the distributed power supply, the electric automobile and the temperature control load are respectively NG、NV、NLAnd satisfy NG+NV+NL＝N；
According to the distributed power supply state model, the electric vehicle state model and the temperature control load state model respectively expressed by the above equations (2), (6) and (10), the demand side resource state model is expressed by equation (11):
Figure BDA00012354024200000717
wherein:
Figure BDA00012354024200000718
Figure BDA00012354024200000719
Figure BDA00012354024200000720
Figure BDA0001235402420000081
Figure BDA0001235402420000082
Figure BDA0001235402420000083
on the basis of equation (11), the demand-side resource unified state model is shown as equation (18):
x(t+Δt)＝x(t)+P(t)δ(t) (18)
in equation (18), the column vector x (t) is the real-time status of the demand-side resource, and the element satisfies
Figure BDA0001235402420000084
The diagonal matrix p (t) is the real-time output power matrix of the demand-side resource,diagonal element satisfies
Figure BDA0001235402420000085
The column vector δ (t) is defined as the modified time interval;
step two, smooth control of a load curve:
and evaluating the load fluctuation condition of the power distribution network by using the power fluctuation rate, wherein the load fluctuation condition is expressed by formulas (19) and (20):
Figure BDA0001235402420000086
Figure BDA0001235402420000087
in the formulae (19) and (20), the function fTUsed for calculating the power fluctuation rate of the load in the time period T; function(s)
Figure BDA0001235402420000088
And
Figure BDA0001235402420000089
used for calculating the maximum value and the minimum value of the load in the time period T;
Figure BDA00012354024200000810
is the rated value of the load;
Figure BDA00012354024200000811
and
Figure BDA00012354024200000812
load maximum and minimum;
Figure BDA00012354024200000813
a real-time load value;
the method for realizing the smooth control of the load curve comprises the following steps:
the first step is as follows: determining a target power for load smoothing
By rtRepresenting the real-time power fluctuation rate, e.g.Formula (21):
Figure BDA0001235402420000091
then, a target power value for load smoothing is determined
Figure BDA0001235402420000092
(i) When in use
Figure BDA0001235402420000093
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000094
(ii) when in use
Figure BDA0001235402420000095
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000096
(iii) when in use
Figure BDA0001235402420000097
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000098
in equations (22), (23) and (24), the upper limit of the load real-time power fluctuation rate
Figure BDA0001235402420000099
And lower limit
Figure BDA00012354024200000910
As shown in equation (25):
Figure BDA00012354024200000911
in the formula (25), the reaction mixture,
Figure BDA00012354024200000912
is the power fluctuation ratio rTThe limit of (2);
Figure BDA00012354024200000913
the target variation power for load smoothing is shown as equation (26);
Figure BDA00012354024200000914
the second step is that: determining responsiveness of different demand side resources
To implement a load curve smoothing strategy based on a unified state model, the matrix P (t) is decomposed into the product of two matrices
Figure BDA00012354024200000915
I.e. real time output power
Figure BDA00012354024200000916
By
Figure BDA00012354024200000917
Instead of, i.e. using
Figure BDA00012354024200000918
In the formula (27), the reaction mixture is,
Figure BDA00012354024200000919
are diagonal matrix, diagonal elements
Figure BDA00012354024200000920
The upper limit of the output power of the demand side resource j; the diagonal matrix B is an output power control matrix, and diagonal elements
Figure BDA00012354024200000921
A control variable used to increase or decrease the output power of the demand-side resource j;
the formula (18) is rewritten as follows:
Figure BDA0001235402420000101
the ability to increase output power is:
Figure BDA0001235402420000102
the ability to reduce output power is:
Figure BDA0001235402420000103
in the formulas (29) and (30), matrix
Figure BDA0001235402420000104
Is the maximum value of the controllable variable, the matrixBIs the minimum value of the controllable variable; l is0Is an N × 1 dimensional matrix with elements all 1; pup(t) is a matrix of dimension N × 1, the non-negative elements of the mth row indicating the ability of the mth resource to increase output power; pdn(t) is an N × 1 dimensional matrix, and the non-positive value element in the mth row represents the capability of the mth resource to reduce the output power;
define lower triangular matrix
Figure BDA0001235402420000105
And lower triangular arrayB *Respectively evaluating the capacity of increasing output power and the capacity of reducing output power of the resource at the demand side, and setting a lower triangular array
Figure BDA0001235402420000106
And lower triangular arrayB *The element in (1) is shown as formula (31):
Figure BDA0001235402420000107
the following are written over equations (29) and (30):
Figure BDA0001235402420000108
in the formula (32), Pup*(t) is an Nx 1 dimensional matrix, and the non-negative value element of the mth row represents the capability of increasing the output power of 1-m resources; pdu*(t) is also an N × 1 dimensional matrix, the non-positive value elements of the m-th row represent the ability of 1-m resources to reduce output power;
the third step: determining an actual control matrix B*
(i) When in use
Figure BDA00012354024200001011
When, set j1To satisfy
Figure BDA00012354024200001012
The maximum subscript of (a);
Figure BDA00012354024200001013
(ii) when in use
Figure BDA00012354024200001014
When the temperature of the water is higher than the set temperature,
B*＝B (34)
(iii) when in use
Figure BDA00012354024200001015
When, set j2To satisfy
Figure BDA00012354024200001016
The maximum subscript of (a) is,
Figure BDA0001235402420000111
the output power of the demand-side resource is represented by equation (36):
Figure BDA0001235402420000112
the updated state model of the demand-side resource is represented by equation (37):
Figure BDA0001235402420000113
compared with the prior art, the invention has the beneficial effects that:
(1) by utilizing a load curve smooth control strategy, the power fluctuation of the load is obviously reduced, and the power fluctuation rate is kept below a preset value of 10 percent;
(2) the proposed unified state model can describe the response characteristics of different types of demand side resources by using a unified mathematical expression;
(3) deducing a control matrix to realize real-time management and control on the output power of the resource at the demand side on the basis of considering the response sequence of the resource at the demand side;
(4) on the basis of considering the user energy comfort level, the provided control strategy can realize the sufficient absorption of renewable energy, ensure that the SOC before the electric automobile goes out meets the user requirement, and ensure that the indoor temperature at which the temperature control load is positioned meets the comfort level requirement of the user.
Drawings
FIG. 1 is a single distributed power operating area;
FIG. 2 is a single electric vehicle operating area;
FIG. 3 is a single temperature controlled load operating zone;
FIG. 4 is a response sequence for an increase in output power;
FIG. 5 is a response sequence for a decrease in output power;
FIG. 6 is a load power without consideration of a load curve smoothing control strategy;
FIG. 7 is a load power for a smooth control strategy taking into account the load curve;
FIG. 8 is a graph of power fluctuation rate of a load under no control and control;
FIG. 9 is the output power of a distributed power supply under no control and control;
FIG. 10 is an output power of an electric vehicle under temperature control and control;
FIG. 11 is the output power of a temperature controlled load without control and under control;
FIG. 12(a) and FIG. 12(b) are SOC of the electric vehicle without control and under control, respectively;
fig. 13(a) and 13(b) are the indoor temperatures at which the temperature-controlled load is placed under no control and under control, respectively.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
The invention provides a load smooth control method based on a uniform state model of demand side resources, which comprises the following steps:
step one, establishing a uniform state model of the demand side resource. The present invention is directed mainly to demand-side resources represented by distributed power supplies, electric vehicles, and temperature-controlled loads. The time of day is divided into M time intervals, each interval having a time Δ t, i.e., M × Δ t is 24. In the invention, a superscript i is used for indicating a resource type, and a distributed power supply, an electric automobile and a temperature control load are respectively represented by G, V and L (i belongs to { G, V, L }); the subscript j is used to indicate the number of a particular demand side resource in G, V, L. The method comprises the following steps:
1-3) establishing a distributed power state model as follows:
the single distributed power source operation area is shown in fig. 1.
The upper and lower limits of the output power of the distributed power supply j are shown in the formula (1).
Figure BDA0001235402420000121
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000122
is the maximum output power provided by the distributed power source j at time t.
The state model of the distributed power supply is shown as equation (2).
Figure BDA0001235402420000123
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000124
outputting the accumulated amount of the electric energy for the distributed power supply j;
Figure BDA0001235402420000125
is the output power of the distributed power supply j, and the upper and lower limit ranges are respectively
Figure BDA0001235402420000126
And
Figure BDA0001235402420000127
is the accumulated amount of electric energy generated by the distributed power source j at the rated output power, and can be calculated according to equation (3).
Figure BDA0001235402420000128
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000129
is the rated output power.
To establish a state model of a distributed power supply, a control center needs to acquire a real-time state
Figure BDA00012354024200001210
Power output
Figure BDA00012354024200001211
Maximum power output
Figure BDA00012354024200001212
And rated output power
Figure BDA00012354024200001213
The information of (1).
1-4) establishing an electric automobile state model, which comprises the following steps:
when the electric automobile goes out, the electric automobile has no influence on a power distribution network; when the power distribution network is accessed, the electric automobile can realize bidirectional power flow control with the power distribution network, and the working area of a single electric automobile is shown in fig. 2.
Upper and lower limits of j power output of electric automobile
Figure BDA0001235402420000131
And
Figure BDA0001235402420000132
as shown in equation (4), wherein the initial charging time is
Figure BDA0001235402420000133
The time of starting trip is
Figure BDA0001235402420000134
Figure BDA0001235402420000135
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000136
and
Figure BDA0001235402420000137
rated charge-discharge power;
Figure BDA0001235402420000138
is the maximum output power, is a positive value;
Figure BDA0001235402420000139
is the minimum output power and is negative.
For the electric automobile access distribution network, the electric automobile operation area (shaded part in fig. 2) is limited by the output power and the SOC state. Point A, B, C, D, E is used to determine the boundary of the operating region, whose upper boundary is defined by A-B-C, and electric vehicle j is charged at rated power from A to B until SOC reaches its upper limit
Figure BDA00012354024200001310
Under itThe boundary is determined by A-D-E-F, and the electric vehicle j is discharged at rated power from A to D until the SOC reaches the lower limit
Figure BDA00012354024200001311
In order to ensure that the electric automobile can meet the SOC requirement of a user before going out
Figure BDA00012354024200001312
The electric vehicle j is forcibly charged at the rated power from E to F.
Figure BDA00012354024200001313
Is the normalized SOC value of the electric vehicle EVj, as shown in equation (5). When the electric automobile is connected with the power distribution network, the state model of the electric automobile is shown in the formula (6).
Figure BDA00012354024200001314
In the formula (I), the compound is shown in the specification,
Figure BDA00012354024200001315
the real-time SOC value of the electric vehicle j is obtained.
Figure BDA00012354024200001316
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000141
for real-time power output of electric vehicle j
Figure BDA0001235402420000142
The corrected battery capacity of the electric vehicle is shown as the formula (7).
Figure BDA0001235402420000143
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000144
the actual capacity of the battery of the electric automobile;
Figure BDA0001235402420000145
and
Figure BDA0001235402420000146
respectively charge and discharge efficiency.
To build a state model of an electric vehicle, the Aggregator needs to obtain real-time states
Figure BDA0001235402420000147
Output power
Figure BDA0001235402420000148
Rated charge and discharge power
Figure BDA0001235402420000149
And
Figure BDA00012354024200001410
battery capacity of electric vehicle
Figure BDA00012354024200001411
Charge and discharge efficiency
Figure BDA00012354024200001412
And
Figure BDA00012354024200001413
travel time
Figure BDA00012354024200001414
SOC value of user's demand
Figure BDA00012354024200001415
Upper and lower bounds of SOC
Figure BDA00012354024200001416
And
Figure BDA00012354024200001417
the information of (1).
1-3) establishing a temperature control load state model
The temperature-controlled load has good heat storage characteristics, and taking the heat pump to increase the indoor temperature as an example, the operation area of the single temperature-controlled load is shown in fig. 3.
Upper and lower limits of output power of temperature controlled load j ((
Figure BDA00012354024200001418
And
Figure BDA00012354024200001419
) As shown in formula (8).
Figure BDA00012354024200001420
In the formula (I), the compound is shown in the specification,
Figure BDA00012354024200001421
rated power consumption;
Figure BDA00012354024200001422
is the lower limit of the output power and is a negative value;
Figure BDA00012354024200001423
the upper limit of the output power is 0.
As shown in figure 3 of the drawings,
Figure BDA00012354024200001424
and
Figure BDA00012354024200001425
respectively, the upper and lower limits of the indoor temperature. In that
Figure BDA00012354024200001426
During the time period, the heat pump is in the 'on' state and the indoor temperature increases. In that
Figure BDA00012354024200001427
During the time period, the heat pump is in the 'off' stateAnd the indoor temperature is lowered. To reduce the number of switching operations, the temperature is in the 'on' state for temperature controlled loads
Figure BDA00012354024200001428
The range may be turned off. For a heat pump in the 'off' state, the temperature is
Figure BDA00012354024200001429
The range may be on.
To normalize indoor and outdoor temperatures
Figure BDA0001235402420000151
And
Figure BDA0001235402420000152
) Indoor and outdoor temperature after normalization of temperature control load j
Figure BDA0001235402420000153
And
Figure BDA0001235402420000154
as shown in formula (9). The state model of the temperature-controlled load is shown in equation (10).
Figure BDA0001235402420000155
Figure BDA0001235402420000156
In the formula (I), the compound is shown in the specification,
Figure BDA0001235402420000157
is the normalized outdoor temperature; a isjIs equal to
Figure BDA0001235402420000158
Wherein R isjAnd CjRespectively a thermal resistor and a capacitor;
Figure BDA0001235402420000159
for output power, when in the 'on' state is
Figure BDA00012354024200001510
In the 'off' state is
Figure BDA00012354024200001511
To establish a state model of the temperature controlled load, the control center needs to obtain real-time state
Figure BDA00012354024200001512
Output power
Figure BDA00012354024200001513
Rated power consumption
Figure BDA00012354024200001514
Thermal resistor and capacitor RjAnd CjUpper and lower limits of indoor temperature
Figure BDA00012354024200001515
And
Figure BDA00012354024200001516
upper and lower temperature control threshold limits
Figure BDA00012354024200001517
And
Figure BDA00012354024200001518
and outdoor temperature
Figure BDA00012354024200001519
And so on.
1-4) establishing a unified state model, including
The numbers of the distributed power supply, the electric automobile and the temperature control load are respectively NG、NV、NLAnd satisfy NG+NV+NLN. According to the state model equations (3), (6) and (10) of the three resources, after different unificationsThe demand-side resource status model of (2) is shown in equation (11), wherein the parameter meanings are shown in equations (12) - (17).
Figure BDA00012354024200001520
Figure BDA00012354024200001521
Figure BDA0001235402420000161
Figure BDA0001235402420000162
Figure BDA0001235402420000163
Figure BDA0001235402420000164
Figure BDA0001235402420000165
Based on the state model given by equation (11), the demand-side resource uniform state model is shown as (18).
x(t+Δt)＝x(t)+P(t)δ(t) (18)
Wherein, the column vector x (t) is the real-time status of the demand-side resource, and the element satisfies
Figure BDA0001235402420000166
The diagonal matrix P (t) is a real-time output power matrix of the resource at the demand side, and the diagonal elements meet
Figure BDA0001235402420000167
The column vector δ (t) is defined as the modified time interval.
And step two, applying a load curve smoothing control strategy.
And evaluating the load fluctuation condition of the power distribution network by using the power fluctuation rate, as shown in formulas (19) and (20).
Figure BDA0001235402420000168
Figure BDA0001235402420000169
In the formula, function fTUsed for calculating the power fluctuation rate of the load in the time period T; function(s)
Figure BDA00012354024200001610
And
Figure BDA00012354024200001611
used for calculating the maximum value and the minimum value of the load in the time period T;
Figure BDA00012354024200001612
is the rated value of the load;
Figure BDA00012354024200001613
and
Figure BDA00012354024200001614
load maximum and minimum;
Figure BDA00012354024200001615
is a real-time load value.
On the basis of the unified state model, the load curve smoothing control strategy proposed by the present invention is described next.
The first step is as follows: determining a target power for load smoothing
By rtRepresents the real-time power fluctuation rate, as shown in (21).
Figure BDA0001235402420000171
Then determining the load averageTarget power value of slip
Figure BDA0001235402420000172
(i) When in use
Figure BDA0001235402420000173
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000174
(ii) when in use
Figure BDA0001235402420000175
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000176
(iii) when in use
Figure BDA0001235402420000177
When the temperature of the water is higher than the set temperature,
Figure BDA0001235402420000178
in the formula, the upper and lower limits of the real-time power fluctuation rate of the load
Figure BDA0001235402420000179
And
Figure BDA00012354024200001710
as shown in equation (25).
Figure BDA00012354024200001711
In the formula (I), the compound is shown in the specification,
Figure BDA00012354024200001712
is the power fluctuation ratio rTThe limit of (2).
Therefore, the target change power for load smoothing is
Figure BDA00012354024200001713
As shown in equation (26).
Figure BDA00012354024200001714
The second step is that: determining responsiveness of different demand side resources
To implement a load curve smoothing strategy based on a unified state model, the matrix P (t) is decomposed into the product of two matrices
Figure BDA00012354024200001715
I.e. real time output power
Figure BDA00012354024200001716
By
Figure BDA00012354024200001717
Instead of, i.e. using
Figure BDA00012354024200001718
In the formula (I), the compound is shown in the specification,
Figure BDA00012354024200001719
are diagonal matrix, diagonal elements
Figure BDA00012354024200001720
The upper limit of the output power of the demand side resource j; the diagonal matrix B is an output power control matrix, and diagonal elements
Figure BDA00012354024200001721
Is a control variable used to increase or decrease the output power of the demand-side resource j.
The modified unified state model is then shown in equation (28).
Figure BDA0001235402420000181
The response sequence of the demand-side resource is determined by the arrangement sequence in the unified state model and is continuously updated with the time. For example, exchange the order of responses for the m and n rows of demand-side resources: 1) exchanging elements of the m-th row and the n-th row for column matrices x (t) and δ (t); 2) for diagonal matrices B and
Figure BDA0001235402420000182
the diagonal elements of the m-th and n-th rows are swapped.
As shown in fig. 4, when the output power is increased, the order of response of the demand-side resource is: distributed power (increased output), electric car (load shedding) and temperature controlled load (shutdown device), electric car (discharge), uncontrollable resource (reached upper limit of output power).
As shown in fig. 5, when the output power is reduced, the order of response of the demand-side resource is: electric cars (reduced discharge), electric cars (increased charge) and temperature controlled loads (turned on devices), distributed power supplies (reduced output), uncontrollable resources (reached lower output power limit).
On the basis of the unified state model, the capability of increasing the output power is shown as a formula (29), and the capability of reducing the output power is shown as a formula (30). Pup(t) is a matrix of dimension N × 1, and the non-negative value elements of the mth row indicate the ability of the mth resource to increase the output power. Pdn(t) is an N x 1 dimensional matrix, and the non-positive value elements in the mth row indicate the ability of the mth resource to reduce output power.
Figure BDA0001235402420000183
In the form of matrixThe diagonal element of (a) is the controllable variable maximum; l is0Is an N × 1 dimensional matrix with elements all 1.
Figure BDA0001235402420000185
In the form of matrixBIs the controllable variable minimum.
To further illustrate the responsiveness of demand-side resources, a lower triangular matrix is defined
Figure BDA0001235402420000186
AndB *to evaluate the ability of the demand side resources to increase and decrease output power,
Figure BDA0001235402420000187
andB *the element in (A) is represented by formula (31).
Figure BDA0001235402420000188
After the improvement, the ability to increase and decrease output power is shown as equation (32).
Figure BDA0001235402420000189
In the formula, Pup*(t) is an Nx 1 dimensional matrix, and the non-negative value element of the mth row represents the capability of increasing the output power of 1-m resources; pdn*(t) is also an N x 1 dimensional matrix, with the non-positive value elements in the m-th row indicating the ability of 1-m resources to reduce output power.
The third step: determining an actual control matrix B*
(i) When in use
Figure BDA0001235402420000191
When, set j1To satisfy
Figure BDA0001235402420000192
The maximum subscript of (c).
Figure BDA0001235402420000193
(ii) When in use
Figure BDA0001235402420000194
When the temperature of the water is higher than the set temperature,
B*＝B (34)
(iii) when in use
Figure BDA0001235402420000195
When, set j2To satisfy
Figure BDA0001235402420000196
The maximum subscript of (c).
Figure BDA0001235402420000197
Therefore, the output power of the demand-side resource can be obtained by equation (36), and the updated state model of the demand-side resource is shown by equation (37).
Figure BDA0001235402420000198
Figure BDA0001235402420000199
Example simulation and result analysis
In this embodiment, an IEEE-33 node distribution network is taken as an example [24]The effectiveness of the proposed load curve smoothing strategy based on the demand side resource unified state model is verified. Each load node has 10-40 users [25 ]]And assuming that each home is equipped with a rooftop photovoltaic, the rooftop area is [60,100 ]]And the photovoltaic power density is 100W/m2Data reference UKGDS [26 ] for photovoltaic power generation and user load]。
The number of electric vehicles per user is 1.86 [27], the rated charge-discharge power of the electric vehicle is 7kW [28], and other parameters such as battery capacity, charge-discharge efficiency, charge and go time, user demand SOC, maximum and minimum value of SOC, and the like are cited in reference [29 ].
It is assumed that each user has a heat pump. The rated power consumption of the heat pump is 6kW [30], and other parameters such as outdoor temperature, upper and lower limits of indoor temperature, thermal resistance and capacitance are referred to in reference [17 ].
3-1) load curve smoothing control effect
When the load curve smoothing strategy is not considered, the output powers of the distributed power supply, the electric automobile, the temperature control load, the uncontrollable load and the total load are shown in fig. 6, and the power fluctuation of the total load is mainly caused by the randomness of the output power of the distributed power supply.
Therefore, a load curve smoothing control strategy is required to be adopted to smooth the power fluctuation of the load, in this embodiment, assuming that the limit of the power fluctuation rate per 15 minutes is 10%, the total load, the target power of the total load and the total load after control are as shown in fig. 7, and it can be seen that the power fluctuation condition of the load is obviously reduced. However, in the period from 8:00 to 15:00, the actual load value cannot accurately follow the smoothed load target value, and sometimes the actual load value is lower than the smoothed load target value, which are caused by the response capability of the demand-side resource whose output power increases reaching the limit.
The load power fluctuation rate under uncontrolled and controlled conditions is shown in fig. 8, and the power fluctuation rate of the load is kept below 10% after the control strategy is adopted.
3-2) demand side resource response characteristics
The output power of the uncontrolled and controlled distributed power supply is shown in fig. 9, the controlled distributed power supply output can well track the uncontrolled distributed power supply output, and the main reason is that in the load curve smooth control strategy, the distributed power supply is the first means for increasing the output power and the last means for reducing the output power, so that the absorption and absorption of renewable energy can be effectively promoted.
The output power of the electric automobile under the uncontrolled and controlled conditions is shown in fig. 10, and the fluctuation of the output power of the electric automobile is large in a period from 8:00 to 15:00, and the main reason is that the electric automobile changes the connection state (charging, idling and discharging) with a power distribution network. To increase the output power, the electric vehicle being charged will stop charging or even discharge to the grid, so there is a time when the total output power of the electric vehicle is greater than zero. And the traveling habits of the electric automobiles of residents have certain similarity, so that the charging load of the electric automobiles has a peak value.
Output power of uncontrolled and controlled temperature controlled loads as shown in fig. 11, the controlled temperature controlled load output power can almost follow the uncontrolled output power before 5:00, mainly because the power fluctuation of the load during this time is small. After 5:00, the output power of the temperature-controlled load after control cannot follow the output power under no control, mainly because the temperature-controlled load participates in the control of stabilizing the fluctuation of the load power. The output power of the temperature-controlled load after control is close to 0 in the period from 15:00 to 17:00, mainly because almost all the temperature-controlled loads are in the off state in response to the power fluctuation. However, in the period from 18:00 to 19:00, the output power of the temperature-controlled load after control peaks, mainly because the temperature-controlled load in the off state is gradually turned on due to the continuous drop of the indoor temperature.
To further illustrate the response characteristics of the electric vehicle in the control strategy, the SOC state of the electric vehicle accessing the power grid under no control and under control is shown in fig. 12, while the SOC state in the trip overcharge is not given when the electric vehicle is traveling. In the period from 08:00 to 15:00, most electric automobiles are in a trip state and cannot respond to power fluctuation of loads; in the period from 15:00 to 20:00, most electric automobiles finish traveling and start charging. Under the condition that a load curve smooth control strategy is not considered, the charging process is not influenced, the change process of the SOC state of the electric automobile is obviously different after the control, the line of the rising trend indicates that the electric automobile is being charged, the line of the falling trend indicates that the electric automobile is being discharged, and the line of the horizontal trend indicates that the electric automobile is only connected to a power distribution network and the output power is zero (idle state). In the load curve smoothing control strategy, the electric automobile responds to load fluctuation by changing the access state of the electric automobile
Figure BDA0001235402420000201
To further illustrate the response characteristics of a temperature controlled load in a control strategy, the room temperature at which the temperature controlled load is subjected to both uncontrolled and controlled conditions is shown in FIG. 13. The line of the rising trend indicates that the room temperature rises and the temperature control load is in the on state, and the line of the falling trend indicates that the room temperature falls and the temperature control load is in the off state. As can be seen from FIG. 13(a), the room temperature was varied regularly between 19 ℃ and 23 ℃ without control; as can be seen from fig. 13(b), the indoor temperature change process under control is significantly different. To increase the output power, the temperature controlled load with higher room temperature and in the on state will be preferentially selected to participate in the load smoothing control, while the output power of the temperature controlled load in fig. 11 is close to 0 in the period from 15:00 to 17:00, and as can be seen from fig. 13(b), the reason why the temperature controlled load response capability is limited in this period is further illustrated.
According to the invention, on the basis of a unified state model, a load curve smooth control strategy is utilized, the load power fluctuation of the power distribution network is obviously reduced, and meanwhile, the energy utilization comfort level of a user is ensured in the process of controlling the output power of the resource at the demand side.
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are intended to be illustrative rather than restrictive, and many modifications may be made by those skilled in the art without departing from the spirit of the present invention within the scope of the appended claims.

Claims (1)

1. A load smooth control method based on a demand side resource unified state model comprises the following steps:
step one, establishing a uniform state model of demand side resources:
dividing the time of one day into M time intervals by taking a distributed power supply, an electric automobile and a temperature control load as demand side resources, wherein each interval time is delta t, namely M multiplied by delta t is 24 h;
the superscript i is used for indicating the resource type, i belongs to { G, V, L }, G, V and L respectively represent a distributed power supply, an electric automobile and a temperature control load; the subscript j is used for indicating the number of a specific demand side resource in the distributed power supply G, the electric automobile V and the temperature control load L;
1-1) establishing a distributed power state model as follows:
distributed electricityUpper limit of output power of source j
Figure FDA0002258781470000011
And lower limit
Figure FDA0002258781470000012
The following were used:
Figure FDA0002258781470000013
in the formula (1), the reaction mixture is,
Figure FDA0002258781470000014
the maximum output power provided by the distributed power supply j in a real-time state at the moment t;
the state model of the distributed power supply is as follows:
Figure FDA0002258781470000015
in the formula (2), the reaction mixture is,
Figure FDA0002258781470000016
is the accumulated amount of output power of the distributed power supply j in a real-time state, wherein,
Figure FDA0002258781470000017
Figure FDA0002258781470000018
the output power of the distributed power supply j in a real-time state is within the upper limit and the lower limit respectively
Figure FDA0002258781470000019
And
Figure FDA00022587814700000110
Figure FDA00022587814700000111
the accumulated quantity of the electric energy generated by the distributed power supply j with rated output power is calculated according to the formula (3):
Figure FDA00022587814700000112
in the formula (3), the reaction mixture is,
Figure FDA00022587814700000113
is the rated output power in real time;
1-2) establishing an electric automobile state model, comprising the following steps:
upper limit of j power output of electric vehicle
Figure FDA00022587814700000114
And lower limit
Figure FDA00022587814700000115
The following were used:
Figure FDA00022587814700000116
in the formula (4), the reaction mixture is,
Figure FDA00022587814700000117
in order to start the charging time,
Figure FDA00022587814700000118
in order to start the time of the trip,
Figure FDA00022587814700000119
and
Figure FDA00022587814700000120
rated charge and discharge powers, respectively;
Figure FDA00022587814700000121
in order to be the maximum output power,
Figure FDA00022587814700000122
is a positive value;
Figure FDA00022587814700000123
in order to achieve the minimum output power,
Figure FDA00022587814700000124
is a negative value;
normalized SOC value of electric vehicle j
Figure FDA0002258781470000021
The following were used:
Figure FDA0002258781470000022
in the formula (5), the reaction mixture is,
Figure FDA0002258781470000023
the real-time SOC value of the electric vehicle j is obtained;
Figure FDA0002258781470000024
the electric vehicle j is charged at the rated power up to the upper limit of the SOC,
Figure FDA0002258781470000025
discharging the electric vehicle j at rated power until reaching the lower limit of the SOC; when the electric automobile is charged, the SOC value rises to the SOC value required by the user
Figure FDA0002258781470000026
If so, stopping charging;
when the electric automobile is connected with the power distribution network, the j state model of the electric automobile is as follows:
Figure FDA0002258781470000027
in the formula (6), the reaction mixture is,
Figure FDA0002258781470000028
for the real-time power output of the electric vehicle j,
Figure FDA0002258781470000029
Figure FDA00022587814700000210
for the corrected j battery capacity of the electric vehicle,
Figure FDA00022587814700000211
comprises the following steps:
Figure FDA00022587814700000212
in the formula (7), the reaction mixture is,
Figure FDA00022587814700000213
the actual battery capacity of the electric automobile;
Figure FDA00022587814700000214
and
Figure FDA00022587814700000215
respectively the charging efficiency and the discharging efficiency of the electric vehicle,
Figure FDA00022587814700000216
is the output power;
1-3) establishing a temperature control load state model
Upper limit of output power of temperature controlled load j
Figure FDA00022587814700000217
And lower limit
Figure FDA00022587814700000218
The following were used:
Figure FDA00022587814700000219
in the formula (8), the reaction mixture is,
Figure FDA00022587814700000220
rated power consumption;
Figure FDA00022587814700000221
in order to obtain a lower limit of the output power,
Figure FDA00022587814700000222
is a negative value;
Figure FDA00022587814700000223
in order to achieve the upper limit of the output power,
Figure FDA0002258781470000031
the value is 0;
normalized indoor temperature of temperature controlled load j
Figure FDA0002258781470000032
And outdoor temperature
Figure FDA0002258781470000033
As shown below, wherein,
Figure FDA0002258781470000034
Figure FDA0002258781470000035
the state model of the temperature-controlled load is as follows:
Figure FDA0002258781470000036
in the formulae (9) and (10),
Figure FDA0002258781470000037
is the temperature of the room in question,
Figure FDA0002258781470000038
and
Figure FDA0002258781470000039
respectively the upper and lower limits of the temperature control threshold,
Figure FDA00022587814700000310
is the temperature of the outside of the room,
Figure FDA00022587814700000311
and
Figure FDA00022587814700000312
upper and lower limits of the indoor temperature, respectively; in that
Figure FDA00022587814700000313
During the time period, the temperature control load is in an on state and the indoor temperature rises
Figure FDA00022587814700000314
In the time period, the heat source equipment is in a turn-off state, and the indoor temperature is reduced; for temperature-controlled loads in the on state, the temperature is
Figure FDA00022587814700000315
Range-off, temperature in the case of temperature-controlled loads in the off state
Figure FDA00022587814700000316
Opening when the range is reached;
Figure FDA00022587814700000317
is normalized outdoor temperature, ajIs equal to
Figure FDA00022587814700000318
Wherein R isjAnd CjRespectively a thermal resistor and a capacitor;
Figure FDA00022587814700000319
to output power, when the temperature controlled load is in an on state,
Figure FDA00022587814700000320
the temperature controlled load in the off state,
Figure FDA00022587814700000321
is composed of
Figure FDA00022587814700000322
1-4) establishing a unified state model, including
The numbers of the distributed power supply, the electric automobile and the temperature control load are respectively NG、NV、NLAnd satisfy NG+NV+NL＝N；
According to the distributed power supply state model, the electric vehicle state model and the temperature control load state model respectively expressed by the above equations (2), (6) and (10), the demand side resource state model is expressed by equation (11):
Figure FDA0002258781470000041
wherein:
Figure FDA0002258781470000042
Figure FDA0002258781470000043
Figure FDA0002258781470000044
Figure FDA0002258781470000045
Figure FDA0002258781470000046
Figure FDA0002258781470000047
on the basis of equation (11), the demand-side resource unified state model is shown as equation (18):
x(t+Δt)＝x(t)+P(t)δ(t) (18)
in equation (18), the column vector x (t) is the real-time status of the demand-side resource, and the element satisfies
Figure FDA0002258781470000048
The superscript i represents a resource type, and i G, i V, i L represents that the resource type is a distributed power supply, an electric automobile and a temperature control load; the diagonal matrix P (t) is a real-time output power matrix of the resource at the demand side, and the diagonal elements meet
Figure FDA0002258781470000049
The column vector δ (t) is defined as the modified time interval;
step two, smooth control of a load curve:
and evaluating the load fluctuation condition of the power distribution network by using the power fluctuation rate, wherein the load fluctuation condition is expressed by formulas (19) and (20):
Figure FDA0002258781470000051
Figure FDA0002258781470000052
in the formulae (19) and (20), the function fTUsed for calculating the power fluctuation rate of the load in the time period T; function(s)
Figure FDA0002258781470000053
And
Figure FDA0002258781470000054
used for calculating the maximum value and the minimum value of the load in the time period T;
Figure FDA0002258781470000055
is the rated value of the load;
Figure FDA0002258781470000056
and
Figure FDA0002258781470000057
load maximum and minimum; pt DA real-time load value;
the method for realizing the smooth control of the load curve comprises the following steps:
the first step is as follows: determining a target power for load smoothing
By rtRepresents the real-time power fluctuation rate, as shown in equation (21):
Figure FDA0002258781470000058
then, a target power value P for load smoothing is determinedt *
(i) When in use
Figure FDA0002258781470000059
When the temperature of the water is higher than the set temperature,
Figure FDA00022587814700000510
(ii) when in use
Figure FDA00022587814700000511
When the temperature of the water is higher than the set temperature,
Figure FDA00022587814700000512
(iii) when in use
Figure FDA00022587814700000513
When the temperature of the water is higher than the set temperature,
Pt *＝Pt D(24)
in equations (22), (23) and (24), the upper limit of the load real-time power fluctuation rate
Figure FDA00022587814700000514
And lower limit
Figure FDA00022587814700000515
As shown in equation (25):
Figure FDA00022587814700000516
in the formula (25), the reaction mixture,
Figure FDA00022587814700000517
is the power fluctuation ratio rTThe limit of (2); delta Pt *The target variation power for load smoothing is shown as equation (26);
ΔPt *＝Pt *-Pt D(26)
the second step is that: determining responsiveness of different demand side resources
To implement a load curve smoothing strategy based on a unified state model, the matrix P (t) is decomposed into the product of two matrices
Figure FDA00022587814700000518
I.e. real time output power
Figure FDA00022587814700000519
By
Figure FDA00022587814700000520
Instead of, i.e. using
Figure FDA00022587814700000521
In the formula (27), the reaction mixture is,
Figure FDA00022587814700000522
are diagonal matrix, diagonal elements
Figure FDA00022587814700000523
The upper limit of the output power of the demand side resource j; the diagonal matrix B is an output power control matrix, and diagonal elements
Figure FDA0002258781470000061
To increase or decrease the control variable for the output power of the demand-side resource j,
Figure FDA0002258781470000062
the formula (18) is rewritten as follows:
Figure FDA0002258781470000063
the ability to increase output power is:
Figure FDA0002258781470000064
the ability to reduce output power is:
Figure FDA0002258781470000065
in the formulas (29) and (30), matrix
Figure FDA0002258781470000066
Is the maximum value of the controllable variable, the matrixBIs the minimum value of the controllable variable; l is0Is an N × 1 dimensional matrix with elements all 1; pup(t) is a matrix of dimension N × 1, the non-negative elements of the mth row indicating the ability of the mth resource to increase output power; pdn(t) is an N × 1 dimensional matrix, and the non-positive value element in the mth row represents the capability of the mth resource to reduce the output power;
define lower triangular matrix
Figure FDA0002258781470000067
And lower triangular arrayB *Respectively evaluating the capacity of increasing output power and the capacity of reducing output power of the resource at the demand side, and setting a lower triangular array
Figure FDA0002258781470000068
And lower triangular arrayB *The element in (1) is shown as formula (31):
Figure FDA0002258781470000069
the following are written over equations (29) and (30):
Figure FDA00022587814700000610
in the formula (32), Pup*(t) is an Nx 1 dimensional matrix, and the non-negative value element of the mth row represents the capability of increasing the output power of 1-m resources; pdn*(t) is also an N × 1 dimensional matrix, the non-positive value elements of the m-th row represent the ability of 1-m resources to reduce output power;
the third step: determining an actual control matrix B*
(i) When Δ Pt *When > 0, let j1To satisfy
Figure FDA00022587814700000611
The maximum subscript of (a);
Figure FDA00022587814700000612
(ii) when Δ Pt *When the content is equal to 0, the content,
B*＝B (34)
(iii) when Δ Pt *When < 0, let j2To satisfy
Figure FDA00022587814700000613
The maximum subscript of (a) is,
Figure FDA0002258781470000071
the output power of the demand-side resource is represented by equation (36):
Figure FDA0002258781470000072
the updated state model of the demand-side resource is represented by equation (37):
Figure FDA0002258781470000073
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