CN113762622A - Virtual power plant access point and capacity optimization planning method - Google Patents

Virtual power plant access point and capacity optimization planning method Download PDF

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CN113762622A
CN113762622A CN202111056901.9A CN202111056901A CN113762622A CN 113762622 A CN113762622 A CN 113762622A CN 202111056901 A CN202111056901 A CN 202111056901A CN 113762622 A CN113762622 A CN 113762622A
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王素
高赐威
郭明星
林固静
吕冉
王晓晖
丁建勇
陈涛
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Southeast University
State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention discloses a virtual power plant access point and a capacity optimization planning method, which comprises planning of an upper layer virtual power plant access point, multi-target operation optimization of a lower layer power distribution network and a solution method for converting multi-target into single-target optimization based on an NBI method. According to the invention, a virtual power plant access point and capacity double-layer optimization planning model is constructed by taking nodes of 10kV and above as virtual generator sets, the upper layer takes the minimum planning cost of a power distribution network as a target, the virtual generator set accessed into the virtual power plant is planned on the premise of meeting the requirement of no capacity expansion of a transformer substation in a planning year, the lower layer model adopts an NBI method to convert double targets into a single target for optimization calculation according to the upper layer planning result, the result is fed back to the upper layer, and the virtual power plant access point and capacity optimization planning scheme is formed through interactive iteration of the upper layer and the lower layer. The invention realizes the full utilization of resources, reduces the network loss and improves the utilization rate of equipment.

Description

Virtual power plant access point and capacity optimization planning method
Technical Field
The invention relates to the technical field of power distribution network planning of a power system, in particular to a virtual power plant access point and a capacity optimization planning method.
Background
In recent years, along with the deterioration of the power load characteristic in economic development, the highest load of the whole network in summer is highly innovative, and in order to meet the load demand of a few load peak days, the capacity expansion transformation needs to be carried out on a transformer substation, a matching line and the like in advance, so that the low equipment utilization rate is caused. The appearance of virtual power plant technology, through aggregating a large amount of flexible adjustable resources of user side, carry out virtual electricity generation in peak hour, realize the peak clipping, improve the utilization ratio of transformer substation equipment. The construction of the virtual power plant has the investment cost of user-side control terminal equipment, the cost is overhigh due to the access of excessive resources, the adjustment potentials of the resources at different positions are different, and the influences of the participation of the resources in the virtual power plant on the power distribution network are also nearly the same, so that the access point and the capacity of the power distribution network are optimized and planned.
Disclosure of Invention
The invention aims to provide a virtual power plant access point and a capacity optimization planning method, and in order to optimize power distribution network planning, the planning method saves the investment cost of a distribution network, improves the utilization rate of equipment, improves the voltage level of the distribution network and reduces the network loss on the basis of meeting the planning requirement of the distribution network.
The purpose of the invention can be realized by the following technical scheme:
a virtual power plant access point and capacity optimization planning method comprises planning of an upper layer virtual power plant access point, multi-target operation optimization of a lower layer power distribution network and a solution method for multi-target conversion to single target optimization based on an NBI method.
Further, the construction of the node virtual generator set is as follows: nodes with regulation potential of 10kV or more of the power distribution network form a virtual generator set; the 10kV transformer substation where the adjusting resources below 10kV are located serves as an equivalent node, and the equivalent node aggregates all the adjusting resources below the equivalent node to construct a virtual generator set.
Further, the upper virtual power plant optimization planning model is as follows:
1) the virtual power plant access point planning is carried out by taking the minimum planning cost of the power distribution network as a target, and the target function is as follows:
min F＝-C1+C2+C3
in the formula, F controls the cost of the distribution network in the whole life cycle of the terminal; c1In order to delay the benefit of upgrading and expanding the power grid; c2Controlling terminal equipment cost for the access point; c3Subsidy cost for invoking virtual power plant resources;
2) and transmitting the planning result of the virtual generator set accessed to the virtual power plant to the lower model.
Further, the construction constraints of the upper-layer virtual power plant optimization planning model comprise: power balance constraints, node load constraints, capacity-to-load ratio constraints, and lifetime constraints.
Further, the lower-layer power distribution network multi-objective optimization operation model is as follows:
1) optimization objective 1: targeting minimum loss of power distribution network
Figure BDA0003255017220000021
In the formula, f is the active loss of the system; lhkIs a line between the node h and the node k; phkAnd QhkFlow to node for node iActive and reactive power of j; u shapehIs the voltage of node h; r ishkIs the resistance of the line between node h and node k;
2) optimization objective 2: targeting minimum voltage deviation of distribution network
Figure BDA0003255017220000022
In the formula of UrefA network reference voltage is provided for the distribution network; n is a radical ofDIs a collection of nodes with higher requirements on voltage quality.
Further, the construction constraint of the lower-layer power distribution network multi-objective optimization operation model comprises the following steps: power flow constraints, node load constraints, voltage constraints, and line transmission power constraints.
Further, the NBI method is adopted to solve the multi-objective optimization of the lower-layer power distribution network, and the specific steps comprise:
s01, the dimension and the physical meaning of the two optimization targets are different, and the two targets are matched through normalization;
s02, evenly dividing the Utobond line, and mapping each equant point on the Utobond line to a pareto curve along the direction of a unit normal vector to obtain a corresponding pareto optimal solution;
and S03, converting the lower-layer multi-objective optimization into single-objective optimization through intercept optimization.
Further, the optimal solution of the access point and the capacity planning is finally obtained through the interactive iteration of the solving result between the two-layer models.
Further, the specific steps of solving the two-layer model include:
s1, generating initial positions of particles by an upper layer virtual power plant optimization planning model according to the position and the adjustable potential of a virtual generator set and regional annual load prediction in a planning year in the future by adopting a particle swarm algorithm, so as to determine a planning scheme of the access point;
s2, performing multi-objective optimization by the aid of the lower-layer power distribution network multi-objective optimization operation model according to the access point planning scheme determined by the upper layer and the minimum calling capacity constraint required by each transformer substation by adopting an NBI (negative bias average) method aiming at two scenes of a typical day and a maximum load day, and feeding back output results of each virtual generator set to the upper-layer model;
s3, calculating the fitness of the corresponding particles by the upper layer model, judging the convergence condition of the algorithm, and updating the position of each particle;
and S4, outputting the upper and lower layer optimal solution, and determining the access point and capacity optimal planning scheme.
The invention has the beneficial effects that:
1. the virtual power plant calls the existing resources, so that the resources can be fully utilized;
2. according to the method, the situation that the capacity-expansion transformation is urgently needed due to the fact that the capacity-load ratio of the transformer substation is too high can be relieved by optimally planning the access point of the virtual power plant, the utilization rate of equipment is improved, and the capacity-expansion transformation investment is reduced;
3. according to the virtual power plant access point planning scheme, the voltage level of the distribution network can be improved through optimized calling of access resources, and the network loss is reduced;
4. the invention adopts the NBI method to solve the multi-objective optimization, so that two optimization objectives with different dimensions can also generate a uniformly distributed pareto frontier solution set.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a virtual power plant access point planning of the present invention;
FIG. 2 is a virtual power plant double-layer planning optimization solution flow of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
First, upper model
1. Objective function
The virtual power plant is built by utilizing the adjustable resources on the power distribution network side, the upgrading and the extension of the power distribution network are delayed, the minimum planning cost of the power distribution network is taken as a target, and the calculated cost and the calculated benefit are as follows: postponing the benefits of upgrading and expanding the power grid, the cost of each access point controlling the terminal equipment and the cost of calling the resources of the virtual power plant, i.e.
min F＝-C1+C2+C3
In the formula, F is the distribution network cost in the whole life cycle of the control terminal; c1In order to delay the benefit of upgrading and expanding the power grid; c2Controlling terminal equipment cost for the access point; c3Subsidy cost for invoking virtual power plant resources.
1) Delay the benefit of upgrading and expanding the power grid
For a power distribution network, a virtual power plant is constructed to generate a corresponding indirect application value, wherein the delay of upgrading and reconstruction of power grid equipment is a main indirect income source of the virtual power plant in planning the power distribution network, and is mainly divided into a residual value time value for delaying retirement and a time value income for delaying expansion investment of a transformer substation, and a mathematical model of the method is as follows:
C1＝Cretire+Cinv
in the formula, CretireThe time value benefit for delaying retirement residual value; cinvThe time value and the income for delaying the capacity expansion investment of the transformer substation are achieved.
The residual value time value benefit of deferring retirement is expressed as:
Figure BDA0003255017220000051
in the formula, ωiThe decommissioning residual value rate of the transformer substation i is obtained; ctran,iThe original value of the transformer substation i; drThe current rate is the current rate; t is t0,iAnd tiThe number of retired years before and after the delay of the transformer substation i respectively.
The time value gain for delaying the capacity expansion investment of the transformer substation is expressed as:
Figure BDA0003255017220000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000053
expanding the capacity and transforming cost for the transformer substation i;
Figure BDA0003255017220000054
and transforming the cost for the corresponding line of the transformer substation i.
2) Controlling terminal equipment cost
For the condition that the intelligent control terminal equipment needs to be installed for the adjustable resources under the nodes accessed into the virtual power plant, the equipment cost and the operation maintenance cost are assumed to be in linear relation with the terminal power, namely
Figure BDA0003255017220000055
In the formula, NDRIn order to provide a virtual generator set node set with adjustable resources, the method is equivalent to a virtual generator set by users of 10kV or more, and the resources of the users below 10kV form a virtual generator set by aggregating subordinate adjustable resources in a 10kV transformer substation where the users are located; x is the number ofijWhether a virtual generator set j of a subordinate node of a transformer substation i is provided with a variable of 0-1 of an intelligent control terminal device or not, wherein 0 represents that the node is not provided, and 1 represents that the node is provided with an intelligent terminal gammaDRThe unit power cost of the intelligent terminal equipment;
Figure BDA0003255017220000056
and (4) virtualizing the maximum callable power of the generator set j for the subordinate node of the transformer substation i.
3) Invoking virtual Power plant costs
Figure BDA0003255017220000061
Figure BDA0003255017220000062
Wherein m is the number of days of maximum load occurrence in one year; d is the number of hours of maximum load occurring during the day; c is the average cost of calling the virtual power plant resources;
Figure BDA0003255017220000063
calling the power of a subordinate virtual generator set for the nth transformer substation i, wherein the subordinate virtual generator set is directly accessed to the resource of the transformer substation i and does not comprise the resource in the subordinate transformer substation;
Figure BDA0003255017220000064
the calling power of a virtual generator set j is set for a subordinate node of the nth-year transformer station i;
Figure BDA0003255017220000065
is NDRThe subset of (b) represents a virtual generator set node set with adjustable resources under the substation i.
2. Constraint conditions
1) Power balance constraint
The power balance should take into account the annual load growth rate of the area supplied by the substation, i.e.
Figure BDA0003255017220000066
Figure BDA0003255017220000067
In the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000068
the power supply power of the nth-year transformer station i; piLoad carried by the transformer substation i; alpha is alphaiThe annual load growth rate of a power supply area for a transformer substation i; n is a radical ofkRepresent belonging to the same upper partA set of substations of a level substation.
2) Node load constraints
Figure BDA0003255017220000069
3) Capacity to load ratio constraint
Figure BDA00032550172200000610
In the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000071
the capacity-load ratio of the nth-year transformer station i; sT,iThe capacity of a main transformer of a transformer substation is obtained;
Figure BDA0003255017220000072
the annual maximum load in the power supply area of the nth-year transformer station i is obtained;
Figure BDA0003255017220000073
and (5) planning a minimum capacity-load ratio for the transformer substation i.
4) Life restraint
t0,i≤ti≤Ti
In the formula, TiThe remaining life span of substation i.
Second, lower layer model
When the virtual power plant performs virtual power generation, the network loss and the voltage level of the distribution network are improved, so that the lower layer constructs a multi-objective optimization model by respectively taking the minimum system network loss and the minimum voltage deviation as targets.
1. Objective function
1) Minimum loss of power distribution network
When the virtual power plant is called to perform virtual power generation, the influence on the transmission power on the line can be generated, the active loss of the network can be reduced, and the minimum network loss of the power distribution network is taken as the optimization target
Figure BDA0003255017220000074
In the formula, f is the active loss of the system; lhkIs a line between the node h and the node k; phkAnd QhkActive and reactive power flowing to node j for node i; u shapehIs the voltage of node h; r ishkIs the resistance of the line between node h and node k.
2) Distribution network voltage deviation minimization
Partial nodes in the distribution network area have higher voltage quality requirements, the voltage level of the distribution network is improved by calling virtual power plant resources, and the minimum voltage deviation of the partial nodes is an optimization target
Figure BDA0003255017220000075
In the formula of UrefA network reference voltage is provided for the distribution network; n is a radical ofDIs a collection of nodes with higher requirements on voltage quality.
2. Constraint conditions
1) Flow restraint
Figure BDA0003255017220000081
Figure BDA0003255017220000082
In the formula, xhkIs the reactance of the line between node h and node k; pkAnd QkRespectively the active power and the reactive power flowing into the node k; u (k) is the set of nodes for which power flows to node k; v (k) is the set of nodes to which node k power flows.
2) Node load constraints
For the node of the virtual generator set with the adjustable resources, the power of the node should meet the following conditions:
Figure BDA0003255017220000083
in the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000084
the power of the node h is the power of the virtual power plant in calling;
Figure BDA0003255017220000085
adjusting power for node h
3) Voltage confinement
Figure BDA0003255017220000086
In the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000087
and
Figure BDA0003255017220000088
respectively, the lower limit and the upper limit of the voltage amplitude of the node i.
4) Line transmission power constraint
Figure BDA0003255017220000089
In the formula (I), the compound is shown in the specification,
Figure BDA00032550172200000810
is a line lhkThe maximum value of the transmission power.
3. NB I-based multi-target processing
1) Target normalization
The dimension and the physical meaning of the two optimized targets are different, so that the two targets need to be matched through normalization. The normalization process is as follows:
Figure BDA0003255017220000091
Figure BDA0003255017220000092
in the formula, PDRThe output vector of the virtual generator set is formed by the output of each virtual generator set; f. ofmaxAnd fminRespectively a maximum value and a minimum value of the network loss when the voltage optimization target is not considered; delta UmaxAnd Δ UminThe maximum and minimum values of the voltage deviation are respectively the maximum and minimum values without considering the grid loss optimization target.
After normalization, the coordinates of the two end points of the pareto front are divided by M1b(0,0) and M2b(1,1), the connecting line of the two short points is a Utobang line, and one unit normal vector of the Utobang line is
Figure BDA0003255017220000093
2) Normal vector projection
The two endpoints of the pareto front (pareto curve) coincide with the two endpoints of the utopia line. After the utopia line is equally divided, x is divided for each equally divided point on the utopia lineabAlong a unit normal vector
Figure BDA0003255017220000094
Direction mapping to point M on pareto curveabIs the corresponding pareto optimal solution. Due to point xabAnd point MabThe two are in linear mapping relation, when the equal division point x on the Utobond lineabWhen the intervals are uniform, pareto optimal solution sets which are uniformly distributed in a solution space can be obtained.
D equally dividing the Utobonnaise line, and dividing the a-th equally dividing point x under the normalized unit coordinateabEdge of
Figure BDA0003255017220000097
Projecting the direction on a pareto curve to obtain a corresponding pareto leading edge endpoint MabThe coordinates are
Figure BDA0003255017220000095
In the formula (I), the compound is shown in the specification,
Figure BDA0003255017220000096
is the a-th bisector point xabThe coordinates of (a); daIs divided into equal division points xabAnd a projection point MabThe distance between them.
3) Intercept optimization
According to the three formulas, the compound can be obtained
Figure BDA0003255017220000101
Figure BDA0003255017220000102
From the pareto-optimum condition, for an equally divided point xabWhen mapping point MabOn the pareto curve, D must be presentaThe maximum value is concluded. Therefore, the multi-objective optimization problem of the network loss and the voltage deviation can be converted into the calculation of DaSingle target optimization problem of maximum value. The transformed lower-layer multi-objective optimization model can be expressed as follows:
min(-Da)
Figure BDA0003255017220000103
three-layer and double-layer model solving process
As shown in fig. 2, the specific process of solving the two-layer model is as follows:
firstly, the upper layer adopts a particle swarm algorithm, according to the position and the adjustable potential of a virtual generator set, according to the annual load prediction of a region in the future planning year, the upper layer model generates the initial position of particles, and therefore the planning scheme of an access point is determined;
secondly, the lower model performs multi-objective optimization by adopting an NBI method aiming at two scenes of a typical day and a maximum load day according to an access point planning scheme determined by the upper layer and the minimum calling capacity constraint required by each transformer substation, and feeds back the output result of each virtual generator set to the upper model;
calculating the fitness of corresponding particles by the upper layer model, judging the convergence condition of the algorithm, and updating the position of each particle;
and fourthly, outputting the optimal solution of the upper layer and the lower layer, and determining the optimal planning scheme of the access point and the capacity.
According to the invention, nodes of 10kV and above are taken as virtual generator sets, a virtual power plant access point and capacity double-layer optimization planning model is constructed, the upper layer takes the minimum planning cost of a power distribution network as a target, the virtual generator set accessed into the virtual power plant is planned on the premise of meeting the requirement of no capacity expansion of a transformer substation in a planning year, the lower layer model adopts an NBI method to convert double targets into a single target for optimization calculation according to the upper layer planning result, the result is fed back to the upper layer, a virtual power plant access point and capacity optimization planning scheme is formed through interactive iteration of the upper layer and the lower layer, and FIG. 1 is a virtual power plant access point planning diagram.
The invention discloses a virtual power plant access point and a capacity optimization planning method, which comprises planning of an upper layer virtual power plant access point, multi-target operation optimization of a lower layer power distribution network and a solution method for converting multiple targets into single target optimization based on an NBI (negative bias potential indicator) method, as described in the patent.
The invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments of the invention, other than those specifically set forth herein, are equally possible within the scope of this invention, as defined by the appended patent claims.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (9)

1. A virtual power plant access point and capacity optimization planning method is characterized by comprising planning of an upper layer virtual power plant access point, multi-target operation optimization of a lower layer power distribution network and a multi-target-to-single-target optimization solving method based on an NBI method.
2. The virtual power plant access point and capacity optimization planning method according to claim 1, wherein the node virtual generator set is constructed by: nodes with regulation potential of 10kV or more of the power distribution network form a virtual generator set; the 10kV transformer substation where the adjusting resources below 10kV are located serves as an equivalent node, and the equivalent node aggregates all the adjusting resources below the equivalent node to construct a virtual generator set.
3. The virtual power plant access point and capacity optimization planning method according to claim 1, wherein the upper virtual power plant optimization planning model is:
1) the virtual power plant access point planning is carried out by taking the minimum planning cost of the power distribution network as a target, and the target function is as follows:
minF＝-C1+C2+C3
in the formula, F controls the cost of the distribution network in the whole life cycle of the terminal; c1In order to delay the benefit of upgrading and expanding the power grid; c2Controlling terminal equipment cost for the access point; c3Subsidy cost for invoking virtual power plant resources;
2) and transmitting the planning result of the virtual generator set accessed to the virtual power plant to the lower model.
4. The virtual power plant access point and capacity optimization planning method of claim 3, wherein the construction constraints of the upper virtual power plant optimization planning model comprise: power balance constraints, node load constraints, capacity-to-load ratio constraints, and lifetime constraints.
5. The virtual power plant access point and capacity optimization planning method according to claim 3, wherein the lower-layer power distribution network multi-objective optimization operation model is:
1) optimization objective 1: targeting minimum loss of power distribution network
Figure FDA0003255017210000011
In the formula, f is the active loss of the system; lhkIs a line between the node h and the node k; phkAnd QhkActive and reactive power flowing to node j for node i; u shapehIs the voltage of node h; r ishkIs the resistance of the line between node h and node k;
2) optimization objective 2: targeting minimum voltage deviation of distribution network
Figure FDA0003255017210000021
In the formula of UrefA network reference voltage is provided for the distribution network; n is a radical ofDIs a collection of nodes with higher requirements on voltage quality.
6. The virtual power plant access point and capacity optimization planning method according to claim 5, wherein the construction constraints of the lower-layer power distribution network multi-objective optimization operation model comprise: power flow constraints, node load constraints, voltage constraints, and line transmission power constraints.
7. The virtual power plant access point and capacity optimization planning method according to claim 5, wherein the NBI method is adopted to solve the multi-objective optimization of the lower-layer power distribution network, and the specific steps include:
s01, the dimension and the physical meaning of the two optimization targets are different, and the two targets are matched through normalization;
s02, evenly dividing the Utobond line, and mapping each equant point on the Utobond line to a pareto curve along the direction of a unit normal vector to obtain a corresponding pareto optimal solution;
and S03, converting the lower-layer multi-objective optimization into single-objective optimization through intercept optimization.
8. The virtual power plant access point and capacity optimization planning method according to claim 5, wherein the optimal solution of the access point and the capacity planning is finally obtained through solution result interactive iteration between the two-layer models.
9. The virtual power plant access point and capacity optimization planning method according to claim 8, wherein the specific steps of the two-layer model solution include:
s1, generating initial positions of particles by an upper layer virtual power plant optimization planning model according to the position and the adjustable potential of a virtual generator set and regional annual load prediction in a planning year in the future by adopting a particle swarm algorithm, so as to determine a planning scheme of the access point;
s2, performing multi-objective optimization by the aid of the lower-layer power distribution network multi-objective optimization operation model according to the access point planning scheme determined by the upper layer and the minimum calling capacity constraint required by each transformer substation by adopting an NBI (negative bias average) method aiming at two scenes of a typical day and a maximum load day, and feeding back output results of each virtual generator set to the upper-layer model;
s3, calculating the fitness of the corresponding particles by the upper layer model, judging the convergence condition of the algorithm, and updating the position of each particle;
and S4, outputting the upper and lower layer optimal solution, and determining the access point and capacity optimal planning scheme.
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