CN112434444A - Power distribution network station network collaborative capacity expansion planning method considering demand response - Google Patents

Power distribution network station network collaborative capacity expansion planning method considering demand response Download PDF

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CN112434444A
CN112434444A CN202011427995.1A CN202011427995A CN112434444A CN 112434444 A CN112434444 A CN 112434444A CN 202011427995 A CN202011427995 A CN 202011427995A CN 112434444 A CN112434444 A CN 112434444A
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charging
distribution network
electric automobile
power
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CN112434444B (en
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王哲
迟福建
万宝
李桂鑫
孙阔
徐晖
张可佳
何玉龙
梁海深
王庆彪
徐福
周建伟
穆云飞
董晓红
邓友均
唐舒懿
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Tianjin University
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Binhai Power Supply Co of State Grid Tianjin Electric Power Co Ltd
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Tianjin University
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Binhai Power Supply Co of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention discloses a power distribution network station network collaborative capacity expansion planning method considering demand response, which adopts a double-layer optimization model: the lower layer mainly establishes an EV charge-discharge behavior optimization model based on compensation price, and optimizes EV charge-discharge power with the aim of minimum user charge cost; and the upper layer considers the response of the EV user to the compensation price, establishes a power distribution network station network collaborative capacity expansion planning model, and determines the capacities of the transformer substation and the line and the charging compensation price by taking the minimum annual total investment operation cost as a target. The method of the invention indirectly guides the charging process by introducing the optimized compensation price, improves the charging load distribution and the operation of the power distribution network, delays the investment of the power distribution network and can reduce the annual total investment and operation cost of the power distribution network.

Description

Power distribution network station network collaborative capacity expansion planning method considering demand response
Technical Field
The invention belongs to the technical field of power distribution network capacity expansion planning, and relates to a power distribution network station network collaborative capacity expansion planning method considering demand response.
Background
In recent years, under the double pressure of the shortage of fossil energy and the problem of environmental pollution, an Electric Vehicle (EV) has been receiving wide attention and vigorously developed from countries around the world as a clean, low-noise and near-zero-emission vehicle. As the amount of EV remaining increases, a high percentage of EV charging load accesses will also have some negative impact on the distribution grid. Research has shown that EV charging load access will significantly reduce the service life of distribution lines and transformers. In addition, some distribution network operating problems are also caused, such as voltage excursions, three-term imbalances, and harmonic pollution.
Current research mainly addresses the influence of EV charging load access on the distribution network from two aspects: firstly, on a short time scale, by orderly controlling EV charge and discharge behaviors, load peak clipping and valley filling of a system are realized, and the operating environment of a power distribution network is improved; and secondly, on a long time scale, the increased EV charging load is met by carrying out capacity expansion planning and upgrading on the power distribution network. However, the sensitivity of EV users to the charging price is not considered in the two aspects, the demand response capability of the EV is not fully utilized, and the charging load is managed through price guidance, so as to achieve the purpose of reducing the investment and operation cost of the power distribution network.
Disclosure of Invention
The invention aims to make up for the defects in the prior art, and provides a power distribution network station and network collaborative capacity expansion planning method which can reduce the investment and operation cost of a power distribution network and takes demand response into consideration.
In order to solve the problems, the invention adopts the following technical scheme:
a power distribution network station network collaborative capacity expansion planning method considering demand response is characterized in that a double-layer optimization model is adopted, an EV charge-discharge behavior optimization model based on compensation price is mainly established at the lower layer of the double-layer optimization model, and EV charge-discharge power is optimized with the aim of minimizing user charge cost; the upper layer of the double-layer optimization model considers the response of EV users to the compensation price, establishes a power distribution network station network collaborative capacity expansion planning model, and determines the capacities of the transformer substation and the lines and the charging compensation price by taking the minimum annual total investment operation cost as a target;
the method comprises the following steps:
generating a line capacity expansion type, a substation capacity expansion capacity and a compensation price scheme according to upper and lower limit constraint conditions of line capacity, substation capacity and compensation price, and setting iteration number It to be 1;
determining the charge and discharge power of each EV by utilizing an EV charge and discharge behavior optimization model based on the compensation price according to the compensation price scheme;
step three, calculating the annual charging compensation cost C of the power distribution network for compensating the power distribution network to the user according to the optimized charging and discharging behaviorsB(ii) a The charging load is 0 in combination with other moments when the electric automobile is not connected to the power grid, and the charging load of a certain node at a certain moment can be obtained;
updating specific loads according to the calculated charging loads and nodes accessed to the power distribution network, and calculating load flow distribution and network loss;
step five, judging whether node voltage constraint, circuit current constraint and transformer substation load rate constraint are met, and if yes, setting zeta to 0; otherwise, ζ ═ infinity;
step six, calculating the annual total investment operation cost C according to the power distribution network station network collaborative capacity expansion planning model, wherein the annual total investment operation cost C is the annual construction operation cost C of the transformer substationSAnnual line construction cost CLDistribution network annual network loss cost COAnnual charging compensation cost CBAnd ζ;
judging whether a termination condition is met, if so, obtaining a final substation and line capacity expansion scheme; if not, regenerating a new scheme through a selection, intersection and mutation algorithm, updating the iteration number It to be It +1, and executing the steps two to seven until a termination condition is met.
Further, the specific steps of the first step are as follows:
generating a capacity expansion type x of the line j according to upper and lower limit constraint conditions of line capacity, transformer substation capacity and compensation pricejAnd the capacity expansion capacity delta S of the transformer substationeAnd a compensation pricing scheme pl (t), ph (t), setting It equal to 1;
l type compensation price constraint:
pLmin≤pl(t)≤pLmax (1)
in the formula, pLminAnd pLmaxThe minimum value and the maximum value of the L-type compensation price are respectively, and the specific values are respectively 0 and 0.5;
type H compensation price constraint:
pHmin≤ph(t)≤pHmax (2)
in the formula, pHminAnd pHmaxThe minimum value and the maximum value of the H-type compensation price are respectively, and the specific values are respectively 0.5 and 1.08;
xj∈LinS (3)
ΔSe∈SubS (4)
in the formula, LinS and SubS are candidate line types and substation candidate capacity expansion sets, respectively.
Further, the specific steps of the second step are as follows:
step 2.1, considering the following three user types, and determining the user response type of each EV;
1) type A EV
Under the effect of the compensation price, the charging process of the A-type EV is not changed, namely the A-type EV is charged at rated power immediately after being connected into a power grid, and the A-type EV has no response to the compensation price of electricity, so that the A-type EV can be regarded as an uncontrollable load;
2) type B EV
Under the effect of the L-type compensation price, the B-type EV does not participate in discharge response, but can change the charging power of the B-type EV, and plan the charging process with the lowest charging cost of a user in a period of accessing the power grid, namely transferring the charging load to a period of low electricity price;
3) type C EV
Under the action of the H-type compensation price, the C-type EV participates in discharge response, the charge and discharge power of the C-type EV can be changed, and the charge process is planned with the lowest charge cost of a user in a period of accessing the power grid;
step 2.2, under the effect of price compensation, B type electric automobile i1And type C electric vehicle i2Respectively at the cost of charging
Figure BDA0002819806750000021
And cost of charging
Figure BDA0002819806750000031
The minimum is taken as a target, and the B type electric automobile i is optimally determined as shown in a formula (11) and a formula (12)1And type C electric vehicle i2The charging load after the power grid is accessed meets the following constraint conditions, as shown in formulas (14) and (21);
Figure BDA0002819806750000032
Figure BDA0002819806750000033
Figure BDA0002819806750000034
wherein the content of the first and second substances,
Figure BDA0002819806750000035
and
Figure BDA0002819806750000036
are respectively an electric automobile i1And electric vehicle i2The time of access;
Figure BDA0002819806750000037
and
Figure BDA0002819806750000038
are respectively an electric automobile i1And electric vehicle i2Time of departure from the grid;
Figure BDA0002819806750000039
and
Figure BDA00028198067500000310
respectively a k node t time B type electric automobile i1Charging power and type C electric vehicle i2The charging and discharging power of (2) is a decision variable of the EV charging and discharging behavior optimization model; pr (t) is the electricity price at time t; pl (t) and ph (t) are the compensated prices for the L type and the H type at time t, respectively; delta (k, i)2T) is k node t moment electric automobile i20-1 variable of (1), when for type C electric vehicles
Figure BDA00028198067500000311
Greater than 0, delta (k, i)2T) takes the value of 1; otherwise, the reverse is carried out
Figure BDA00028198067500000312
Less than 0, namely the electric automobile i2Upon discharge, δ (k, i)2T) takes the value of 0 as shown in the formula (13);
accessing a single EV and meeting the power and time use constraints, namely in a V2G operation area of the single EV; 1) assuming that the electric automobile i is charged with rated active power immediately after being connected into a power grid until the SOC of the electric automobile reaches the upper limit value of the electric automobile; 2) assuming that the electric automobile i immediately discharges with rated active power after being connected to a power grid until the SOC of the electric automobile reaches a lower limit value of the SOC; 3) in order to ensure that the SOC of the electric automobile can meet the user requirement when the electric automobile leaves the power grid, namely ensuring that the SOC is not lower than the SOCi,d；
Wherein, the [ alpha ], [ beta ]SOC i,
Figure BDA00028198067500000313
]The upper and lower limit ranges of the state of charge of the electric vehicle i are used for preventing the EV from being charged or discharged excessively in the V2G process; SOCi,sThe method comprises the steps of obtaining an initial SOC value when an electric vehicle i is connected into a power grid after traveling is finished; SOCi,dThe demand of the user on the SOC of the battery before going out is met; t is ti,sTime for accessing the electric vehicle i to the power grid; t is ti,dThe time when the electric automobile i leaves the power grid and starts to travel is shown;
for a single electric vehicle in an operation area, the charging process is optimized, namely the following constraint conditions are required to be met:
b type electric automobile i1Charging power constraint of (1):
Figure BDA00028198067500000314
wherein the content of the first and second substances,
Figure BDA0002819806750000041
the rated charging power is 7 kW;
type C electric automobile i2Charging power constraint of (1):
Figure BDA0002819806750000042
wherein the content of the first and second substances,
Figure BDA0002819806750000043
the rated discharge power is-7 kW;
b type electric automobile i1The SOC constraint of (2):
Figure BDA0002819806750000044
Figure BDA0002819806750000045
wherein eta iscThe value is 0.9 for the charging efficiency; dt is the duration of the charging power, and the value is 1 hour; capi1Is an electric automobile i1The battery capacity of (a) is 35 kWh; SOCi1,tFor the electric automobile i at the time t1SOC of (1);
Figure BDA0002819806750000046
is an electric automobile i1The SOC upper and lower limits of (1) and (9) are respectively 0.1 and 0.9;
type C electric automobile i2The SOC constraint of (2):
Figure BDA0002819806750000047
Figure BDA0002819806750000048
wherein eta isdFor the discharge efficiency, the value is 0.9;
Figure BDA0002819806750000049
is an electric automobile i2The battery capacity of (a) is 35 kWh;
Figure BDA00028198067500000410
is an electric automobile i2SOC at time t;
Figure BDA00028198067500000411
is an electric automobile i2The SOC upper and lower limits of (1) and (9) are respectively 0.1 and 0.9;
b type electric automobile i1Energy constraint of
Figure BDA00028198067500000412
Wherein the content of the first and second substances,
Figure BDA00028198067500000413
is an electric automobile i1At the moment of ending charging
Figure BDA00028198067500000414
SOC at time;
type C electric automobile i2Energy constraint of
Figure BDA00028198067500000415
Wherein the content of the first and second substances,
Figure BDA0002819806750000051
is an electric automobile i1At the moment of ending charging
Figure BDA0002819806750000052
SOC at time of day.
Further, the third step comprises the following specific steps:
according to the optimized charging and discharging behaviors, calculating the annual charging compensation cost C of the power distribution network for compensating the power distribution network to the userBAs shown in the following formula (5); the charging load at the time of the k node t can be obtained by combining the charging load of 0 at other times when the electric automobile is not connected to the power grid
Figure BDA0002819806750000053
As shown in the following formula (6);
Figure BDA0002819806750000054
Figure BDA0002819806750000055
wherein N isday，
Figure BDA0002819806750000056
And
Figure BDA0002819806750000057
the number of the EV type electric vehicles is the number of the power distribution network nodes accessed to the power distribution network, the number of the B type electric vehicles accessed to the k nodes of the power distribution network and the number of the C type electric vehicles accessed to the k nodes of the power distribution network.
Further, the specific steps of the fourth step are as follows:
updating specific loads according to the calculated charging loads and nodes accessed to the power distribution network, and calculating load flow distribution and network loss as shown in the following formula (7);
Figure BDA0002819806750000058
wherein n isdnIs the number of nodes of the power distribution network,
Figure BDA0002819806750000059
is the voltage vector of node k, PkAnd QkRespectively the active and reactive power injected at node k,
Figure BDA00028198067500000510
is a distribution network node admittance matrix element Ykk1The complex number of the conjugate of (a),
Figure BDA00028198067500000511
is node k1The conjugate voltage of (c).
Further, the concrete steps of the fifth step are as follows:
judging whether node voltage constraint, circuit current constraint and transformer substation load rate constraint are met, wherein the node voltage constraint, the circuit current constraint and the transformer substation load rate constraint are shown in the following formulas (8) to (10); if so, ζ is 0; otherwise, ζ ═ infinity;
node voltage constraint: u shapek,min<Uk<Uk,max k∈ndn (8)
In the formula of UkIs the voltage per unit value of node k; u shapek，maxAnd Uk，minRespectively, the minimum value and the maximum value allowed by the node k, respectivelyValues of 0.9 and 1;
and (3) line current constraint: i isl≤Il,max l∈nline (9)
In the formula IlIs the per unit value of the current of the line l; n islineThe total number of lines of the power distribution network; i isl,maxIs the maximum value allowed by the line k, and the value is 1;
and (3) load rate constraint of the transformer substation:
Figure BDA0002819806750000061
in the formula, SubL is the maximum load factor, and is 0.85.
Further, in the sixth step, the power distribution network station collaborative capacity expansion planning model aims at minimizing the total annual investment operation cost C, as shown in formula (22);
minC＝Cs+CL+Co+CB+ζ (22)
wherein the annual charge compensation cost CBZeta can be calculated from the third and fifth steps;
annual construction and operation cost C of transformer substationS: the method can be calculated by using an equal-year value method, and is formed by annual capacity expansion construction cost and annual operation cost of the transformer substation, which are shown in a formula (23);
Figure BDA0002819806750000062
in the formula, r0Is the annual interest rate; m issIs the life of the substation; delta SeThe capacity expansion and enlargement capacity of the substation e belongs to a candidate capacity expansion scheme set SubS, and is shown as a formula (3);
Figure BDA0002819806750000063
the variable cost of the capacity-increasing extension of the transformer substation is achieved;
Figure BDA0002819806750000064
the fixed cost of the capacity-increasing extension of the transformer substation is obtained; t is the total number of time intervals; s0Is the initial capacity of the substationAn amount; sp,e(t) is the load carried by substation e at time t; n isseThe number of main transformers of the transformer substation e is shown; delta Ps,eShort-circuit loss of a single main transformer; delta P0,eIs the no-load loss of a single main transformer;
annual line construction cost CL: can be calculated by using an equal-year value method, and is specifically shown as a formula (24);
Figure BDA0002819806750000065
wherein m isLIs the line life; cl (x)j) Is line j capacity xjCorresponding cost per kilometer; capacity xjIs a decision variable, which belongs to a candidate line capacity scheme set LinS, as shown in formula (4); djIs the length of line j.
Network loss cost C of distribution network yearOThe calculation is specifically shown in formula (25):
Figure BDA0002819806750000066
wherein, PlossAnd (t) is the distribution network loss at the time t.
Further, in the seventh step, the termination condition is:
1) the iteration times meet the maximum times;
2) the difference between the total annual investment operation cost and the previous generation proposal is less than a threshold value.
Compared with the prior art, the invention has the beneficial effects that: because the EV charging load has certain flexibility, the distribution of the charging load can be improved under the guidance of the compensation price. The invention can appoint reasonable compensation price aiming at different types of users, improves charging load distribution, delays equipment investment and reduces annual total investment and operation cost of the power distribution network.
Drawings
FIG. 1 is a block diagram of the planning method of the present invention;
FIG. 2 is a flow chart of the planning method solution of the present invention;
FIG. 3a is a schematic diagram of distribution of time (working time) when an electric vehicle is connected to a power grid for work;
FIG. 3b is a schematic diagram of distribution of time (off-duty time) when the electric vehicle is connected to the power grid for work;
FIG. 4 is a schematic diagram of the distribution of the time when each type of electric vehicle leaves the power grid;
FIG. 5 is a schematic view of the operation region (energy angle) of the single-body electric vehicle V2G;
FIG. 6 is a schematic diagram of a power distribution network including an electric vehicle;
FIG. 7 is a schematic diagram of a compensated pricing scheme.
Detailed Description
The invention will be further illustrated with reference to specific embodiments:
as shown in fig. 1 and fig. 2, the method for planning the cooperative expansion of the power distribution network station network in consideration of the demand response provided by the present invention adopts a two-layer optimization model, which mainly includes an upper-layer power distribution network cooperative expansion planning model and a lower-layer EV charge-discharge behavior optimization model based on the compensation price, and the method specifically includes the following steps:
step one, generating a capacity expansion type x of a line j according to upper and lower limit constraint conditions of line capacity, substation capacity and compensation pricejAnd the capacity expansion capacity delta S of the transformer substationeAnd a compensation pricing scheme pl (t), ph (t), setting It equal to 1;
l type compensation price constraint:
pLmin≤pl(t)≤pLmax (1)
in the formula, pLminAnd pLmaxThe minimum and maximum values of the L-type compensation price, respectively.
Type H compensation price constraint:
pHmin≤ph(t)≤pHmax(2) in the formula, pHminAnd pHmaxRespectively the minimum and maximum value of the H-type compensation price.
xj∈LinS (3)
ΔSe∈SubS (4)
In the formula, LinS is a candidate line type, 10 types of lines with values of 1-10 are respectively taken, and specific parameters are shown in table 1; and the SubS is the candidate capacity expansion capacity of the substation and the capacity of 5, 8, 10 and 20 MVA.
TABLE 1 candidate line parameters
Numbering Line class Current (A) Line cost (10)3Yuan/km)
1 LGJ-25 135 150
2 LGJ-35 170 180
3 LGJ-50 220 220
4 LGJ-70 275 250
5 LGJ-95 335 300
6 LGJ-120 380 330
7 LGJ-150 445 370
8 LGJ-185 515 400
9 LGJ-240 610 450
10 LGJ-300 710 520
And step two, determining the charge and discharge power of each EV by utilizing an EV charge and discharge behavior optimization model based on the compensation price according to the compensation price scheme.
And 2.1, mainly considering the following three user types, and determining the user response type of each EV.
1) Type A EV
Under the effect of the compensation price, the charging process of the type A EV is not changed, namely the type A EV is charged at rated power immediately after being connected into a power grid. This type of EV does not have any response to the compensation price of electricity, so it can be considered as an uncontrollable load.
2) Type B EV
Under the effect of the L-type compensation price, the B-type EV does not participate in the discharge response, but can change the charging power of the B-type EV, and plan the charging process with the lowest charging cost of a user in the period of accessing the power grid, namely transfer the charging load to the period of low electricity price.
3) Type C EV
Under the action of the H-type compensation price, the C-type EV participates in discharge response, the charge and discharge power of the C-type EV can be changed, and the charge process is planned with the lowest charge cost of a user in a period of accessing the power grid.
Step 2.2, under the effect of price compensation, B type electric automobile i1And type C electric vehicle i2Respectively at the cost of charging
Figure BDA0002819806750000081
And cost of charging
Figure BDA0002819806750000082
The minimum is taken as a target, and the B type electric automobile i is optimally determined as shown in a formula (11) and a formula (12)1And type C electric vehicle i2And charging load after the power grid is connected. While satisfying the following constraints as shown in equations (14) and (21).
Figure BDA0002819806750000083
Figure BDA0002819806750000084
Figure BDA0002819806750000085
Wherein the content of the first and second substances,
Figure BDA0002819806750000086
and
Figure BDA0002819806750000087
are respectively an electric automobile i1And electric vehicle i2The time of access, specifically taking as an example the distribution of work usage EV obeying to fig. 3a and 3 b;
Figure BDA0002819806750000088
and
Figure BDA0002819806750000089
are respectively an electric automobile i1And electric vehicle i2The time of leaving the grid, specifically taking the work use EV leaving grid time distribution shown in fig. 4 as an example;
Figure BDA00028198067500000810
and
Figure BDA00028198067500000811
respectively a k node t time B type electric automobile i1Charging power and type C electric vehicle i2The charging and discharging power of (2) is a decision variable of the EV charging and discharging behavior optimization model; pr (t) is the electricity price at time t, and the electricity prices taken as time of use are shown in table 2; pl (t) and ph (t) are the compensated prices for the L type and the H type at time t, respectively; delta (k, i)2T) is k node t moment electric automobile i20-1 variable of (1), when for type C electric vehicles
Figure BDA00028198067500000812
Greater than 0, delta (k, i)2T) takes the value of 1; otherwise, the reverse is carried out
Figure BDA00028198067500000813
Less than 0, namely the electric automobile i2Upon discharge, δ (k, i)2And t) is 0 as shown in formula (13).
TABLE 2 price distribution of electricity
Classification Time period Electricity price (Yuan/kWh)
Peak period 8:00-12:00,17:00-21:00 1.082
Flat time period 12:00-17:00,21:00-24:00 0.649
In the valley period 0:00-8:00 0.316
The single EV is accessed and the power and time use constraint is satisfied, namely, the single EV is in the V2G operation area (shown by the shaded part in FIG. 5). 1) Assuming that the electric vehicle i is charged with rated active power immediately after being connected to the power grid, as shown in section AB in fig. 5, until the SOC of the electric vehicle reaches the upper limit value; 2) assuming that the electric vehicle i immediately discharges with rated active power after being connected to the power grid, as shown in section AD in fig. 5, until the SOC of the electric vehicle reaches its lower limit value; 3) to ensure that the electric vehicle leaves the power grid (t)i,dTime) of which the SOC can meet the user demand, i.e. the SOC is guaranteed not to be lower than the SOCi,dThe EF section is a forced charging process.
Wherein, the [ alpha ], [ beta ]SOC i,
Figure BDA0002819806750000091
]The upper and lower limit ranges of the State of charge (SOC) of the electric vehicle i to prevent EVOver-charged or over-discharged during V2G; SOCi,sThe method comprises the steps of obtaining an initial SOC value when an electric vehicle i is connected into a power grid after traveling is finished; SOCi,dThe demand of the user on the SOC of the battery before going out is met; t is ti,sTime for accessing the electric vehicle i to the power grid; t is ti,dAnd (4) the time when the electric automobile i leaves the power grid and starts to travel.
For a single electric vehicle in an operation area, the charging process is optimized, namely the following constraint conditions are required to be met:
b type electric automobile i1Charging power constraint of (1):
Figure BDA0002819806750000092
wherein the content of the first and second substances,
Figure BDA0002819806750000093
the rated charging power is 7 kW;
type C electric automobile i2Charging power constraint of (1):
Figure BDA0002819806750000094
wherein the content of the first and second substances,
Figure BDA0002819806750000095
the rated discharge power is-7 kW;
b type electric automobile i1The SOC constraint of (2):
Figure BDA0002819806750000096
Figure BDA0002819806750000097
wherein eta iscThe value is 0.9 for the charging efficiency; dt is the duration of the charging power, and the value is 1 hour;
Figure BDA0002819806750000098
is an electric automobile i1The battery capacity of (a) is 35 kWh;
Figure BDA0002819806750000101
for the electric automobile i at the time t1SOC of (1);
Figure BDA0002819806750000102
is an electric automobile i1The SOC upper and lower limits of (1) are respectively 0.1 and 0.9.
Type C electric automobile i2The SOC constraint of (2):
Figure BDA0002819806750000103
Figure BDA0002819806750000104
wherein eta isdFor the discharge efficiency, the value was 0.9. Capi2Is an electric automobile i2The battery capacity of (a) is 35 kWh;
Figure BDA0002819806750000105
is an electric automobile i2SOC at time t;
Figure BDA0002819806750000106
is an electric automobile i2The SOC upper and lower limits of (1) are respectively 0.1 and 0.9.
B type electric automobile i1Energy constraint of
Figure BDA0002819806750000107
Wherein the content of the first and second substances,
Figure BDA0002819806750000108
is an electric automobilei1At the moment of ending charging
Figure BDA0002819806750000109
SOC at time;
type C electric automobile i2Energy constraint of
Figure BDA00028198067500001010
Wherein
Figure BDA00028198067500001011
Is an electric automobile i1At the moment of ending charging
Figure BDA00028198067500001012
SOC at time;
step three, calculating the annual charging compensation cost C of the power distribution network for compensating the power distribution network to the user according to the optimized charging and discharging behaviorsBThe following formula (5) shows. The charging load at the time of the k node t can be obtained by combining the charging load of 0 at other times when the electric automobile is not connected to the power grid
Figure BDA00028198067500001013
As shown in the following formula (6);
Figure BDA00028198067500001014
Figure BDA00028198067500001015
wherein N isday，
Figure BDA00028198067500001016
And
Figure BDA00028198067500001017
the number of days of one year, the number of nodes of the power distribution network accessed by the EV, and the accessThe number of B-type electric vehicles of the k nodes of the power distribution network and the number of C-type electric vehicles of the k nodes of the power distribution network are 324 for example.
Step four, updating specific loads according to the calculated charging loads and nodes accessed to the power distribution network, and calculating the power flow distribution and the network loss as shown in the following formula (7);
Figure BDA0002819806750000111
wherein n isdnIs the number of distribution network nodes;
Figure BDA0002819806750000112
is the voltage vector of node k; pkAnd QkActive power and reactive power injected by the node k are respectively;
Figure BDA0002819806750000113
is a distribution network node admittance matrix element Ykk1The conjugate complex number of (a);
Figure BDA0002819806750000114
is node k1The conjugate voltage of (c).
And step five, judging whether node voltage constraint, circuit current constraint and transformer substation load rate constraint are met or not under the scheme, wherein the method is specifically shown in the following formulas (8) to (10). If so, ζ is 0; otherwise, ζ ═ infinity.
Node voltage constraint: u shapek,min<Uk<Uk,max k∈ndn (8)
In the formula of UkIs the voltage per unit value of node k; u shapek，maxAnd Uk，minThe minimum value and the maximum value allowed by the node k are respectively 0.9 and 1.
And (3) line current constraint: i isl≤Il,max l∈nline (9)
In the formula IlIs the per unit value of the current of the line l; n islineThe total number of lines of the power distribution network; i isl,maxIs allowed by line kThe maximum value is 1.
And (3) load rate constraint of the transformer substation:
Figure BDA0002819806750000115
in the formula, SubL is the maximum load factor, and is 0.85.
Step six, combining the annual charging compensation cost C obtained in the step threeBAnd calculating the annual total investment and operation cost C according to the power distribution network station network collaborative capacity expansion planning model considering the demand response. The total annual investment and operation cost C is the annual construction and operation cost C of the transformer substationSAnnual line construction cost CLDistribution network annual network loss cost COAnnual charging compensation cost CBAnd ζ. The method comprises the following specific steps:
the power distribution network station network collaborative capacity expansion planning model considering the demand response aims at minimizing the annual total investment operation cost C, as shown in a formula (22). The total annual investment and operation cost C is the annual construction and operation cost C of the transformer substationSAnnual line construction cost CLDistribution network annual network loss cost COAnd annual charging compensation cost CBAnd ζ. Wherein C isBAnd ζ can be calculated from step three and step five.
minC＝Cs+CL+Co+CB+ζ (22)
Annual construction and operation cost C of transformer substationS: the method can be calculated by using an equal-year value method, and is formed by annual capacity expansion construction cost and annual operation cost of the transformer substation, which are specifically shown as two items in a formula (23).
Figure BDA0002819806750000121
In the formula, r0Is annual interest rate, with a value of 10%; m issThe service life of the transformer substation is 10; delta SeThe capacity expansion and enlargement capacity of the substation e belongs to a candidate capacity expansion scheme set SubS, and is shown as a formula (3);
Figure BDA0002819806750000122
the variable cost of the capacity increase and extension of the transformer substation is 12.919 multiplied by 104RMB/MVA；
Figure BDA0002819806750000123
The fixed cost of the capacity increase and extension of the transformer substation is 248.62 multiplied by 104RMB/MVA; t is the total number of time intervals; s0The initial capacity of the transformer substation is 22.2 MVA; sp,e(t) is the load carried by substation e at time t; n isseThe number of main transformers of the transformer substation e is shown; delta Ps,eThe short-circuit loss of a single main transformer is 148 kW; delta P0,eThe no-load loss of a single main transformer is 38.6 kW.
② annual line construction cost CL: the method can be calculated by using an equal-year value method, and is specifically shown as a formula (24).
Figure BDA0002819806750000124
Wherein m isLIs the line life, the value is 10; cl (x)j) Is line j capacity xjCorresponding cost per kilometer; capacity xjIs a decision variable, which belongs to a candidate line capacity scheme set LinS, as shown in formula (4); djIs the length of line j.
Third, the annual loss cost of the distribution network CO：
Figure BDA0002819806750000125
Wherein, PlossAnd (t) is the distribution network loss at the time t.
Step seven, judging whether the following termination conditions are met, if so, obtaining a final substation and line capacity expansion scheme; if not, a new scheme is regenerated through a selection, intersection and mutation algorithm, the iteration number It is updated to be It +1, namely It is equal to It +1, and the steps two to seven are executed until the following termination condition is met.
1) The iteration times meet the maximum times;
2) the difference between the total annual investment operation cost and the previous generation proposal is less than a threshold value.
Simulation calculation example:
the feasibility and the effectiveness of the capacity expansion planning method are proved by applying the invention to an improved IEEE 33 node system and accessing the charging load at the corresponding node as shown in figure 6.
The scheme for planning the expansion of the distribution network without considering the compensation price is shown in table 3. The price compensation scheme shown in fig. 7 is adopted, and at this time, the capacity expansion planning scheme of the power distribution network is shown in table 4.
TABLE 3 Power distribution network Capacity expansion planning scheme without consideration of Compensation price
Figure BDA0002819806750000126
Figure BDA0002819806750000131
TABLE 4 Power distribution network capacity expansion planning scheme considering compensation price
Figure BDA0002819806750000132
Figure BDA0002819806750000141
As can be seen from tables 3 and 4, the investment of the power distribution network line can be delayed and the total annual investment and operation cost can be reduced due to the introduction of the compensation price. The cost comparison results for the 2 expansion schemes are shown in table 5.
TABLE 5 cost comparison of Capacity expansion protocol
Figure BDA0002819806750000142
The above description is only one application scenario of the present invention, and all equivalent changes and modifications made in the claims of the present invention or applied to the programming shall fall within the scope of the present invention.

Claims (8)

1. A power distribution network station network collaborative capacity expansion planning method considering demand response is characterized in that a double-layer optimization model is adopted, an EV charge-discharge behavior optimization model based on compensation price is established at the lower layer of the double-layer optimization model, and EV charge-discharge power is optimized with the aim of minimizing user charge cost; the upper layer of the double-layer optimization model considers the response of EV users to the compensation price, establishes a power distribution network station network collaborative capacity expansion planning model, and determines the capacities of the transformer substation and the lines and the charging compensation price by taking the minimum annual total investment operation cost as a target;
the method comprises the following steps:
generating a line capacity expansion type, a substation capacity expansion capacity and a compensation price scheme according to upper and lower limit constraint conditions of line capacity, substation capacity and compensation price, and setting iteration number It to be 1;
determining the charge and discharge power of each EV by utilizing an EV charge and discharge behavior optimization model based on the compensation price according to the compensation price scheme;
step three, calculating the annual charging compensation cost C of the power distribution network for compensating the power distribution network to the user according to the optimized charging and discharging behaviorsB(ii) a The charging load is 0 in combination with other moments when the electric automobile is not connected to the power grid, and the charging load of a certain node at a certain moment can be obtained;
updating specific loads according to the calculated charging loads and nodes accessed to the power distribution network, and calculating load flow distribution and network loss;
step five, judging whether node voltage constraint, circuit current constraint and transformer substation load rate constraint are met, and if yes, setting zeta to 0; otherwise, ζ ═ infinity;
step six, calculating the annual total investment operation cost C according to the power distribution network station network collaborative capacity expansion planning model, wherein the annual total investment operation cost C is the annual construction operation cost C of the transformer substationSAnnual line construction cost CLDistribution network annual network loss cost COAnnual charging compensation cost CBAnd ζ;
judging whether a termination condition is met, if so, obtaining a final substation and line capacity expansion scheme; if not, regenerating a new scheme through a selection, intersection and mutation algorithm, updating the iteration number It to be It +1, and executing the steps two to seven until a termination condition is met.
2. The power distribution network station network collaborative capacity expansion planning method considering demand response of claim 1, wherein the specific steps of the first step are as follows:
generating a capacity expansion type x of the line j according to upper and lower limit constraint conditions of line capacity, transformer substation capacity and compensation pricejAnd the capacity expansion capacity delta S of the transformer substationeAnd a compensation price scheme pl (t), ph (t), setting the iteration number It to 1;
l type compensation price constraint:
pLmin≤pl(t)≤pLmax (1)
in the formula, pLminAnd pLmaxThe minimum value and the maximum value of the L-type compensation price are respectively, and the specific values are respectively 0 and 0.5;
type H compensation price constraint:
pHmin≤ph(t)≤pHmax (2)
in the formula, pHminAnd pHmaxThe minimum value and the maximum value of the H-type compensation price are respectively, and the specific values are respectively 0.5 and 1.08;
xj∈LinS (3)
ΔSe∈SubS (4)
in the formula, LinS and SubS are candidate line types and substation candidate capacity expansion sets, respectively.
3. The power distribution network station network collaborative capacity expansion planning method considering the demand response of claim 2, wherein the specific steps of the second step are as follows:
step 2.1, considering the following three user types, and determining the user response type of each EV;
1) type A EV
Under the effect of the compensation price, the charging process of the A-type EV is not changed, namely the A-type EV is charged at rated power immediately after being connected into a power grid, and the A-type EV has no response to the compensation price of electricity, so that the A-type EV can be regarded as an uncontrollable load;
2) type B EV
Under the effect of the L-type compensation price, the B-type EV does not participate in discharge response, but can change the charging power of the B-type EV, and plan the charging process with the lowest charging cost of a user in a period of accessing the power grid, namely transferring the charging load to a period of low electricity price;
3) type C EV
Under the action of the H-type compensation price, the C-type EV participates in discharge response, the charge and discharge power of the C-type EV can be changed, and the charge process is planned with the lowest charge cost of a user in a period of accessing the power grid;
step 2.2, under the effect of price compensation, B type electric automobile i1And type C electric vehicle i2Respectively at the cost of charging
Figure FDA0002819806740000021
And cost of charging
Figure FDA0002819806740000022
The minimum is taken as a target, and the B type electric automobile i is optimally determined as shown in a formula (11) and a formula (12)1And type C electric vehicle i2The charging load after the power grid is accessed meets the following constraint conditions, as shown in formulas (14) and (21);
Figure FDA0002819806740000023
Figure FDA0002819806740000024
Figure FDA0002819806740000025
wherein the content of the first and second substances,
Figure FDA0002819806740000026
and
Figure FDA0002819806740000027
are respectively an electric automobile i1And electric vehicle i2The time of access;
Figure FDA0002819806740000028
and
Figure FDA0002819806740000029
are respectively an electric automobile i1And electric vehicle i2Time of departure from the grid;
Figure FDA00028198067400000210
and
Figure FDA00028198067400000211
respectively a k node t time B type electric automobile i1Charging power and type C electric vehicle i2The charging and discharging power of (2) is a decision variable of the EV charging and discharging behavior optimization model; pr (t) is the electricity price at time t; pl (t) and ph (t) are the compensated prices for the L type and the H type at time t, respectively; delta (k, i)2T) is k node t moment electric automobile i20-1 variable of (1), when for type C electric vehicles
Figure FDA0002819806740000031
Greater than 0, delta (k, i)2T) takes the value of 1; otherwise, the reverse is carried out
Figure FDA0002819806740000032
Less than 0, namely the electric automobile i2Upon discharge, δ (k, i)2T) takes the value of 0 as shown in the formula (13);
accessing a single EV and meeting the power and time use constraints, namely in a V2G operation area of the single EV; 1) assuming that the electric automobile i is charged with rated active power immediately after being connected into a power grid until the SOC of the electric automobile reaches the upper limit value of the electric automobile; 2) assuming that the electric automobile i immediately discharges with rated active power after being connected to a power grid until the SOC of the electric automobile reaches a lower limit value of the SOC; 3) in order to ensure that the SOC of the electric automobile can meet the user requirement when the electric automobile leaves the power grid, namely ensuring that the SOC is not lower than the SOCi,d；
Wherein the content of the first and second substances,
Figure FDA0002819806740000033
the upper and lower limit ranges of the state of charge of the electric vehicle i are used for preventing the EV from being charged or discharged excessively in the V2G process; SOCi,sThe method comprises the steps of obtaining an initial SOC value when an electric vehicle i is connected into a power grid after traveling is finished; SOCi,dThe demand of the user on the SOC of the battery before going out is met; t is ti,sTime for accessing the electric vehicle i to the power grid; t is ti,dThe time when the electric automobile i leaves the power grid and starts to travel is shown;
for a single electric vehicle in an operation area, the charging process is optimized, namely the following constraint conditions are required to be met:
b type electric automobile i1Charging power constraint of (1):
Figure FDA0002819806740000034
wherein the content of the first and second substances,
Figure FDA0002819806740000035
the rated charging power is 7 kW;
type C electric automobile i2Charging power constraint of (1):
Figure FDA0002819806740000036
wherein the content of the first and second substances,
Figure FDA0002819806740000037
the rated discharge power is-7 kW;
b type electric automobile i1The SOC constraint of (2):
Figure FDA0002819806740000038
Figure FDA0002819806740000039
wherein eta iscThe value is 0.9 for the charging efficiency; dt is the duration of the charging power, and the value is 1 hour;
Figure FDA00028198067400000310
is an electric automobile i1The battery capacity of (a) is 35 kWh;
Figure FDA00028198067400000311
for the electric automobile i at the time t1SOC of (1);
Figure FDA00028198067400000312
is an electric automobile i1The SOC upper and lower limits of (1) and (9) are respectively 0.1 and 0.9;
type C electric automobile i2The SOC constraint of (2):
Figure FDA0002819806740000041
Figure FDA0002819806740000042
wherein eta isdFor the discharge efficiency, the value is 0.9;
Figure FDA0002819806740000043
is an electric automobile i2The battery capacity of (a) is 35 kWh;
Figure FDA0002819806740000044
is an electric automobile i2SOC at time t;
Figure FDA0002819806740000045
is an electric automobile i2The SOC upper and lower limits of (1) and (9) are respectively 0.1 and 0.9;
b type electric automobile i1Energy constraint of
Figure FDA0002819806740000046
Wherein the content of the first and second substances,
Figure FDA0002819806740000047
is an electric automobile i1At the moment of ending charging
Figure FDA0002819806740000048
SOC at time;
type C electric automobile i2Energy constraint of
Figure FDA0002819806740000049
Wherein the content of the first and second substances,
Figure FDA00028198067400000410
is an electric automobile i1At the moment of ending charging
Figure FDA00028198067400000411
SOC at time of day.
4. The power distribution network station network collaborative capacity expansion planning method considering the demand response, according to claim 3, is characterized in that the specific steps of the third step are as follows:
according to the optimized charging and discharging behaviors, calculating the annual charging compensation cost C of the power distribution network for compensating the power distribution network to the userBAs shown in the following formula (5); the charging load at the time of the k node t can be obtained by combining the charging load of 0 at other times when the electric automobile is not connected to the power grid
Figure FDA00028198067400000412
As shown in the following formula (6);
Figure FDA00028198067400000413
Figure FDA00028198067400000414
wherein N isday，
Figure FDA00028198067400000415
And
Figure FDA00028198067400000416
the number of the EV type electric vehicles is the number of the power distribution network nodes accessed to the power distribution network, the number of the B type electric vehicles accessed to the k nodes of the power distribution network and the number of the C type electric vehicles accessed to the k nodes of the power distribution network.
5. The power distribution network station network collaborative capacity expansion planning method considering the demand response, according to claim 4, is characterized in that the fourth step specifically comprises the following steps:
updating specific loads according to the calculated charging loads and nodes accessed to the power distribution network, and calculating load flow distribution and network loss as shown in the following formula (7);
Figure FDA0002819806740000051
wherein n isdnIs the number of nodes of the power distribution network,
Figure FDA0002819806740000052
is the voltage vector of node k, PkAnd QkRespectively the active and reactive power injected at node k,
Figure FDA0002819806740000053
is a distribution network node admittance matrix element Ykk1The complex number of the conjugate of (a),
Figure FDA0002819806740000054
is node k1The conjugate voltage of (c).
6. The power distribution network station network collaborative capacity expansion planning method considering demand response of claim 5, wherein the concrete steps of the fifth step are as follows:
judging whether node voltage constraint, circuit current constraint and transformer substation load rate constraint are met, wherein the node voltage constraint, the circuit current constraint and the transformer substation load rate constraint are shown in the following formulas (8) to (10); if so, ζ is 0; otherwise, ζ ═ infinity;
node voltage constraint: u shapek,min<Uk<Uk,max k∈ndn (8)
In the formula of UkIs the voltage per unit value of node k; u shapek，maxAnd Uk，minThe minimum value and the maximum value allowed by the node k are respectively 0.9 and 1;
and (3) line current constraint: i isl≤Il,max l∈nline (9)
In the formula IlIs the per unit value of the current of the line l; n islineThe total number of lines of the power distribution network; i isl,maxIs the maximum value allowed by the line k, and the value is 1;
load factor of transformer substationAnd (3) constraint:
Figure FDA0002819806740000055
in the formula, SubL is the maximum load factor, and is 0.85.
7. The power distribution network station collaborative capacity expansion planning method considering demand response of claim 6, wherein in the sixth step, the power distribution network station collaborative capacity expansion planning model aims at minimizing annual total investment operation cost C, as shown in formula (22);
minC＝Cs+CL+Co+CB+ζ (22)
wherein the annual charge compensation cost CBAnd ζ can be calculated from step three and step five;
annual construction and operation cost C of transformer substationS: the method can be calculated by using an equal-year value method, and is formed by annual capacity expansion construction cost and annual operation cost of the transformer substation, which are shown in a formula (23);
Figure FDA0002819806740000061
in the formula, r0Is the annual interest rate; m issIs the life of the substation; delta SeThe capacity expansion and enlargement capacity of the substation e belongs to a candidate capacity expansion scheme set SubS, and is shown as a formula (3);
Figure FDA0002819806740000062
the variable cost of the capacity-increasing extension of the transformer substation is achieved;
Figure FDA0002819806740000063
the fixed cost of the capacity-increasing extension of the transformer substation is obtained; t is the total number of time intervals; s0Is the initial capacity of the substation; sp,e(t) is the load carried by substation e at time t; n isseThe number of main transformers of the transformer substation e is shown; delta Ps,eShort-circuit loss of a single main transformer; delta P0,eIs a single tableNo-load loss of a main transformer;
annual line construction cost CL: can be calculated by using an equal-year value method, and is specifically shown as a formula (24);
Figure FDA0002819806740000064
wherein m isLIs the line life; cl (x)j) Is line j capacity xjCorresponding cost per kilometer; capacity xjIs a decision variable, which belongs to a candidate line capacity scheme set LinS, as shown in formula (4); djIs the length of line j.
Network loss cost C of distribution network yearOThe calculation is specifically shown in formula (25):
Figure FDA0002819806740000065
wherein, PlossAnd (t) is the distribution network loss at the time t.
8. The power distribution network station network collaborative capacity expansion planning method considering the demand response of claim 7, wherein in the seventh step, the termination condition is:
1) the iteration times meet the maximum times;
2) the difference between the total annual investment operation cost and the previous generation proposal is less than a threshold value.
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