CN114139780A - Coordinated optimization method and system for virtual power plant and power distribution network containing distributed power supply - Google Patents

Coordinated optimization method and system for virtual power plant and power distribution network containing distributed power supply Download PDF

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CN114139780A
CN114139780A CN202111355212.8A CN202111355212A CN114139780A CN 114139780 A CN114139780 A CN 114139780A CN 202111355212 A CN202111355212 A CN 202111355212A CN 114139780 A CN114139780 A CN 114139780A
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郑惠萍
刘新元
王金浩
程雪婷
张谦
蒋涛
暴悦爽
王玮茹
李蒙赞
金玉龙
张丽敏
张艳菲
张一帆
马春
仲颖
段伟文
张颖
张宇萌
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Nari Technology Co Ltd
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Abstract

The invention discloses a coordinated optimization method and a coordinated optimization system for a virtual power plant and a power distribution network containing distributed power supplies, wherein the coordinated optimization method comprises the following steps: acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant; inputting the power distribution network VPP distribution robust coordination optimization model to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; and the second-stage model is used for performing power distribution network optimal scheduling by using the result of the first-stage model, and solving to obtain the daily transaction electric quantity and the operation cost of the power distribution network. The advantages are that: the consumption level of renewable energy sources is improved, and the economical efficiency of the system is optimized; the power distribution network electricity purchasing cost can be reduced, the electricity selling income is improved, and peak clipping and valley filling can be realized.

Description

Coordinated optimization method and system for virtual power plant and power distribution network containing distributed power supply
Technical Field
The invention relates to a coordinated optimization method and system for a virtual power plant and a power distribution network containing distributed power supplies, and belongs to the technical field of distributed power supply control.
Background
In order to realize the national aims of 'carbon peak reaching and carbon neutralization', a distributed power supply which takes renewable energy as power supply is connected to a power distribution network in a high proportion. In recent years, virtual power plants have been rapidly developed as an effective means for consuming renewable energy, but at the same time, the uncertainty of distributed power supplies also brings great challenges to the operation control of the virtual power plants. In order to cope with the increment of uncertainty caused by high-proportion renewable energy, a Virtual Power Plant (VPP) based on a distributed Power supply is generated at the same time, and the complementary cooperative operation of the VPP and the renewable energy is realized by excavating the flexibility adjusting capability of resources in each link of source network load storage.
The VPP aggregates all distributed power supplies and adjustable resources contained in a certain area through an internal communication and control architecture, and realizes effective management of large-scale and scattered distributed power supplies. The method takes a Yunnan small Zhongdian wind-solar-water distributed power supply demonstration project as a research object, and analyzes the coordination scheduling problem of a virtual power plant containing wind, solar and water and a power distribution company. The coordinated operation and reliable grid connection of a large number of distributed power supplies are realized by uniformly and optimally scheduling the combined heat and power generation units aggregated by the VPPs and the scattered small hydropower stations through an energy management system.
However, in the prior art, uncertainty is caused by processing distributed power access by using a distributed robust optimization method, and the problem needs to be solved urgently.
Disclosure of Invention
Aiming at the problem of cooperative optimization of VPP and a power distribution network, the invention provides a coordinated optimization method and a coordinated optimization system of a virtual power plant containing a distributed power supply and the power distribution network, wherein uncertainty caused by distributed power supply access is processed by adopting a distributed robust optimization method.
In order to solve the technical problem, the invention provides a coordinated optimization method of a virtual power plant containing distributed power supplies and a power distribution network, which comprises the following steps:
acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant;
inputting the power distribution network VPP distribution robust coordination optimization model to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; the second-stage model is used for carrying out power distribution network optimization scheduling by using the result of the first-stage model, and solving to obtain daily transaction electric quantity and operation cost of the power distribution network;
a VPP internal constraint stage and a power distribution network optimization scheduling stage based on the result of the VPP internal constraint stage;
the construction of the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes comprises the following steps:
constructing a two-stage power distribution network coordination optimization model containing a VPP, wherein the VPP is a virtual power plant of a distributed power supply constructed on the basis of a distributed photovoltaic power generation system, an energy storage battery, a gas turbine and a controllable load which are connected into a power distribution network; and considering the uncertainty of photovoltaic output, and constructing a multi-discrete scene-based two-stage power distribution network VPP distribution robust coordination optimization model based on the two-stage power distribution network coordination optimization model containing VPP.
Further, the two-stage power distribution network coordination optimization model containing the VPP comprises:
the first stage model, the objective function is the running cost of the VPP in the scheduling period, and is expressed as:
Figure RE-GDA0003479305120000021
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000022
cost factors for the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively,
Figure RE-GDA0003479305120000023
the rates of adjustment of the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively;
the VPP running cost objective function in the scheduling period comprises the following constraints:
1) DG constraints
Figure RE-GDA0003479305120000024
Figure RE-GDA0003479305120000025
In the formula, Pi DG,minAnd Pi DG,maxRespectively DG output power
Figure RE-GDA0003479305120000026
Upper and lower limits of (d);
Figure RE-GDA0003479305120000027
and
Figure RE-GDA0003479305120000028
respectively representing upward and downward climbing rate limits of the DG, wherein t and t +1 respectively represent two adjacent time periods in front and back, and the formula (1-1) and the formula (1-2) respectively represent DG output constraint and climbing rate constraint;
2) ESS constraints
Figure RE-GDA0003479305120000031
Figure RE-GDA0003479305120000032
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000033
and
Figure RE-GDA0003479305120000034
represents the charge and discharge power of the ESS,
Figure RE-GDA0003479305120000035
and
Figure RE-GDA0003479305120000036
identified by 0-1 for the charging and discharging state of the ESS,
Figure RE-GDA0003479305120000037
indicating that the device is in a charging state,
Figure RE-GDA0003479305120000038
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time;
Figure RE-GDA0003479305120000039
represents the upper limit of the charging and discharging power of the ESS;
Figure RE-GDA00034793051200000310
and
Figure RE-GDA00034793051200000311
as is the charge-discharge coefficient of the ESS,
Figure RE-GDA00034793051200000312
and
Figure RE-GDA00034793051200000313
representing the lower limit and the upper limit of the capacity of the ESS, and respectively representing energy storage charge-discharge power limit and energy storage electric quantity constraint by an equation (1-3) and an equation (1-4);
3) SL constraint
Figure RE-GDA00034793051200000314
Figure RE-GDA00034793051200000315
In the formula Pi SL,down Pi SL,upRespectively SL load removal and load
Figure RE-GDA00034793051200000316
Shifting into a maximum value; pi SL,maxThe maximum load translation amount;
Figure RE-GDA00034793051200000317
for the load non-translatable period, equations (1-5) represent a load translation power constraint, and equations (1-6) represent a load translation power balance constraint, a load translation power total constraint and a non-translatable period constraint;
4) IL constraint
Figure RE-GDA00034793051200000318
Figure RE-GDA00034793051200000319
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000041
for the power of the call for the IL,
Figure RE-GDA0003479305120000042
is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure RE-GDA0003479305120000043
is IL atUpper limit of number of calls in scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure RE-GDA0003479305120000044
for the non-callable period, equations (1-7) represent an IL call power constraint, equations (1-8) represent a call number constraint, a continuous non-call number constraint, and a non-callable period constraint;
5) PV restraint
The active power of the distributed photovoltaic is set to be in a maximum power point tracking mode, the photovoltaic is connected into the power distribution network through the inverter, and therefore the reactive power of the photovoltaic is adjustable and limited by the capacity of the inverter:
Figure RE-GDA0003479305120000045
in the formula:
Figure RE-GDA0003479305120000046
representing the active power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000047
representing the reactive power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000048
is the maximum apparent power of the photovoltaic inverter;
and in the second stage model, the optimization target is the minimum cost in the operation period of the power distribution network, and the target function minF is as follows:
Figure RE-GDA0003479305120000049
in the formula, Δ T is a time interval, and T is a scheduling period; etabuy,b、
Figure RE-GDA00034793051200000410
Respectively the electricity price for purchasing electricity in the day ahead and the electricity price for purchasing electricity in the spot market,
Figure RE-GDA00034793051200000411
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B、Pt R、
Figure RE-GDA00034793051200000412
The method comprises the following steps of respectively purchasing electric quantity in the day ahead, purchasing electric quantity in the spot market and using electric load of a user;
Figure RE-GDA00034793051200000413
calling power for the VPP, obtained by a first stage model optimization:
Figure RE-GDA00034793051200000414
Figure RE-GDA00034793051200000415
representing the active power of the photovoltaic in the VPP at time t,
Figure RE-GDA00034793051200000416
representing the discharge power of the stored energy in VPP at time t,
Figure RE-GDA00034793051200000417
representing the heavy electric power stored in the VPP at time t,
the objective function of the second stage model contains the following constraints:
1) flow restraint
Figure RE-GDA0003479305120000051
Figure RE-GDA0003479305120000052
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
J → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage assignment at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure RE-GDA0003479305120000053
and
Figure RE-GDA0003479305120000054
represents the active and reactive injection of the jth VPP;
Figure RE-GDA0003479305120000055
indicating the reactive compensation quantity, P, of a continuous type reactive compensation devicejk,tI → j represents the set of all line end nodes j pointed to by node i as the head end,
Figure RE-GDA0003479305120000056
representing the active load, Q, of node jjk,tRepresenting the reactive power at the head end of branch jk,
Figure RE-GDA0003479305120000057
the PV reactive power is represented by the PV reactive power,
Figure RE-GDA0003479305120000058
representing reactive load of node j, Vj,tRepresents the voltage magnitude at node j;
the above equation is further relaxed as a second order cone constraint, as follows:
Figure RE-GDA0003479305120000059
2) power balance constraint
Figure RE-GDA00034793051200000510
In the formula Pt lossThe active loss of the network is equivalent to the sum of active power injected by all nodes;
3) voltage safety constraints
Figure RE-GDA00034793051200000511
In the formula:
Figure RE-GDA00034793051200000512
and
Figure RE-GDA00034793051200000513
respectively representing the allowable voltage V of the systemj,tUpper and lower limits;
4) reactive power compensator restraint
Figure RE-GDA00034793051200000514
In the formula:
Figure RE-GDA00034793051200000515
and
Figure RE-GDA00034793051200000516
reactive compensation permitted for the reactive compensation device respectively
Figure RE-GDA00034793051200000517
A lower limit and an upper limit.
Further, the uncertainty of the photovoltaic output is considered, a two-stage power distribution network VPP distribution robust coordination optimization model based on a multi-discrete scene is constructed on the basis of the two-stage power distribution network coordination optimization model containing VPP, and the two-stage power distribution network VPP distribution robust coordination optimization model is expressed as follows:
Figure RE-GDA0003479305120000061
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents a first stage variable comprising:
Figure RE-GDA0003479305120000062
a is a cost coefficient corresponding to the output decision of different equipment; y issThe second-stage variables under the scene s comprise: vj,t，Iij,t， Pij,t，Qij,t，
Figure RE-GDA0003479305120000063
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes corresponding to variables in the model; xisPredicting an output vector for the PV; n is a radical ofsRepresenting a limited number of discrete scenes, NsK actual scenes are obtained from the limited discrete scenes through historical data and are obtained through scene clustering method screening; equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; equation (6) represents the equality constraint of the photovoltaic power generation uncertainty predicted output.
Further, the input is performed to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network, and the method comprises the following steps:
and solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multi-discrete scene by adopting a column and constraint generation algorithm to obtain the daily trading electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment.
Further, the method for solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes by adopting the column and constraint generation algorithm to obtain the power distribution network intra-day and VPP transaction electric quantity and the operation cost under the uncertain environment comprises the following steps:
decomposing the problem of solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes into a main problem and a sub-problem, wherein the main problem provides a lower bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the sub-problem provides an upper bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the upper bound and the lower bound are gradually closed through continuous iteration, when the difference value of the two bounds is smaller than a preset value, the iteration is stopped, an optimal solution is returned, and the power distribution network transaction power quantity and the operation cost in the day with the VPP under an uncertain environment are obtained;
the lower bound of the main question, denoted:
Figure RE-GDA0003479305120000071
Figure RE-GDA0003479305120000072
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000073
for the probability distribution found by the sub-problem,
Figure RE-GDA0003479305120000074
m is a second-stage variable flexibly adjusted according to the scene, and is the total number of model iterations;
the upper bound of the sub-problem, expressed as:
Figure RE-GDA0003479305120000075
the subproblem is a max-min bilayer structure, due to the inner layer constraint range YsCompletely unrelated to the outer layer constraint range psi, so that the inner layer min problem is solved in parallel, the worst probability distribution of the outer layer is searched according to the inner layer solving result,
Figure RE-GDA0003479305120000076
Figure RE-GDA0003479305120000077
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure RE-GDA0003479305120000081
Figure RE-GDA0003479305120000082
Figure RE-GDA0003479305120000083
in the formula, theta1And theta∞To allow maximum deviation of the probability distribution, α1And alpha∞Respectively uncertainty probability confidence under two norm constraint conditions,
Figure RE-GDA0003479305120000084
representing a set of positive real numbers, hsFor intermediate variables of construction, p0Expressing the initial probability, and K expressing the number of actual operation scenes, which is obtained by historical data;
after the main problem and the sub problem are decomposed, the solving steps are as follows:
step 1): setting an initial value, including:
the number of iterations m is 1, the lower bound L is 0, the upper bound U is + ∞,
Figure RE-GDA0003479305120000085
the superscript m indicates the number of iterations;
step 2): solving a main problem, comprising:
find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
Step 3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure RE-GDA0003479305120000086
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
Step 4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure RE-GDA0003479305120000087
And define new variables in the main problem
Figure RE-GDA0003479305120000088
And adding a correlation constraint Ys (m+1)；
Step 5): updating m to m +1, and returning to the step 2);
when iteration is terminated, the upper bound value and the lower bound value are unified, the optimal solution in the power distribution network accessed by the virtual power plant at the moment is determined according to the upper bound value and the lower bound value, and the optimal solution comprises the daily transaction electric quantity with the VPP of the power distribution network in an uncertain environment
Figure RE-GDA0003479305120000091
And carrying in the target function minF according to the optimal solution to obtain the running cost.
A coordinated optimization system for a virtual power plant and a power distribution network containing distributed power supplies comprises:
the acquisition module is used for acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant;
the optimization model processing module is used for inputting network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of the power distribution network accessed by the current virtual power plant into a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain daily transaction electric quantity and operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; the second-stage model is used for carrying out power distribution network optimization scheduling by using the result of the first-stage model, and solving to obtain daily transaction electric quantity and operation cost of the power distribution network;
a VPP internal constraint stage and a power distribution network optimization scheduling stage based on the result of the VPP internal constraint stage;
the construction of the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes comprises the following steps:
constructing a two-stage power distribution network coordination optimization model containing a VPP, wherein the VPP is a virtual power plant of a distributed power supply constructed on the basis of a distributed photovoltaic power generation system, an energy storage battery, a gas turbine and a controllable load which are connected into a power distribution network; and considering the uncertainty of photovoltaic output, and constructing a multi-discrete scene-based two-stage power distribution network VPP distribution robust coordination optimization model based on the two-stage power distribution network coordination optimization model containing VPP.
Further, the two-stage power distribution network coordination optimization model containing the VPP comprises:
the first stage model, the objective function is the running cost of the VPP in the scheduling period, and is expressed as:
Figure RE-GDA0003479305120000092
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000093
cost factors for the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively,
Figure RE-GDA0003479305120000094
the rates of adjustment of the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively;
the VPP running cost objective function in the scheduling period comprises the following constraints:
1) DG constraints
Figure RE-GDA0003479305120000101
Figure RE-GDA0003479305120000102
In the formula, Pi DG,minAnd Pi DG,maxRespectively DG output power
Figure RE-GDA0003479305120000103
Upper and lower limits of (d);
Figure RE-GDA0003479305120000104
and
Figure RE-GDA0003479305120000105
respectively representing upward and downward climbing rate limits of the DG, wherein t and t +1 respectively represent two adjacent time periods in front and back, and the formula (1-1) and the formula (1-2) respectively represent DG output constraint and climbing rate constraint;
2) ESS constraints
Figure RE-GDA0003479305120000106
Figure RE-GDA0003479305120000107
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000108
and
Figure RE-GDA0003479305120000109
represents the charge and discharge power of the ESS,
Figure RE-GDA00034793051200001010
and
Figure RE-GDA00034793051200001011
identified by 0-1 for the charging and discharging state of the ESS,
Figure RE-GDA00034793051200001012
indicating that the device is in a charging state,
Figure RE-GDA00034793051200001013
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time;
Figure RE-GDA00034793051200001014
represents the upper limit of the charging and discharging power of the ESS;
Figure RE-GDA00034793051200001015
and
Figure RE-GDA00034793051200001016
as is the charge-discharge coefficient of the ESS,
Figure RE-GDA00034793051200001017
and
Figure RE-GDA00034793051200001018
representing the lower limit and the upper limit of the capacity of the ESS, and respectively representing energy storage charge-discharge power limit and energy storage electric quantity constraint by an equation (1-3) and an equation (1-4);
3) SL constraint
Figure RE-GDA00034793051200001019
Figure RE-GDA00034793051200001020
In the formula Pi SL,down Pi SL,upRespectively SL load removal and load
Figure RE-GDA00034793051200001021
Shifting into a maximum value; pi SL,maxThe maximum load translation amount;
Figure RE-GDA00034793051200001022
for the load non-translatable period, equations (1-5) represent a load translation power constraint, and equations (1-6) represent a load translation power balance constraint, a load translation power total constraint and a non-translatable period constraint;
4) IL constraint
Figure RE-GDA0003479305120000111
Figure RE-GDA0003479305120000112
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000113
for the power of the call for the IL,
Figure RE-GDA0003479305120000114
is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure RE-GDA0003479305120000115
for the upper limit of the number of calls for IL in a scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure RE-GDA0003479305120000116
for the non-callable period, equations (1-7) represent an IL call power constraint, equations (1-8) represent a call number constraint, a continuous non-call number constraint, and a non-callable period constraint;
5) PV restraint
The active power of the distributed photovoltaic is set to be in a maximum power point tracking mode, the photovoltaic is connected into the power distribution network through the inverter, and therefore the reactive power of the photovoltaic is adjustable and limited by the capacity of the inverter:
Figure RE-GDA0003479305120000117
in the formula:
Figure RE-GDA0003479305120000118
representing the active power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000119
representing the reactive power of the photovoltaic inverter during the period t,
Figure RE-GDA00034793051200001110
is the maximum apparent power of the photovoltaic inverter;
and in the second stage model, the optimization target is the minimum cost in the operation period of the power distribution network, and the target function minF is as follows:
Figure RE-GDA00034793051200001111
in the formula, DT is a time interval, and T is a scheduling period; etabuy,b、
Figure RE-GDA00034793051200001112
Respectively the electricity price for purchasing electricity in the day ahead and the electricity price for purchasing electricity in the spot market,
Figure RE-GDA00034793051200001113
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B、Pt R、
Figure RE-GDA00034793051200001114
The method comprises the following steps of respectively purchasing electric quantity in the day ahead, purchasing electric quantity in the spot market and using electric load of a user;
Figure RE-GDA0003479305120000121
calling power for the VPP, obtained by a first stage model optimization:
Figure RE-GDA0003479305120000122
Figure RE-GDA0003479305120000123
representing the active power of the photovoltaic in the VPP at time t,
Figure RE-GDA0003479305120000124
representing the discharge power of the stored energy in VPP at time t,
Figure RE-GDA0003479305120000125
representing the heavy electric power stored in the VPP at time t,
the objective function of the second stage model contains the following constraints:
1) flow restraint
Figure RE-GDA0003479305120000126
Figure RE-GDA0003479305120000127
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
J → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage assignment at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure RE-GDA0003479305120000128
and
Figure RE-GDA0003479305120000129
represents the active and reactive injection of the jth VPP;
Figure RE-GDA00034793051200001210
indicating the reactive compensation quantity, P, of a continuous type reactive compensation devicejk,tI → j represents the set of all line end nodes j pointed to by node i as the head end,
Figure RE-GDA00034793051200001211
representing the active load, Q, of node jjk,tRepresenting the reactive power at the head end of branch jk,
Figure RE-GDA00034793051200001212
the PV reactive power is represented by the PV reactive power,
Figure RE-GDA00034793051200001213
representing reactive load of node j, Vj,tRepresents the voltage magnitude at node j;
the above equation is further relaxed as a second order cone constraint, as follows:
Figure RE-GDA00034793051200001214
2) power balance constraint
Figure RE-GDA00034793051200001215
In the formula Pt lossThe active loss of the network is equivalent to the sum of active power injected by all nodes;
3) voltage safety constraints
Figure RE-GDA0003479305120000131
In the formula:
Figure RE-GDA0003479305120000132
and
Figure RE-GDA0003479305120000133
respectively representing the allowable voltage V of the systemj,tUpper and lower limits;
4) reactive power compensator restraint
Figure RE-GDA0003479305120000134
In the formula:
Figure RE-GDA0003479305120000135
and
Figure RE-GDA0003479305120000136
reactive compensation permitted for the reactive compensation device respectively
Figure RE-GDA0003479305120000137
A lower limit and an upper limit.
Further, the optimization model processing module comprises a model construction module for constructing a two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes, and the model construction module is represented as follows:
Figure RE-GDA0003479305120000138
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents a first stage variable comprising:
Figure RE-GDA0003479305120000139
a is a cost coefficient corresponding to the output decision of different equipment; y issThe second-stage variables under the scene s comprise: vj,t，Iij,t， Pij,t，Qij,t，
Figure RE-GDA00034793051200001310
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes corresponding to variables in the model; xisPredicting an output vector for the PV; n is a radical ofsRepresenting a limited number of discrete scenes, NsK actual scenes are obtained from the limited discrete scenes through historical data and are obtained through scene clustering method screening; equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; equation (6) represents the equality constraint of the photovoltaic power generation uncertainty predicted output.
Further, the input is performed to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network, and the method comprises the following steps:
and solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multi-discrete scene by adopting a column and constraint generation algorithm to obtain the daily trading electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment.
Further, the method for solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes by adopting the column and constraint generation algorithm to obtain the power distribution network intra-day and VPP transaction electric quantity and the operation cost under the uncertain environment comprises the following steps:
decomposing the problem of solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes into a main problem and a sub-problem, wherein the main problem provides a lower bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the sub-problem provides an upper bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the upper bound and the lower bound are gradually closed through continuous iteration, when the difference value of the two bounds is smaller than a preset value, the iteration is stopped, an optimal solution is returned, and the power distribution network transaction power quantity and the operation cost in the day with the VPP under an uncertain environment are obtained;
the lower bound of the main question, denoted:
Figure RE-GDA0003479305120000141
Figure RE-GDA0003479305120000142
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000143
for the probability distribution found by the sub-problem,
Figure RE-GDA0003479305120000144
m is a second-stage variable flexibly adjusted according to the scene, and is the total number of model iterations;
the upper bound of the sub-problem, expressed as:
Figure RE-GDA0003479305120000145
the subproblem is a max-min bilayer structure, due to the inner layer constraint range YsCompletely unrelated to the outer layer constraint range psi, so that the inner layer min problem is solved in parallel, the worst probability distribution of the outer layer is searched according to the inner layer solving result,
Figure RE-GDA0003479305120000151
Figure RE-GDA0003479305120000152
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure RE-GDA0003479305120000153
Figure RE-GDA0003479305120000154
Figure RE-GDA0003479305120000155
in the formula, theta1And theta∞To allow maximum deviation of the probability distribution, α1And alpha∞Respectively uncertainty probability confidence under two norm constraint conditions,
Figure RE-GDA0003479305120000156
representing a set of positive real numbers, hsFor intermediate variables of construction, p0Expressing the initial probability, and K expressing the number of actual operation scenes, which is obtained by historical data;
after the main problem and the sub problem are decomposed, the solving steps are as follows:
step 1): setting an initial value, including:
the number of iterations m is 1, the lower bound L is 0, the upper bound U is + ∞,
Figure RE-GDA0003479305120000157
the superscript m indicates the number of iterations;
step 2): solving a main problem, comprising:
find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
Step 3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure RE-GDA0003479305120000158
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
Step 4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure RE-GDA0003479305120000159
And define new variables in the main problem
Figure RE-GDA0003479305120000161
And adding a correlation constraint Ys (m+1)；
Step 5): updating m to m +1, and returning to the step 2);
when iteration is terminated, the upper bound value and the lower bound value are unified, the optimal solution in the power distribution network accessed by the virtual power plant at the moment is determined according to the upper bound value and the lower bound value, and the optimal solution comprises the daily transaction electric quantity with the VPP of the power distribution network in an uncertain environment
Figure RE-GDA0003479305120000162
And carrying in the target function minF according to the optimal solution to obtain the running cost.
The invention achieves the following beneficial effects:
1) the VPP model established by the method carries out most economic optimization on the output of each unit in the VPP on the premise of ensuring the operation of photovoltaic MPPT, thereby not only improving the consumption level of renewable energy sources, but also optimizing the economical efficiency of a system.
2) The power distribution network with VPP participation is optimally scheduled based on the time-of-use electricity price, so that the electricity purchasing cost of the power distribution network can be reduced, the electricity selling income is improved, and the peak clipping and valley filling can be realized.
3) The distributed robust optimization model considering the uncertainty of the distributed power supply can better balance the economy and the robustness and obtain higher benefits.
Drawings
FIG. 1 is a diagram of a VPP coordination control strategy.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
A distribution robust optimization operation strategy of a power distribution network comprising a virtual power plant comprises the following steps:
step (1): a VPP coordination control strategy is adopted, and a VPP coordination control center is responsible for regulating and controlling each power generation unit, energy storage unit and load unit in the VPP coordination control center and carrying out optimization decision in coordination with a power distribution network;
step (2): constructing a power distribution network coordination optimization model containing VPP, and decomposing variables into two stages by adopting a box decomposition algorithm to solve: bringing the constraint in the VPP into a first stage, and constraining the operation domain of each time period of the equipment in the VPP to obtain the integral output of each time period of the VPP; in the second stage, the result of the first stage is utilized to carry out power distribution network optimization scheduling, and the daily transaction electric quantity and the operation cost of the power distribution network are obtained through solving;
and (3): the uncertainty of photovoltaic output is considered, a two-stage power distribution network VPP distribution robust coordination optimization model based on a multi-discrete scene is constructed, and the model is iteratively solved by adopting a column and constraint generation (C & CG) algorithm.
Further, in step (1), the essence of the VPP is to integrally manage different areas and types of power generation resources, and the power distribution network does not directly control the power generation units or the energy storage devices during operation, but controls the VPP to participate in operation and scheduling of the power grid in an integrated manner. As shown in fig. 1, distributed photovoltaic packaging integration into a power distribution grid is incorporated herein, together with energy storage batteries (ESS), gas turbines (DG), and controllable loads, to form a VPP.
Further, in the step (2), operation optimization in a scheduling period is performed on the power distribution network with the VPP based on the time-of-use electricity price, the power distribution network firstly signs an electricity purchasing contract to purchase most of electricity based on load prediction electricity quantity in the day, and the surplus electricity quantity and the difference electricity quantity are exchanged on the spot market. Because the model has more related variables and more constraints, the box decomposition algorithm is adopted to decompose the variables into two stages for solving: bringing the constraint in the VPP into a first stage, and constraining the operation domain of each time period of the equipment in the VPP to obtain the integral output of each time period of the VPP; and in the second stage, the result of the first stage is utilized to carry out power distribution network optimization scheduling, and the daily transaction electric quantity and the operation cost of the power distribution network are obtained through solving.
For the first stage model, the objective function is the operating cost of the VPP in the scheduling period, where ESS and DG are regulated, photovoltaic is not regulated and the generation cost is not counted. IL and SL participate in VPP scheduling by contracting:
Figure RE-GDA0003479305120000171
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000172
the cost coefficients of DG, IL, SL respectively,
Figure RE-GDA0003479305120000173
for their respective call rates.
The following constraints are included:
1) DG constraints
Figure RE-GDA0003479305120000181
Figure RE-GDA0003479305120000182
In the formula, Pi DG,minAnd Pi DG,maxRespectively an upper limit and a lower limit of DG output power;
Figure RE-GDA0003479305120000183
and
Figure RE-GDA0003479305120000184
up and down ramp rate limits for DG, respectively. The above equations represent DG output constraint and ramp rate constraint, respectively.
2) ESS constraints
Figure RE-GDA0003479305120000185
Figure RE-GDA0003479305120000186
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000187
and
Figure RE-GDA0003479305120000188
represents the charge and discharge power of the ESS,
Figure RE-GDA0003479305120000189
and
Figure RE-GDA00034793051200001810
identified by 0-1 for the charging and discharging state of the ESS,
Figure RE-GDA00034793051200001811
indicating that the device is in a charging state,
Figure RE-GDA00034793051200001812
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time;
Figure RE-GDA00034793051200001813
represents the upper limit of the charging and discharging power of the ESS;
Figure RE-GDA00034793051200001814
and
Figure RE-GDA00034793051200001815
as is the charge-discharge coefficient of the ESS,
Figure RE-GDA00034793051200001816
and
Figure RE-GDA00034793051200001817
representing the lower and upper capacity limits of the ESS. The above formula represents the energy storage charge and discharge power limit and the energy storage electric quantity constraint respectively.
3) SL constraint
Figure RE-GDA00034793051200001818
Figure RE-GDA00034793051200001819
In the formula Pi SL,down Pi SL,upSL load shift out and load shift in maximum, respectively; pi SL,maxThe maximum load translation amount;
Figure RE-GDA00034793051200001820
a load non-translatable period. The above equations represent the load translation power constraint, the load translation power balance constraint, the load translation power total constraint, and the non-translation time period constraint, respectively.
4) IL constraint
Figure RE-GDA0003479305120000191
Figure RE-GDA0003479305120000192
In the formula
Figure RE-GDA0003479305120000193
Is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure RE-GDA0003479305120000194
for the upper limit of the number of calls for IL in a scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure RE-GDA0003479305120000195
is a non-callable period. The above equations respectively represent an IL call power constraint, a call number constraint, a consecutive non-call number constraint, and a non-call period constraint.
5) PV restraint
According to the national photovoltaic agricultural policy, in the model, the active power of the distributed photovoltaic is set to be a Maximum Power Point Tracking (MPPT) mode, and the photovoltaic is connected to the power distribution network through the inverter, so that the reactive power of the photovoltaic is adjustable and is limited by the capacity of the inverter:
Figure RE-GDA0003479305120000196
in the formula:
Figure RE-GDA0003479305120000197
representing the reactive power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000198
is the maximum apparent power of the photovoltaic inverter.
For the second stage model, the optimization target is that the cost in the operation period of the power distribution network is minimum, and the target function is as follows:
Figure RE-GDA0003479305120000199
in the formula, DT is a time interval, and T is a scheduling period; etabuy,b
Figure RE-GDA00034793051200001910
Respectively the electricity prices of the day-ahead and spot market purchase,
Figure RE-GDA00034793051200001911
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B Pt R
Figure RE-GDA00034793051200001912
Respectively purchasing electric quantity and user electricity utilization load quantity for the day-ahead and spot market;
Figure RE-GDA00034793051200001913
calling power for the VPP, obtained by a first stage model optimization:
Figure RE-GDA0003479305120000201
the following constraints are included:
1) flow restraint
Figure RE-GDA0003479305120000202
Figure RE-GDA0003479305120000203
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
In the formula: j → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure RE-GDA0003479305120000204
and
Figure RE-GDA0003479305120000205
represents the active and reactive injection of the kth VPP;
Figure RE-GDA0003479305120000206
the reactive compensation amount of the continuous reactive compensation device is shown.
Since the power flow constraint exhibits higher nonlinearity, for the convenience of model simplification, the above equation can be further relaxed into a second-order cone constraint, as shown below:
Figure RE-GDA0003479305120000207
2) power balance constraint
Figure RE-GDA0003479305120000208
In the formula Pt lossThe network active loss is equivalent to the sum of active power injected by all nodes.
3) Voltage safety constraints
Figure RE-GDA0003479305120000209
In the formula:
Figure RE-GDA00034793051200002010
and
Figure RE-GDA00034793051200002011
respectively representing the upper and lower voltage limits allowed by the system.
4) Reactive power compensator restraint
Figure RE-GDA00034793051200002012
In the formula:
Figure RE-GDA0003479305120000211
and
Figure RE-GDA0003479305120000212
respectively, the lower limit and the upper limit allowed by the reactive power compensation device.
Further, in step (3), due to the high uncertainty of PV output, a great challenge is brought to the optimal scheduling of VPP. The uncertainty of the PV output is processed by adopting a distributed robust optimization method, K actual scenes are obtained through the assumed historical data, and N is obtained through screening by certain scene clustering meanssAnd (4) obtaining the worst probability distribution of the finite scene under the known decision of the first-stage model through optimization. In order to limit the fluctuation change of probability distribution under uncertainty, a confidence set of 1-norm and infinity-norm is introduced for comprehensive constraint, and a distributed robust optimization model based on a discrete scene is constructed as follows:
Figure RE-GDA0003479305120000213
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents the first stage variables, including DG, ESS, SL, IL, PV decisions, i.e.
Figure RE-GDA0003479305120000214
Figure RE-GDA0003479305120000215
a is a cost coefficient corresponding to the output decision of different equipment; y issIs a second stage variable under the scene s, comprising a state variable and a continuous reactive power supplementing device action decision, namely Vj,t，Iij,t，Pij,t， Qij,t，
Figure RE-GDA0003479305120000216
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes, ξ, corresponding to variables in the modelsThe force vector is predicted for the PV. Equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable, such as the tidal flow constraint; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; equation (6) represents the equality constraint of the photovoltaic power generation uncertainty predicted output.
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure RE-GDA0003479305120000221
in the formula [ theta ]1And theta∞To allow for the maximum value of the probability distribution deviation, assume α1And alpha∞Respectively, uncertainty probability confidence under two norm constraint conditions, then:
Figure RE-GDA0003479305120000222
Figure RE-GDA0003479305120000223
the method adopts a column constraint generation algorithm (C & CGA) to carry out decoupling solution, and the basic idea is as follows: and decomposing the original problem into a main problem and a subproblem, wherein the main problem provides a lower bound for the model, the subproblem provides an upper bound for the model, the upper bound and the lower bound are gradually closed through continuous iteration, and when the difference value between the upper bound and the lower bound is smaller than a preset value, the iteration is stopped and the optimal solution is returned.
Solving the optimal solution x satisfying the system constraint on the premise of known probability distribution*The lower bound L of the entire model is given.
Figure RE-GDA0003479305120000224
Figure RE-GDA0003479305120000225
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000226
for a known (by sub-problem) probability distribution,
Figure RE-GDA0003479305120000227
and M is the total number of model iterations for a second stage variable which can be flexibly adjusted according to the scene.
Variable optimal solution x of subproblems in a known first stage*On the premise of (1), the worst scene probability distribution in the confidence interval is obtained, and the upper bound U of the whole model is given.
Figure RE-GDA0003479305120000228
The subproblem is a max-min bilayer structure, due to the inner layer constraint range YsAnd the method is completely irrelevant to the outer layer constraint range psi, so that the inner layer min problem can be solved in parallel firstly, and then the worst probability distribution of the outer layer is found according to the inner layer solving result.
Figure RE-GDA0003479305120000231
Figure RE-GDA0003479305120000232
Wherein the optimal solution x of the main problem*And substituting the inner layer problem for iteration.
After the distribution robust optimization model of the power distribution network is decomposed by C & CGA, the overall solving steps are as follows:
1): setting an initial value. The number of iterations m is 1, the lower bound L is 0, the upper bound U is + ∞,
Figure RE-GDA0003479305120000233
2): and solving the main problem. Find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure RE-GDA0003479305120000234
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure RE-GDA0003479305120000235
And define new ones in the main questionVariables of
Figure RE-GDA0003479305120000236
And adding a correlation constraint Ys (m+1)；
5): and updating m to m +1, and returning to the second step.
Correspondingly, the invention also provides a coordinated optimization system of the virtual power plant containing the distributed power supply and the power distribution network, which comprises the following steps:
the acquisition module is used for acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant;
the optimization model processing module is used for inputting network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of the power distribution network accessed by the current virtual power plant into a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain daily transaction electric quantity and operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; the second-stage model is used for carrying out power distribution network optimization scheduling by using the result of the first-stage model, and solving to obtain daily transaction electric quantity and operation cost of the power distribution network;
a VPP internal constraint stage and a power distribution network optimization scheduling stage based on the result of the VPP internal constraint stage;
the construction of the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes comprises the following steps:
constructing a two-stage power distribution network coordination optimization model containing a VPP, wherein the VPP is a virtual power plant of a distributed power supply constructed on the basis of a distributed photovoltaic power generation system, an energy storage battery, a gas turbine and a controllable load which are connected into a power distribution network; and considering the uncertainty of photovoltaic output, and constructing a multi-discrete scene-based two-stage power distribution network VPP distribution robust coordination optimization model based on the two-stage power distribution network coordination optimization model containing VPP.
Further, the two-stage power distribution network coordination optimization model containing the VPP comprises:
the first stage model, the objective function is the running cost of the VPP in the scheduling period, and is expressed as:
Figure RE-GDA0003479305120000241
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000242
cost factors for the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively,
Figure RE-GDA0003479305120000243
the rates of adjustment of the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively;
the VPP running cost objective function in the scheduling period comprises the following constraints:
1) DG constraints
Figure RE-GDA0003479305120000244
Figure RE-GDA0003479305120000245
In the formula, Pi DG,minAnd Pi DG,maxRespectively DG output power
Figure RE-GDA0003479305120000246
Upper and lower limits of (d);
Figure RE-GDA0003479305120000247
and
Figure RE-GDA0003479305120000248
respectively representing the upward and downward climbing rate limits of the DG, wherein t and t +1 respectively represent two adjacent time periods, and the formula (1-1) and the formula (1-2) respectively represent DG output constraintAnd a ramp rate constraint;
2) ESS constraints
Figure RE-GDA0003479305120000249
Figure RE-GDA0003479305120000251
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000252
and
Figure RE-GDA0003479305120000253
represents the charge and discharge power of the ESS,
Figure RE-GDA0003479305120000254
and
Figure RE-GDA0003479305120000255
identified by 0-1 for the charging and discharging state of the ESS,
Figure RE-GDA0003479305120000256
indicating that the device is in a charging state,
Figure RE-GDA0003479305120000257
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time;
Figure RE-GDA0003479305120000258
represents the upper limit of the charging and discharging power of the ESS;
Figure RE-GDA0003479305120000259
and
Figure RE-GDA00034793051200002510
as is the charge-discharge coefficient of the ESS,
Figure RE-GDA00034793051200002511
and
Figure RE-GDA00034793051200002512
representing the lower limit and the upper limit of the capacity of the ESS, and respectively representing energy storage charge-discharge power limit and energy storage electric quantity constraint by an equation (1-3) and an equation (1-4);
3) SL constraint
Figure RE-GDA00034793051200002513
Figure RE-GDA00034793051200002514
In the formula Pi SL,down Pi SL,upRespectively SL load removal and load
Figure RE-GDA00034793051200002515
Shifting into a maximum value; pi SL,maxThe maximum load translation amount;
Figure RE-GDA00034793051200002516
for the load non-translatable period, equations (1-5) represent a load translation power constraint, and equations (1-6) represent a load translation power balance constraint, a load translation power total constraint and a non-translatable period constraint;
4) IL constraint
Figure RE-GDA00034793051200002517
Figure RE-GDA00034793051200002518
In the formula (I), the compound is shown in the specification,
Figure RE-GDA00034793051200002519
for the power of the call for the IL,
Figure RE-GDA00034793051200002520
is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure RE-GDA00034793051200002521
for the upper limit of the number of calls for IL in a scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure RE-GDA0003479305120000261
for the non-callable period, equations (1-7) represent an IL call power constraint, equations (1-8) represent a call number constraint, a continuous non-call number constraint, and a non-callable period constraint;
5) PV restraint
The active power of the distributed photovoltaic is set to be in a maximum power point tracking mode, the photovoltaic is connected into the power distribution network through the inverter, and therefore the reactive power of the photovoltaic is adjustable and limited by the capacity of the inverter:
Figure RE-GDA0003479305120000262
in the formula:
Figure RE-GDA0003479305120000263
representing the active power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000264
representing the reactive power of the photovoltaic inverter during the period t,
Figure RE-GDA0003479305120000265
is the maximum apparent power of the photovoltaic inverter;
and in the second stage model, the optimization target is the minimum cost in the operation period of the power distribution network, and the target function minF is as follows:
Figure RE-GDA0003479305120000266
in the formula, Δ T is a time interval, and T is a scheduling period; etabuy,b、
Figure RE-GDA0003479305120000267
Respectively the electricity price for purchasing electricity in the day ahead and the electricity price for purchasing electricity in the spot market,
Figure RE-GDA0003479305120000268
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B、Pt R、
Figure RE-GDA0003479305120000269
The method comprises the following steps of respectively purchasing electric quantity in the day ahead, purchasing electric quantity in the spot market and using electric load of a user;
Figure RE-GDA00034793051200002610
calling power for the VPP, obtained by a first stage model optimization:
Figure RE-GDA00034793051200002611
Figure RE-GDA00034793051200002612
representing the active power of the photovoltaic in the VPP at time t,
Figure RE-GDA00034793051200002613
representing the discharge power of the stored energy in VPP at time t,
Figure RE-GDA00034793051200002614
representing the heavy electric power stored in the VPP at time t,
the objective function of the second stage model contains the following constraints:
1) flow restraint
Figure RE-GDA00034793051200002615
Figure RE-GDA00034793051200002616
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
J → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage assignment at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure RE-GDA0003479305120000271
and
Figure RE-GDA0003479305120000272
represents the active and reactive injection of the jth VPP;
Figure RE-GDA0003479305120000273
indicating the reactive compensation quantity, P, of a continuous type reactive compensation devicejk,tI → j represents the set of all line end nodes j pointed to by node i as the head end,
Figure RE-GDA0003479305120000274
representing the active load, Q, of node jjk,tRepresenting the reactive power at the head end of branch jk,
Figure RE-GDA0003479305120000275
the PV reactive power is represented by the PV reactive power,
Figure RE-GDA0003479305120000276
representing reactive load of node j, Vj,tRepresents the voltage magnitude at node j;
the above equation is further relaxed as a second order cone constraint, as follows:
Figure RE-GDA0003479305120000277
2) power balance constraint
Figure RE-GDA0003479305120000278
In the formula Pt lossThe active loss of the network is equivalent to the sum of active power injected by all nodes;
3) voltage safety constraints
Figure RE-GDA0003479305120000279
In the formula:
Figure RE-GDA00034793051200002710
and
Figure RE-GDA00034793051200002711
respectively representing the allowable voltage V of the systemj,tUpper and lower limits;
4) reactive power compensator restraint
Figure RE-GDA00034793051200002712
In the formula:
Figure RE-GDA00034793051200002713
and
Figure RE-GDA00034793051200002714
reactive compensation permitted for the reactive compensation device respectively
Figure RE-GDA00034793051200002715
A lower limit and an upper limit.
Further, the optimization model processing module comprises a model construction module for constructing a two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes, and the model construction module is represented as follows:
Figure RE-GDA00034793051200002716
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents a first stage variable comprising:
Figure RE-GDA0003479305120000281
a is a cost coefficient corresponding to the output decision of different equipment; y issThe second-stage variables under the scene s comprise: vj,t，Iij,t， Pij,t，Qij,t，
Figure RE-GDA0003479305120000282
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes corresponding to variables in the model; xisPredicting an output vector for the PV; n is a radical ofsRepresenting a limited number of discrete scenes, NsK actual scenes are obtained from the limited discrete scenes through historical data and are obtained through scene clustering method screening; equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; formula (A), (B) and6) and (3) an equality constraint representing the uncertainty predicted output of the photovoltaic power generation.
Further, the input is performed to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network, and the method comprises the following steps:
and solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multi-discrete scene by adopting a column and constraint generation algorithm to obtain the daily trading electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment.
Further, the method for solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes by adopting the column and constraint generation algorithm to obtain the power distribution network intra-day and VPP transaction electric quantity and the operation cost under the uncertain environment comprises the following steps:
decomposing the problem of solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes into a main problem and a sub-problem, wherein the main problem provides a lower bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the sub-problem provides an upper bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the upper bound and the lower bound are gradually closed through continuous iteration, when the difference value of the two bounds is smaller than a preset value, the iteration is stopped, an optimal solution is returned, and the power distribution network transaction power quantity and the operation cost in the day with the VPP under an uncertain environment are obtained;
the lower bound of the main question, denoted:
Figure RE-GDA0003479305120000291
Figure RE-GDA0003479305120000292
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003479305120000293
for the probability distribution found by the sub-problem,
Figure RE-GDA0003479305120000294
m is a second-stage variable flexibly adjusted according to the scene, and is the total number of model iterations;
the upper bound of the sub-problem, expressed as:
Figure RE-GDA0003479305120000295
the subproblem is a max-min bilayer structure, due to the inner layer constraint range YsCompletely unrelated to the outer layer constraint range psi, so that the inner layer min problem is solved in parallel, the worst probability distribution of the outer layer is searched according to the inner layer solving result,
Figure RE-GDA0003479305120000296
Figure RE-GDA0003479305120000297
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure RE-GDA0003479305120000298
Figure RE-GDA0003479305120000299
Figure RE-GDA00034793051200002910
in the formula, theta1And theta∞To allow maximum deviation of the probability distribution, α1And alpha∞Respectively uncertainty probability confidence under two norm constraint conditions,
Figure RE-GDA0003479305120000301
representing a set of positive real numbers, hsFor intermediate variables of construction, p0Expressing the initial probability, and K expressing the number of actual operation scenes, which is obtained by historical data;
after the main problem and the sub problem are decomposed, the solving steps are as follows:
step 1): setting an initial value, including:
the number of iterations m is 1, the lower bound L is 0, the upper bound U is + ∞,
Figure RE-GDA0003479305120000302
the superscript m indicates the number of iterations;
step 2): solving a main problem, comprising:
find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
Step 3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure RE-GDA0003479305120000303
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
Step 4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure RE-GDA0003479305120000304
And define new variables in the main problem
Figure RE-GDA0003479305120000305
And adding a correlation constraint Ys (m+1)；
Step 5): updating m to m +1, and returning to the step 2);
when iteration is terminated, the upper bound value and the lower bound value are unified, and the power distribution network accessed by the virtual power plant at the moment is determined according to the upper bound value and the lower bound valueThe optimal solution comprises the daily trading electric quantity with the VPP of the power distribution network under the uncertain environment
Figure RE-GDA0003479305120000306
And carrying in the target function minF according to the optimal solution to obtain the running cost.
Aiming at the uncertainty of renewable energy, the power distribution network distribution robust optimization model considering VPP is provided, and the power distribution network distribution robust optimization model has the following advantages:
1) the VPP model established by the method carries out most economic optimization on the output of each unit in the VPP on the premise of ensuring the operation of photovoltaic MPPT, thereby not only improving the consumption level of renewable energy sources, but also optimizing the economical efficiency of a system.
2) The power distribution network with VPP participation is optimally scheduled based on the time-of-use electricity price, so that the electricity purchasing cost of the power distribution network can be reduced, the electricity selling income is improved, and the peak clipping and valley filling can be realized.
3) The distributed robust optimization model considering the uncertainty of the distributed power supply can better balance the economy and the robustness and obtain higher benefits.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A coordinated optimization method for a virtual power plant and a power distribution network with distributed power supplies is characterized by comprising the following steps:
acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant;
inputting the power distribution network VPP distribution robust coordination optimization model to a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; the second-stage model is used for carrying out power distribution network optimization scheduling by using the result of the first-stage model, and solving to obtain daily transaction electric quantity and operation cost of the power distribution network;
a VPP internal constraint stage and a power distribution network optimization scheduling stage based on the result of the VPP internal constraint stage;
the construction of the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes comprises the following steps:
constructing a two-stage power distribution network coordination optimization model containing a VPP, wherein the VPP is a virtual power plant of a distributed power supply constructed on the basis of a distributed photovoltaic power generation system, an energy storage battery, a gas turbine and a controllable load which are connected into a power distribution network; and considering the uncertainty of photovoltaic output, and constructing a multi-discrete scene-based two-stage power distribution network VPP distribution robust coordination optimization model based on the two-stage power distribution network coordination optimization model containing VPP.
2. The coordinated optimization method for the virtual power plant and the power distribution network with the distributed power supplies according to claim 1, wherein the coordinated optimization model for the two-stage power distribution network with the VPP comprises the following steps:
the first stage model, the objective function is the running cost of the VPP in the scheduling period, and is expressed as:
Figure FDA0003357312490000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000012
cost factors for the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively,
Figure FDA0003357312490000013
the rates of adjustment of the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively;
the VPP running cost objective function in the scheduling period comprises the following constraints:
1) DG constraints
Figure FDA0003357312490000021
Figure FDA0003357312490000022
In the formula, Pi DG,minAnd Pi DG,maxRespectively DG output power
Figure FDA0003357312490000023
Upper and lower limits of (d);
Figure FDA0003357312490000024
and
Figure FDA0003357312490000025
respectively representing upward and downward climbing rate limits of the DG, wherein t and t +1 respectively represent two adjacent time periods in front and back, and the formula (1-1) and the formula (1-2) respectively represent DG output constraint and climbing rate constraint;
2) ESS constraints
Figure FDA0003357312490000026
Figure FDA0003357312490000027
In the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000028
and
Figure FDA0003357312490000029
represents the charge and discharge power of the ESS,
Figure FDA00033573124900000210
and
Figure FDA00033573124900000211
identified by 0-1 for the charging and discharging state of the ESS,
Figure FDA00033573124900000212
indicating that the device is in a charging state,
Figure FDA00033573124900000213
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time; pj ESS,maxRepresents the upper limit of the charging and discharging power of the ESS;
Figure FDA00033573124900000214
and
Figure FDA00033573124900000215
as is the charge-discharge coefficient of the ESS,
Figure FDA00033573124900000216
and
Figure FDA00033573124900000217
representing the lower limit and the upper limit of the capacity of the ESS, and respectively representing energy storage charge-discharge power limit and energy storage electric quantity constraint by an equation (1-3) and an equation (1-4);
3) SL constraint
Figure FDA00033573124900000218
Figure FDA00033573124900000219
In the formula Pi SL,down Pi SL,upRespectively SL load removal and load
Figure FDA00033573124900000220
Shifting into a maximum value; pi SL,maxThe maximum load translation amount;
Figure FDA00033573124900000221
for the load non-translatable period, equations (1-5) represent a load translation power constraint, and equations (1-6) represent a load translation power balance constraint, a load translation power total constraint and a non-translatable period constraint;
4) IL constraint
Figure FDA0003357312490000031
Figure FDA0003357312490000032
In the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000033
for the power of the call for the IL,
Figure FDA0003357312490000034
is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure FDA0003357312490000035
for the upper limit of the number of calls for IL in a scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure FDA00033573124900000314
for the non-callable period, equations (1-7) represent the IL call power constraint, and equations (1-8) represent the call count constraint, the number of consecutive callsConstraint, continuous non-calling times constraint and non-calling time constraint;
5) PV restraint
The active power of the distributed photovoltaic is set to be in a maximum power point tracking mode, the photovoltaic is connected into the power distribution network through the inverter, and therefore the reactive power of the photovoltaic is adjustable and limited by the capacity of the inverter:
Figure FDA0003357312490000036
in the formula:
Figure FDA0003357312490000037
representing the active power of the photovoltaic inverter during the period t,
Figure FDA0003357312490000038
representing the reactive power of the photovoltaic inverter during the period t,
Figure FDA0003357312490000039
is the maximum apparent power of the photovoltaic inverter;
and in the second stage model, the optimization target is the minimum cost in the operation period of the power distribution network, and the target function minF is as follows:
Figure FDA00033573124900000310
in the formula, Δ T is a time interval, and T is a scheduling period; etabuy,b、
Figure FDA00033573124900000311
Respectively the electricity price for purchasing electricity in the day ahead and the electricity price for purchasing electricity in the spot market,
Figure FDA00033573124900000312
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B、Pt R、
Figure FDA00033573124900000313
The method comprises the following steps of respectively purchasing electric quantity in the day ahead, purchasing electric quantity in the spot market and using electric load of a user;
Figure FDA0003357312490000041
calling power for the VPP, obtained by a first stage model optimization:
Figure FDA0003357312490000042
Figure FDA0003357312490000043
representing the active power of the photovoltaic in the VPP at time t,
Figure FDA0003357312490000044
representing the discharge power of the stored energy in VPP at time t,
Figure FDA0003357312490000045
representing the heavy electric power stored in the VPP at time t,
the objective function of the second stage model contains the following constraints:
1) flow restraint
Figure FDA0003357312490000046
Figure FDA0003357312490000047
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
J → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage assignment at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure FDA0003357312490000048
and
Figure FDA0003357312490000049
represents the active and reactive injection of the jth VPP;
Figure FDA00033573124900000410
indicating the reactive compensation quantity, P, of a continuous type reactive compensation devicejk,tI → j represents the set of all line end nodes j pointed to by node i as the head end,
Figure FDA00033573124900000411
representing the active load, Q, of node jjk,tRepresenting the reactive power at the head end of branch jk,
Figure FDA00033573124900000412
the PV reactive power is represented by the PV reactive power,
Figure FDA00033573124900000413
representing reactive load of node j, Vj,tRepresents the voltage magnitude at node j;
the above equation is further relaxed as a second order cone constraint, as follows:
Figure FDA00033573124900000414
2) power balance constraint
Figure FDA00033573124900000415
In the formula Pt lossThe active loss of the network is equivalent to the sum of active power injected by all nodes;
3) voltage safety constraints
Figure FDA0003357312490000051
In the formula:
Figure FDA0003357312490000052
and
Figure FDA0003357312490000053
respectively representing the allowable voltage V of the systemj,tUpper and lower limits;
4) reactive power compensator restraint
Figure FDA0003357312490000054
In the formula:
Figure FDA0003357312490000055
and
Figure FDA0003357312490000056
reactive compensation permitted for the reactive compensation device respectively
Figure FDA0003357312490000057
A lower limit and an upper limit.
3. The coordinated optimization method for the virtual power plant and the power distribution network with the distributed power supplies according to claim 2, wherein the uncertainty of the photovoltaic output is considered, and a multi-discrete-scene-based two-stage power distribution network VPP distribution robust coordinated optimization model is constructed based on the two-stage power distribution network coordinated optimization model with the VPP, and is expressed as:
Figure FDA0003357312490000058
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents a first stage variable comprising:
Figure FDA0003357312490000059
a is a cost coefficient corresponding to the output decision of different equipment; y issThe second-stage variables under the scene s comprise: vj,t，Iij,t，Pij,t，Qij,t，
Figure FDA00033573124900000510
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes corresponding to variables in the model; xisPredicting an output vector for the PV; n is a radical ofsRepresenting a limited number of discrete scenes, NsK actual scenes are obtained from the limited discrete scenes through historical data and are obtained through scene clustering method screening; equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; equation (6) represents the equality constraint of the photovoltaic power generation uncertainty predicted output.
4. The coordinated optimization method for the virtual power plant and the power distribution network containing the distributed power supply as claimed in claim 3, wherein the input is to a pre-constructed two-stage power distribution network VPP distribution robust coordinated optimization model based on multiple discrete scenes to obtain the daily transaction electric quantity and the operation cost of the power distribution network, and the method comprises the following steps:
and solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multi-discrete scene by adopting a column and constraint generation algorithm to obtain the daily trading electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment.
5. The coordinated optimization method for the virtual power plant and the power distribution network with the distributed power supplies according to claim 4, wherein the two-stage power distribution network VPP distribution robust coordinated optimization model based on the multiple discrete scenes is solved by adopting a column and constraint generation algorithm to obtain the daily transaction electric quantity and the operation cost with the VPP of the power distribution network in the uncertain environment, and the method comprises the following steps:
decomposing the problem of solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes into a main problem and a sub-problem, wherein the main problem provides a lower bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the sub-problem provides an upper bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the upper bound and the lower bound are gradually closed through continuous iteration, when the difference value of the two bounds is smaller than a preset value, the iteration is stopped, an optimal solution is returned, and the power distribution network transaction power quantity and the operation cost in the day with the VPP under an uncertain environment are obtained;
the lower bound of the main question, denoted:
Figure FDA0003357312490000061
Figure FDA0003357312490000062
in the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000063
to pass through the subproblemThe probability distribution obtained is calculated according to the probability distribution,
Figure FDA0003357312490000064
m is a second-stage variable flexibly adjusted according to the scene, and is the total number of model iterations;
the upper bound of the sub-problem, expressed as:
Figure FDA0003357312490000071
the subproblem is a max-min bilayer structure, due to the inner layer constraint range YsCompletely unrelated to the outer layer constraint range psi, so that the inner layer min problem is solved in parallel, the worst probability distribution of the outer layer is searched according to the inner layer solving result,
Figure FDA0003357312490000072
Figure FDA0003357312490000073
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure FDA0003357312490000074
Figure FDA0003357312490000075
Figure FDA0003357312490000076
in the formula, theta1And theta∞To allow for a maximum value of the deviation of the probability distribution,α1and alpha∞Respectively uncertainty probability confidence under two norm constraint conditions,
Figure FDA0003357312490000077
representing a set of positive real numbers, hsFor intermediate variables of construction, p0Expressing the initial probability, and K expressing the number of actual operation scenes, which is obtained by historical data;
after the main problem and the sub problem are decomposed, the solving steps are as follows:
step 1): setting an initial value, including:
the number of iterations m is 1, the lower bound L is 0, the upper bound U is + ∞,
Figure FDA0003357312490000078
the superscript m indicates the number of iterations;
step 2): solving a main problem, comprising:
find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
Step 3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure FDA0003357312490000081
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
Step 4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure FDA0003357312490000082
And define new variables in the main problem
Figure FDA0003357312490000083
And adding a correlation constraint Ys (m+1)；
Step 5): updating m to m +1, and returning to the step 2);
when iteration is terminated, the upper bound value and the lower bound value are unified, the optimal solution in the power distribution network accessed by the virtual power plant at the moment is determined according to the upper bound value and the lower bound value, and the optimal solution comprises the daily transaction electric quantity with the VPP of the power distribution network in an uncertain environment
Figure FDA0003357312490000084
And carrying in the target function minF according to the optimal solution to obtain the running cost.
6. A coordinated optimization system of a virtual power plant and a power distribution network containing distributed power supplies is characterized by comprising the following components:
the acquisition module is used for acquiring network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of a power distribution network accessed by a current virtual power plant;
the optimization model processing module is used for inputting network parameters, line impedance, load data, equipment parameters and an uncertain variable distribution set of the power distribution network accessed by the current virtual power plant into a pre-constructed two-stage power distribution network VPP distribution robust coordination optimization model based on multiple discrete scenes to obtain daily transaction electric quantity and operation cost of the power distribution network; the two stages comprise a first stage model and a second stage model, wherein the first stage model is used for constraining the operation domain of each period of the equipment in the VPP to obtain the integral output of each period of the VPP; the second-stage model is used for carrying out power distribution network optimization scheduling by using the result of the first-stage model, and solving to obtain daily transaction electric quantity and operation cost of the power distribution network;
a VPP internal constraint stage and a power distribution network optimization scheduling stage based on the result of the VPP internal constraint stage;
the construction of the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes comprises the following steps:
constructing a two-stage power distribution network coordination optimization model containing a VPP, wherein the VPP is a virtual power plant of a distributed power supply constructed on the basis of a distributed photovoltaic power generation system, an energy storage battery, a gas turbine and a controllable load which are connected into a power distribution network; and considering the uncertainty of photovoltaic output, and constructing a multi-discrete scene-based two-stage power distribution network VPP distribution robust coordination optimization model based on the two-stage power distribution network coordination optimization model containing VPP.
7. The method for the coordinated optimization of the virtual power plant and the power distribution network with the distributed power supplies according to claim 6, wherein the VPP-containing two-stage power distribution network coordinated optimization model comprises:
the first stage model, the objective function is the running cost of the VPP in the scheduling period, and is expressed as:
Figure FDA0003357312490000091
in the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000092
cost factors for the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively,
Figure FDA0003357312490000093
the rates of adjustment of the gas turbine DG, the interruptible load IL and the shiftable load SL, respectively;
the VPP running cost objective function in the scheduling period comprises the following constraints:
1) DG constraints
Figure FDA0003357312490000094
Figure FDA0003357312490000095
In the formula, Pi DG,minAnd Pi DG,maxRespectively DG output power
Figure FDA0003357312490000096
Above the upper part of,A lower limit;
Figure FDA0003357312490000097
and
Figure FDA0003357312490000098
respectively representing upward and downward climbing rate limits of the DG, wherein t and t +1 respectively represent two adjacent time periods in front and back, and the formula (1-1) and the formula (1-2) respectively represent DG output constraint and climbing rate constraint;
2) ESS constraints
Figure FDA0003357312490000099
Figure FDA00033573124900000910
In the formula (I), the compound is shown in the specification,
Figure FDA00033573124900000911
and
Figure FDA00033573124900000912
represents the charge and discharge power of the ESS,
Figure FDA00033573124900000913
and
Figure FDA00033573124900000914
identified by 0-1 for the charging and discharging state of the ESS,
Figure FDA00033573124900000915
indicating that the device is in a charging state,
Figure FDA00033573124900000916
indicating a discharging or idle state, the ESS cannot be charged and discharged simultaneously at the same time;
Figure FDA0003357312490000101
represents the upper limit of the charging and discharging power of the ESS;
Figure FDA0003357312490000102
and
Figure FDA0003357312490000103
as is the charge-discharge coefficient of the ESS,
Figure FDA0003357312490000104
and
Figure FDA0003357312490000105
representing the lower limit and the upper limit of the capacity of the ESS, and respectively representing energy storage charge-discharge power limit and energy storage electric quantity constraint by an equation (1-3) and an equation (1-4);
3) SL constraint
Figure FDA0003357312490000106
Figure FDA0003357312490000107
In the formula Pi SL,down Pi SL,upRespectively SL load removal and load
Figure FDA0003357312490000108
Shifting into a maximum value; pi SL,maxThe maximum load translation amount;
Figure FDA0003357312490000109
for the load non-translatable period, equations (1-5) represent a load translation power constraint, and equations (1-6) represent a load translation power balance constraint, a load translation power total constraint and a non-translatable period constraint;
4) IL constraint
Figure FDA00033573124900001010
Figure FDA00033573124900001011
In the formula (I), the compound is shown in the specification,
Figure FDA00033573124900001012
for the power of the call for the IL,
Figure FDA00033573124900001013
is a 0-1 variable, P, representing the calling state of ILi IL,maxAn upper limit for power per call;
Figure FDA00033573124900001014
for the upper limit of the number of calls for IL in a scheduling period, TmaxAnd TminRespectively the maximum continuous calling times and the minimum continuous non-called times;
Figure FDA00033573124900001015
for the non-callable period, equations (1-7) represent an IL call power constraint, equations (1-8) represent a call number constraint, a continuous non-call number constraint, and a non-callable period constraint;
5) PV restraint
The active power of the distributed photovoltaic is set to be in a maximum power point tracking mode, the photovoltaic is connected into the power distribution network through the inverter, and therefore the reactive power of the photovoltaic is adjustable and limited by the capacity of the inverter:
Figure FDA0003357312490000111
in the formula:
Figure FDA0003357312490000112
representing the active power of the photovoltaic inverter during the period t,
Figure FDA0003357312490000113
representing the reactive power of the photovoltaic inverter during the period t,
Figure FDA0003357312490000114
is the maximum apparent power of the photovoltaic inverter;
and in the second stage model, the optimization target is the minimum cost in the operation period of the power distribution network, and the target function minF is as follows:
Figure FDA0003357312490000115
in the formula, Δ T is a time interval, and T is a scheduling period; etabuy,b、
Figure FDA0003357312490000116
Respectively the electricity price for purchasing electricity in the day ahead and the electricity price for purchasing electricity in the spot market,
Figure FDA0003357312490000117
selling electricity for the distribution network; b and N are the number of load nodes of the power distribution network and the number of VPPs respectively; pt B、Pt R、
Figure FDA0003357312490000118
The method comprises the following steps of respectively purchasing electric quantity in the day ahead, purchasing electric quantity in the spot market and using electric load of a user;
Figure FDA0003357312490000119
calling power for the VPP, obtained by a first stage model optimization:
Figure FDA00033573124900001110
Figure FDA00033573124900001111
representing the active power of the photovoltaic in the VPP at time t,
Figure FDA00033573124900001112
representing the discharge power of the stored energy in VPP at time t,
Figure FDA00033573124900001113
representing the heavy electric power stored in the VPP at time t,
the objective function of the second stage model contains the following constraints:
1) flow restraint
Figure FDA00033573124900001114
Figure FDA00033573124900001115
Vj,t 2＝Vi,t 2-2(Pij,trij+Qij,txij)+Iij,t 2(rij 2+xij 2)
J → k represents the set of all line end nodes k pointed by the node j as the head end; r isijRepresenting the resistance, x, on branch ijijRepresents the reactance of branch ij; i isij,tRepresents the current on branch ij; vi,tRepresenting the voltage assignment at node i, Pij,tAnd Qij,tRespectively representing active power and reactive power at the head end of the branch ij;
Figure FDA00033573124900001116
and
Figure FDA00033573124900001117
represents the active and reactive injection of the jth VPP;
Figure FDA0003357312490000121
indicating the reactive compensation quantity, P, of a continuous type reactive compensation devicejk,tI → j represents the set of all line end nodes j pointed to by node i as the head end,
Figure FDA0003357312490000122
representing the active load, Q, of node jjk,tRepresenting the reactive power at the head end of branch jk,
Figure FDA0003357312490000123
the PV reactive power is represented by the PV reactive power,
Figure FDA0003357312490000124
representing reactive load of node j, Vj,tRepresents the voltage magnitude at node j;
the above equation is further relaxed as a second order cone constraint, as follows:
Figure FDA0003357312490000125
2) power balance constraint
Figure FDA0003357312490000126
In the formula Pt lossThe active loss of the network is equivalent to the sum of active power injected by all nodes;
3) voltage safety constraints
Figure FDA0003357312490000127
In the formula:
Figure FDA0003357312490000128
and
Figure FDA0003357312490000129
respectively representing the allowable voltage V of the systemj,tUpper and lower limits;
4) reactive power compensator restraint
Figure FDA00033573124900001210
In the formula:
Figure FDA00033573124900001211
and
Figure FDA00033573124900001212
reactive compensation permitted for the reactive compensation device respectively
Figure FDA00033573124900001213
A lower limit and an upper limit.
8. The coordinated optimization method for the virtual power plant and the power distribution network containing the distributed power supplies according to claim 7, wherein the optimization model processing module comprises a model construction module for constructing a two-stage power distribution network VPP distribution robust coordinated optimization model based on multiple discrete scenes, and the model construction module is represented as follows:
Figure FDA00033573124900001214
Ax≤c (2)
Ex+Fys≤d (3)
||Mys+o||2≤qTys+n (4)
Hys≤j (5)
Gys＝ξs (6)
in formula (1): x represents a first stage variable comprising:
Figure FDA0003357312490000131
a is a cost coefficient corresponding to the output decision of different equipment; y issThe second-stage variables under the scene s comprise: vj,t，Iij,t，Pij,t，Qij,t，
Figure FDA0003357312490000132
psIs the probability of occurrence of scene s; a, c, d, E, F, G, H, j, M, n, o and q are all vectors or coefficient matrixes corresponding to variables in the model; xisPredicting an output vector for the PV; n is a radical ofsRepresenting a limited number of discrete scenes, NsK actual scenes are obtained from the limited discrete scenes through historical data and are obtained through scene clustering method screening; equation (2) represents all constraints associated with the first stage variables; equation (3) represents the constraint associated with the first stage variable and the second stage variable; equation (4) represents a second order cone relaxation constraint; equation (5) represents the relevant constraint of the second stage variable; equation (6) represents the equality constraint of the photovoltaic power generation uncertainty predicted output.
9. The system for coordination and optimization of the virtual power plant and the power distribution network with the distributed power supplies according to claim 8, wherein the power distribution network intra-day transaction power quantity and the operation cost are obtained by inputting the power distribution network VPP distribution robust coordination and optimization model to a pre-constructed two-stage power distribution network based on multiple discrete scenes, and the system comprises:
and solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multi-discrete scene by adopting a column and constraint generation algorithm to obtain the daily trading electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment.
10. The system of claim 9, wherein the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenarios is solved by a column and constraint generation algorithm to obtain the daily transaction electric quantity and the operating cost of the power distribution network with the VPP under the uncertain environment, and the system comprises:
decomposing the problem of solving the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes into a main problem and a sub-problem, wherein the main problem provides a lower bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the sub-problem provides an upper bound for the two-stage power distribution network VPP distribution robust coordination optimization model based on the multiple discrete scenes, the upper bound and the lower bound are gradually closed through continuous iteration, when the difference value of the two bounds is smaller than a preset value, the iteration is stopped, an optimal solution is returned, and the power distribution network transaction power quantity and the operation cost in the day with the VPP under an uncertain environment are obtained;
the lower bound of the main question, denoted:
Figure FDA0003357312490000141
Figure FDA0003357312490000142
in the formula (I), the compound is shown in the specification,
Figure FDA0003357312490000143
for the probability distribution found by the sub-problem,
Figure FDA0003357312490000144
m is a second-stage variable flexibly adjusted according to the scene, and is the total number of model iterations;
the upper bound of the sub-problem, expressed as:
Figure FDA0003357312490000145
the subproblem is a max-min bilayer structure, due to the inner layer constraint range YsCompletely unrelated to the outer layer constraint range psi, so that the inner layer min problem is solved in parallel, the worst probability distribution of the outer layer is searched according to the inner layer solving result,
Figure FDA0003357312490000146
Figure FDA0003357312490000147
Ψ is a set interval of the scene probability distribution, representing the confidence set bounded by the 1-norm and ∞ -norm:
Figure FDA0003357312490000148
Figure FDA0003357312490000149
Figure FDA0003357312490000151
in the formula, theta1And theta∞To allow maximum deviation of the probability distribution, α1And alpha∞Respectively uncertainty probability confidence under two norm constraint conditions,
Figure FDA0003357312490000152
representing a set of positive real numbers, hsFor intermediate variables of construction, p0Expressing the initial probability, and K expressing the number of actual operation scenes, which is obtained by historical data;
after the main problem and the sub problem are decomposed, the solving steps are as follows:
step 1): setting an initial value, including:
number of iterations m equals 1, belowThe cutoff value L is equal to 0, the upper cutoff value U is equal to + ∞,
Figure FDA0003357312490000153
the superscript m indicates the number of iterations;
step 2): solving a main problem, comprising:
find the optimal solution x*And L(m)*And updating the lower limit value L ═ max { L, L(m)*}；
Step 3): solving the result x in the main problem*Solving the subproblems on the basis of the obtained probability distribution of the worst scene
Figure FDA0003357312490000154
And U(m)*And updating the lower bound value U ═ min { U, U ═(m)*}；
Step 4): judging whether the upper and lower bound gaps are smaller than the convergence precision, if so, stopping iteration and returning to the optimal solution x*(ii) a If not, updating scene probability distribution in the main problem
Figure FDA0003357312490000155
And define new variables in the main problem
Figure FDA0003357312490000156
And adding a correlation constraint Ys (m+1)；
Step 5): updating m to m +1, and returning to the step 2);
when iteration is terminated, the upper bound value and the lower bound value are unified, the optimal solution in the power distribution network accessed by the virtual power plant at the moment is determined according to the upper bound value and the lower bound value, and the optimal solution comprises the daily transaction electric quantity with the VPP of the power distribution network in an uncertain environment
Figure FDA0003357312490000157
And carrying in the target function minF according to the optimal solution to obtain the running cost.
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