CN108695853B - Active power distribution network optimal scheduling model and method considering uncertainty of information system - Google Patents
Active power distribution network optimal scheduling model and method considering uncertainty of information system Download PDFInfo
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Abstract
The invention relates to an active power distribution network optimization scheduling model and method considering uncertainty of an information system, and the method comprises the following specific steps: constructing a power generation transfer factor expressing the relationship between the power change of the renewable energy source and the flexibility provided by a superior power grid and the flexibility provided by a controllable load; solving an optimal solution of the conventional optimal power flow model of the power distribution network by adopting the conventional optimal power flow model of the power distribution network, and constructing a sensitivity factor matrix expressing linear relations between the node injection power variation and the real part and the imaginary part of the node voltage, and between the branch power flow active variation and the reactive variation; constructing an optimal power flow model based on opportunity constraint by combining the steps, converting the optimal power flow model into a deterministic optimal power flow model, solving a deterministic optimal power flow problem, and updating a sensitivity factor matrix; judging whether the optimal power flow model converted into the certainty is converged or not, and if so, obtaining an optimal solution; if not, returning to the previous step.
Description
Technical Field
The invention belongs to the technical field of active power distribution network scheduling in a non-ideal communication environment, and particularly relates to an active power distribution network optimal scheduling model and method considering uncertainty of an information system.
Background
At present, as the permeability of renewable energy sources in medium-voltage and low-voltage power distribution networks is continuously improved, the volatility and randomness of the renewable energy sources bring great challenges to the safe operation of the power distribution networks. In the technical field of power distribution networks, an active power distribution network aiming at accommodating controllable devices such as distributed power supplies, energy storage devices and controllable loads is an important development direction of future intelligent power distribution networks, and demand response based on an information communication technology and an advanced measurement system is a research hotspot in the field of active power distribution networks.
However, there is a certain deficiency in the flexibility of regulation to compensate for the renewable energy output. How to regulate and control flexibility to compensate the output of the renewable energy sources in the prior art is artificially specified and is not combined with the actual situation of a network. Due to the defects of the renewable energy output prediction technology, the improvement of the permeability of the renewable energy obviously increases the flexibility requirement of the power grid. If the flexibility is provided entirely by a conventional generator set, the value of renewable energy will be greatly reduced. In the requirement of flexibility of the power grid, the demand response is a new action that the power consumer achieves balance of supply and demand by changing the power demand of the power consumer, the willingness and the capability of the consumer to actively participate in the regulation and control of the power grid are reflected, and the flexibility can be provided for the power distribution network; the controllable load represented by the constant-temperature control load is an important component of demand response and is a flexible resource of the active power distribution network. Therefore, how to provide an optimal scheduling method with flexibility by controllable loads is a problem to be considered in solving the flexibility of renewable energy sources in an active power distribution network.
Secondly, there is also a certain deficiency in the existing physical-information coupled active distribution network. In physically-information coupled active power distribution networks, advanced measurement systems including two-way communication capabilities are the basis for demand response. The physical meaning of the two-way communication is uploading of state information and issuing of control information; wherein, the state information comprises the on-off state of the controllable load, the indoor temperature and the like; the control information includes switching commands for the controllable load, etc. However, the communication environment of the actual power distribution network is not ideal, and packet loss, delay, error code and the like can occur in the information transmission process.
In summary, at present, the deficiencies of researching demand response in an active power distribution network are mainly as follows:
1) research work for comprehensively considering various uncertainties such as communication uncertainty and renewable energy power uncertainty in a physics-information coupled smart grid is lacked.
2) How to regulate and control the output of the renewable energy sources is specified artificially, and the actual situation of the network is not combined.
An effective solution to the above problems is lacking.
Disclosure of Invention
In order to solve the problems, the problem that the demand response research work in the active power distribution network is insufficient in the prior art is solved, and the active power distribution network optimization scheduling model and the method considering the uncertainty of the information system are provided, wherein the probability of constraint violation at the lower limit of various uncertain factors is realized by the active power distribution network optimization scheduling model considering the uncertainty of the information system, and the balance between economy and safe operation of the power system is substantially realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
an active power distribution network optimal scheduling model considering uncertainty of an information system adopts an optimal power flow model based on opportunity constraint; the optimal power flow model based on opportunity constraint assumes that the renewable energy power prediction error, the upper limit of the controllable load and the lower limit of the controllable load are all in accordance with normal distribution, and is constructed according to a node injection power variation vector formed by power generation transfer factors and a sensitivity factor matrix constructed by adopting the optimal solution of the optimal power flow model of the power distribution network.
Furthermore, the power generation transfer factor expresses the relationship between the power change of the renewable energy source and the flexibility provided by a superior power grid and the flexibility provided by a controllable load;
the sensitivity factor matrix expresses the linear relation between the node injection power variation and the real part and the imaginary part of the node voltage, and the branch power flow active variation and the reactive variation.
The invention provides an active power distribution network optimization scheduling model and method considering uncertainty of an information system in order to overcome the problem of insufficient research work of demand response in an active power distribution network in the prior art, wherein the active power distribution network optimization scheduling method considering uncertainty of the information system adopts the upper limit and the lower limit of controllable load aggregate power as uncertain parameters expressing influence of communication uncertainty, assumes that a renewable energy power prediction error obeys normal distribution, and adopts an evaluation method of sensitivity factors, namely, an approximate and linear relation exists between deviation of node power injection and node voltage and branch flow near an expected operation point. And making a decision of flexibility provided by a superior power grid and a controllable load and following the output change of the renewable energy according to the network characteristics.
In order to achieve the purpose, the invention adopts the following technical scheme:
an active power distribution network optimal scheduling method considering uncertainty of an information system is based on an active power distribution network optimal scheduling model considering uncertainty of the information system, and the method specifically comprises the following steps:
(1) constructing a power generation transfer factor, wherein the power generation transfer factor expresses the relationship between the power change of renewable energy sources and the flexibility provided by a superior power grid and the flexibility provided by a controllable load;
(2) solving an optimal solution of a conventional optimal power flow model of the power distribution network by adopting the conventional optimal power flow model of the power distribution network, and constructing a sensitivity factor matrix, wherein the sensitivity factor matrix expresses the linear relation between the variable quantity of node injection power and the real part and the imaginary part of node voltage as well as the active variable quantity and the reactive variable quantity of branch power flow;
(3) constructing an optimal power flow model based on opportunity constraint by combining the step (1) and the step (2), converting the optimal power flow model into a deterministic optimal power flow model, solving a deterministic optimal power flow problem, and updating a sensitivity factor matrix;
(4) judging whether the optimal power flow model converted into the certainty is converged or not, and if so, obtaining an optimal solution; and if not, returning to the step (3).
Further, the specific steps of constructing the power generation transfer factor in the step (1) are as follows:
according to the formula Δ Pinj＝D·ΔPωConstruction of Power Generation transfer factor
Wherein the content of the first and second substances,
representation collection
The change in the active injection of the medium node,
represents a set consisting of a superior grid connection node, a controllable load node, and a renewable energy node,
the elements are arranged and integrated according to the sequence of the three
The number of elements of (2) is NBThe number of the renewable energy source nodes is NωLet N stand forP＝NB-Nω；
The arrangement order of elements in (1) and
the arrangement sequence of the elements is consistent;
the active power variation of a node connected with a superior power grid is represented, namely the flexibility provided by the superior power grid is represented;
representing the variable quantity of the active power of the controllable load node, namely the flexibility provided by the controllable load;
representing the change of active power of the renewable energy source node;
is a matrix formed by combining variables and constants;
wherein, wmRepresenting the change of the active power of the mth renewable energy source node;
wherein, line 1 to NPThe behavior is variable, and the elements of each row are equal, with dq(q＝1,…,NP) It is shown that,
the rest of NωLine NωThe column elements form NωAn order unit matrix.
Further, the conventional optimal power flow model of the power distribution network in the step (2) is as follows:
min PUNC·Δt
wherein, PUNCΔ t denotes the power P in the scheduling period Δ tUNCThe purchase of electricity from an upper-level grid.
Furthermore, in the step (2), a plurality of constraint conditions are provided for the conventional optimal power flow model of the power distribution network, and the constraint conditions include power flow constraint, upper-level power grid and controllable load reactive power injection limitation, upper-level power grid and controllable load active power injection limitation, node voltage constraint and branch power flow constraint.
Further, in the step (2)
The power flow constraint is an active and reactive injection equation of the power distribution network node:
wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
Pk、Qkrespectively represents the node active injection quantity and the node reactive injection quantity, and V represents the node voltage.
Further, in the step (2)
And limiting the upper-level power grid and the controllable load reactive power injection:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
the maximum value of the reactive injection quantity of the upper-level power grid and the controllable load.
Further, in the step (2)
And limiting the upper-level power grid and the controllable load active power injection:
wherein the content of the first and second substances,
representing a set of superior grid connection nodes, controllable load nodes, PkThe active injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the active injection quantity of the upper-level power grid and the controllable load,
the maximum value of the active injection quantity of the upper-level power grid and the controllable load.
Further, in the step (2)
Constraint of the node voltage:
wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
respectively representing the real and imaginary parts of the node voltage,
respectively representing the maximum and minimum allowed values of the node voltage.
Further, in the step (2)
And the constraint of the branch flow is as follows:
wherein the content of the first and second substances,
representing n in a distribution networklSet of lines, Plm、QlmRespectively representing the active and the reactive in the branch,
representing the maximum allowable value of the branch flow.
Further, a method for constructing the sensitivity factor matrix in the step (2):
obtaining a vector X formed by the real part and the imaginary part of the node voltage according to the voltage complex vector V,
X:＝[Re{V}T Im{V}T]T
wherein Re { } and Im { } respectively represent the operation of taking a real part and an imaginary part;
the sensitivity factor matrix
Further, another method for constructing the sensitivity factor matrix in the step (2) is as follows:
according to Yk:＝ek(ek)TY
Wherein the content of the first and second substances,
a node admittance matrix is represented, which is,
which represents the basis of a standard vector,
representing a set of n nodes of the distribution network,
obtaining:
according to
Wherein the content of the first and second substances,
represents the capacitance to ground of the pi-type equivalent circuit (l, m); y islmRepresents the admittance of the line (l, m);
representing n in a distribution networklA set of lines;
obtaining:
then:
the node injected active and reactive power can be expressed as:
the branch power flow active and reactive can be expressed as:
wherein Tr { } represents the trace of the matrix, and YlmRepresents a matrix constructed from the admittances of the lines (l, m).
Will Pk,inj、Qk,injRespectively carrying out derivation on the X signals,
obtaining the sensitivity factor matrix:
further, in the third method for constructing the sensitivity factor matrix in step (2):
according to
Obtaining Δ X ═ LV[… ΔPk,inj … ΔQk,inj …]T；
Wherein, the matrix LVThe physical meaning of the representation is a linear relation between the real part and the imaginary part variable quantity of the node voltage and the variable quantity of the node injection power;
will have active P in the branchlmQ in branchlmReactive power is respectively injected into active power P to nodesk,injAnd node injection reactive Qk,injDerivation:
obtaining the sensitivity factor matrix:
wherein, the matrix LlmThe physical meaning of the representation is the linear relation between the active power flow and the reactive power flow of the branch and the node injection power deviation.
Further, the specific steps of constructing the optimal power flow model based on the opportunistic constraint in the step (3) are as follows:
assuming that the prediction error of renewable energy power follows a normal distribution, i.e.
And the prediction error of the renewable energy power is not correlated, by
Can obtain the product
Order to
And the upper limit of the controllable load and the lower limit of the controllable load are obeyedNormal distribution, i.e.
The target function of the optimal power flow model of the opportunity constraint is as follows:
wherein, PUNCIndicating that the electricity is purchased from the upper electric network,
representing the flexibility offered by the superior grid,
indicating that, β is a constant;
are located in a set according to the serial numbers of the connection nodes with the superior power grid
The first position in (1), followed by the controllable load node sequence number, is obtained:
further, a new variable t is introducedobjEliminating quadratic terms in the objective function of the optimal power flow model based on the opportunity constraint,
obj＝PUNC+(βσ2)·tobj。
further, there are several opportunity constraints for the optimal power flow model based on opportunity constraints in step (3), where the opportunity constraints include:
a first constraint:
the first constraint is obtained according to the transformation of an objective function;
a second constraint: g (P)k,Qk,V)＝0；
Wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
Pk、Qkrespectively representing the active injection quantity and the reactive injection quantity of the node, wherein V represents the voltage of the node;
the second constraint is a power flow constraint, namely node active and reactive injection balance;
and a third constraint:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
the maximum value of the reactive injection quantity of the upper-level power grid and the controllable load;
the third constraint is the reactive power injection limit of a superior power grid and a controllable load;
the first opportunity constrains:
wherein ε represents the probability of violating a constraint;
the second chance constraint:
wherein ε represents the probability of violating a constraint;
the first opportunity constraint and the second opportunity constraint represent an upper-level grid active injection limit;
the third chance constrains:
the fourth chance constrains:
the third opportunity constraint and the fourth opportunity constraint represent controllable load active injection limits, upper and lower power limits of which are uncertainty parameters, and physical meanings of which are the influence of non-ideal communication on demand response.
The fifth opportunity constrains:
the sixth opportunity constrains:
the fifth opportunity constraint and the sixth opportunity constraint represent probabilities that the node voltage of the power distribution network still does not exceed the limit after the new power balance is reached;
respectively representing the influence of the power fluctuation of the renewable energy source on the real part and the imaginary part of the node voltage;
the seventh opportunity constrains:
the seventh opportunity constraint represents the probability that the branch power flow still does not exceed the limit after the power distribution network reaches the new power balance;
respectively representing the influence of the power fluctuation of the renewable energy source on the active power and the reactive power of the branch power flow.
Further, an iterative method is used for solving the optimal solution of the optimal power flow model of the opportunity constraint, and during initial iteration, a sensitivity factor matrix obtained by calculating the optimal solution solved by the conventional optimal power flow model of the power distribution network in the step (2) is used;
and updating the sensitivity factor matrix after the optimal solution of the optimal power flow model of the opportunity constraint in the step 3) is calculated.
Further, according to (x + Δ x)2+(y+Δy)2≈x2+2xΔx+y2+2 yDeltay, x in the formula can be replaced by a node voltage real part or a branch power flow active part, and y in the formula can be replaced by a node voltage imaginary part or a branch power flow reactive part; solving the optimal solution of the optimal power flow model of the opportunity constraint by using an iterative method, wherein 2x delta x +2y delta y is approximately equal to 2x*Δx+2y*Δ y, wherein x*、y*Is the solution of the previous optimal power flow; in iteratively solving the optimal solution of the optimal power flow model for the opportunity constraints,
the fifth opportunity constraint is:
the sixth opportunity constraint is:
the seventh opportunity constraint is:
in the formula
And respectively obtaining the real part and the imaginary part of the voltage of the previous optimal power flow node and the active and reactive solutions of the branch power flow.
Further, converting the first chance constraint, the second chance constraint, the third chance constraint, the fourth chance constraint, the fifth chance constraint, the sixth chance constraint and the seventh chance constraint in the optimal power flow model based on chance constraints into deterministic constraint conditions,
the deterministic constraints of the first chance constraint are:
wherein phi-1(1-epsilon) represents a constant corresponding to the standard normal distribution quantile epsilon;
the deterministic constraint of the second chance constraint is:
the deterministic constraint of the third chance constraint is:
the deterministic constraint of the fourth chance constraint is:
the deterministic constraint of the fifth chance constraint is:
the deterministic constraint of the sixth chance constraint is:
the deterministic constraint of the seventh chance constraint is:
further, the specific step of determining whether the opportunistic constraint in the optimal power flow model based on the opportunistic constraint converges in the step (4) is: and judging whether the opportunity constraint in the optimal power flow model based on the opportunity constraint is converged or not according to the superior power grid, the controllable load active power injection and the reactive power injection.
The invention has the beneficial effects that:
1. according to the model and the method for optimizing and scheduling the active power distribution network, which are disclosed by the invention, various uncertain parameters in a physical-information system, such as uncertainty of non-ideal communication and renewable energy output, are comprehensively considered, and the influence of the information system on the optimizing and scheduling of the active power distribution network in a non-ideal communication environment is effectively considered;
2. according to the active power distribution network optimization scheduling model and method considering the uncertainty of the information system, the flexibility provided by a superior power grid and a controllable load and following the output change of renewable energy sources is decided according to network characteristics, and the conventional optimal power flow mainly focusing on economy is expanded into the opportunity constraint optimal power flow comprehensively considering the economy and the safety.
Drawings
FIG. 1 is a flow chart of an active power distribution network optimization scheduling method of the present invention that accounts for information system uncertainty;
FIG. 2 is a system wiring diagram of IEEE33 nodes in an embodiment of the invention;
fig. 3 is a power decision graph of controllable loads at different superior grid flexibility costs of the present invention.
The specific implementation mode is as follows:
it should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present application may be combined with each other without conflict. The invention is further described with reference to the following figures and examples.
Example 1:
in the embodiment, as shown in fig. 2, the IEEE33 node system wiring diagram assumes that the distribution network has only one node connected to the upper level grid, and wind power represents a renewable energy source with randomness, and only the change of active power is considered.
Representing a set of n nodes of the distribution network,
represents a set consisting of a superior grid connection node, a controllable load node and a wind power node,
the elements are arranged according to the sequence of the three,
the representation is a set consisting of a superior grid connection node and a controllable load node. Wherein, aggregate
The number of elements of (2) is NBThe number of the wind power nodes is NωLet N stand forP＝NB-Nω。
As described in the background art, in order to solve the above technical problems, an active power distribution network optimization scheduling model and method considering uncertainty of an information system are provided, wherein the active power distribution network optimization scheduling model considering uncertainty of the information system realizes a probability of constraint violation at a lower limit of various uncertainty factors, and substantially realizes a balance between economy and safe operation of an electric power system.
In an embodiment of the active power distribution network optimization scheduling model that accounts for information system uncertainty,
an active power distribution network optimal scheduling model considering uncertainty of an information system adopts an optimal power flow model based on opportunity constraint; the optimal power flow model based on opportunity constraint assumes that the renewable energy power prediction error, the upper limit of the controllable load and the lower limit of the controllable load are all in accordance with normal distribution, and is constructed according to a node injection power variation vector formed by power generation transfer factors and a sensitivity factor matrix constructed by adopting the optimal solution of the optimal power flow model of the power distribution network.
The power generation transfer factor expresses the relationship between the wind power change and the flexibility provided by a superior power grid and the flexibility provided by a controllable load;
the sensitivity factor matrix expresses the linear relation between the node injection power variation and the real part and the imaginary part of the node voltage, and the branch power flow active variation and the reactive variation.
In this embodiment, the application further provides an active power distribution network optimization scheduling method considering uncertainty of an information system, the upper limit and the lower limit of the controllable load polymer power are used as uncertain parameters expressing influence of communication uncertainty, a wind power prediction error is assumed to be in accordance with normal distribution, and an evaluation method of a sensitivity factor is used, that is, an approximate and linear relation exists between a node power injection deviation and a node voltage and a branch power flow near an expected operation point. And making a decision of flexibility provided by a superior power grid and a controllable load and following the wind power output change according to the network characteristics.
In order to achieve the purpose, the invention adopts the following technical scheme:
an active power distribution network optimal scheduling method considering uncertainty of an information system is based on an active power distribution network optimal scheduling model considering uncertainty of the information system, and as shown in fig. 1, the method specifically comprises the following steps:
(1) constructing a power generation transfer factor, wherein the power generation transfer factor expresses the relationship between the wind power change and the flexibility provided by a superior power grid and the flexibility provided by a controllable load;
(2) solving an optimal solution of a conventional optimal power flow model of the power distribution network by adopting the conventional optimal power flow model of the power distribution network, and constructing a sensitivity factor matrix, wherein the sensitivity factor matrix expresses the linear relation between the variable quantity of node injection power and the real part and the imaginary part of node voltage as well as the active variable quantity and the reactive variable quantity of branch power flow;
(3) constructing an optimal power flow model based on opportunity constraint by combining the step (1) and the step (2), converting the optimal power flow model into a deterministic optimal power flow model, solving a deterministic optimal power flow problem, and updating a sensitivity factor matrix;
(4) judging whether the optimal power flow model converted into the certainty is converged or not, and if so, obtaining an optimal solution; and if not, returning to the step (3).
The specific steps of constructing the power generation transfer factor in the step (1) are as follows:
according to the formula Δ Pinj＝D·ΔPωConstruction of Power Generation transfer factor
Wherein the content of the first and second substances,
representation collection
The change in the active injection of the medium node,
represents a set consisting of a superior grid connection node, a controllable load node and a wind power node,
the elements are arranged and integrated according to the sequence of the three
The number of elements of (2) is NBThe number of the wind power nodes is NωLet N stand forP＝NB-Nω；
The arrangement order of elements in (1) and
the arrangement sequence of the elements is consistent;
the active power variation of a node connected with a superior power grid is represented, namely the flexibility provided by the superior power grid is represented;
representing the variable quantity of the active power of the controllable load node, namely the flexibility provided by the controllable load;
representing the change of the active power of the wind power node;
is a matrix formed by combining variables and constants;
wherein, wmRepresenting the change of the active power of the mth wind power node;
wherein, line 1 to NPThe behavior is variable, and the elements of each row are equal, with dq(q＝1,…,NP) It is shown that,
the rest of NωLine NωThe column elements form NωAn order unit matrix.
In the embodiment, the optimal solution of the conventional optimal power flow model of the power distribution network is solved in the step (2) by using a method based on semi-positive definite convex relaxation,
the conventional optimal power flow model of the power distribution network is as follows:
min PUNC·Δt
wherein, PUNCΔ t denotes the power P in the scheduling period Δ tUNCThe purchase of electricity from an upper-level grid.
Furthermore, in the step (2), a plurality of constraint conditions are provided for the conventional optimal power flow model of the power distribution network, and the constraint conditions include power flow constraint, upper-level power grid and controllable load reactive power injection limitation, upper-level power grid and controllable load active power injection limitation, node voltage constraint and branch power flow constraint.
Further, in the step (2)
The power flow constraint is the balance of active and reactive injection of the power distribution network nodes:
wherein the content of the first and second substances,
representing a set of n nodes, P, of a distribution networkk,inj、Qk,injRespectively representing the active and reactive net injection quantities at the conventional node of the generator.
Further, in the step (2)
And limiting the upper-level power grid and the controllable load reactive power injection:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
the maximum value of the reactive injection quantity of the upper-level power grid and the controllable load.
Further, in the step (2)
And limiting the upper-level power grid and the controllable load active power injection:
wherein the content of the first and second substances,
representing a set of superior grid connection nodes, controllable load nodes, PkThe active injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the active injection quantity of the upper-level power grid and the controllable load,
the maximum value of the active injection quantity of the upper-level power grid and the controllable load.
Further, in the step (2)
Constraint of the node voltage:
wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
respectively representing the maximum and minimum allowed values of the node voltage.
Further, in the step (2)
And the constraint of the branch flow is as follows:
wherein the content of the first and second substances,
representing n in a distribution networklA set of one or more lines of wire,
representing the maximum allowable value of the branch flow.
The linear matrix inequality of further constraint 5 is:
constraint 6: w is not less than 0, rank (W) is 1
Finally, the constraint rank (w) ═ 1 is removed, and the model is a semi-positive definite convex optimization problem.
Assuming that there is an optimal solution for the conventional optimal power flow,
in this embodiment, the sensitivity factor matrix in step (2) adopts a construction method:
according to
To obtain
Matrix LVAnd the linear relation between the active and reactive injection variable quantity of the node and the real and imaginary component variable quantities of the node voltage is represented.
Matrix LlmAnd the linear relation between the active and reactive injection variable quantity of the node and the active and reactive variable quantity of the branch power flow is represented.
The specific steps of constructing the optimal power flow model based on the opportunity constraint in the step (3) are as follows:
assuming that the prediction error of the wind power follows normal distribution, i.e.
And the prediction error of the wind power is not related, by
Can obtain the product
Order to
Also, the upper limit of the controllable load and the lower limit of the controllable load follow a normal distribution, i.e.
The target function of the optimal power flow model of the opportunity constraint is as follows:
wherein, PUNCIndicating that the electricity is purchased from the upper electric network,
representing the flexibility offered by the superior grid,
indicating that, β is a constant;
are located in a set according to the serial numbers of the connection nodes with the superior power grid
The first position in (1), followed by the controllable load node sequence number, is obtained:
introducing a new variable tobjEliminating quadratic terms in the objective function of the optimal power flow model based on the opportunity constraint,
obj＝PUNC+(βσ2)·tobj。
in the step (3), there are several opportunity constraints for the optimal power flow model based on opportunity constraints, where the opportunity constraints include:
a first constraint:
the first constraint is obtained according to the transformation of an objective function;
a second constraint: pk,inj＝Tr{YkW}；
Wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
Pk,inj、Qk,injrespectively representing the active net injection quantity and the reactive net injection quantity of a node according to the common practice of the generator;
the second constraint is a power flow constraint, namely node active and reactive injection balance;
and a third constraint:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
the maximum value of the reactive injection quantity of the upper-level power grid and the controllable load;
the third constraint is the reactive power injection limit of a superior power grid and a controllable load;
the first opportunity constrains:
wherein ε represents the probability of violating a constraint;
the second chance constraint:
wherein ε represents the probability of violating a constraint;
the first opportunity constraint and the second opportunity constraint represent an upper-level grid active injection limit;
the third chance constrains:
the fourth chance constrains:
the third opportunity constraint and the fourth opportunity constraint represent controllable load active injection limits, upper and lower power limits of which are uncertainty parameters, and physical meanings of which are the influence of non-ideal communication on demand response.
The fifth opportunity constrains:
the sixth opportunity constrains:
the fifth opportunity constraint and the sixth opportunity constraint represent probabilities that the node voltage of the power distribution network still does not exceed the limit after the new power balance is reached;
respectively representing the influence of wind power fluctuation on the real part and the imaginary part of the node voltage;
the seventh opportunity constrains:
the seventh opportunity constraint represents the probability that the branch power flow still does not exceed the limit after the power distribution network reaches the new power balance;
and respectively representing the influence of wind power fluctuation on the active power and the reactive power of the branch power flow.
Solving the optimal solution of the optimal power flow model of the opportunity constraint by using an iteration method, and during initial iteration, solving the optimal solution by using the conventional optimal power flow model of the power distribution network in the step (2) to obtain a sensitivity factor matrix;
and updating the sensitivity factor matrix after the optimal solution of the optimal power flow model of the opportunity constraint in the step 3) is calculated.
According to (x + Δ x)2+(y+Δy)2≈x2+2xΔx+y2+2 yDeltay, x in the formula can be replaced by a node voltage real part or a branch power flow active part, and y in the formula can be replaced by a node voltage imaginary part or a branch power flow reactive part; solving the optimal solution of the optimal power flow model of the opportunity constraint by using an iterative method，2xΔx+2yΔy≈2x*Δx+2y*Δ y, wherein x*、y*Is the solution of the previous optimal power flow; in iteratively solving the optimal solution of the optimal power flow model for the opportunity constraints,
the fifth opportunity constraint is:
the sixth opportunity constraint is:
the seventh opportunity constraint is:
in the formula
And respectively obtaining the real part and the imaginary part of the voltage of the previous optimal power flow node and the active and reactive solutions of the branch power flow.
Converting the first chance constraint, the second chance constraint, the third chance constraint, the fourth chance constraint, the fifth chance constraint, the sixth chance constraint and the seventh chance constraint in the optimal power flow model based on chance constraints into deterministic constraints,
according to
Then
The deterministic constraint of the first chance constraint is:
Φ-1(1-. epsilon.) represents a constant corresponding to the standard normal distribution quantile ε. And d1≦ 0, so the constraint may ultimately be expressed as:
similarly, the deterministic constraint condition of the second chance constraint is:
according to
To obtain
The deterministic constraint of the third chance constraint is:
the initial deterministic constraint of the third opportunity constraint is:
the initial deterministic constraint condition of the third chance constraint is a nonlinear constraint, and a new variable is introduced
This constraint can be translated into:
||·||2a 2-norm is expressed, which translates a non-linear constraint into a linear constraint and a second order cone constraint.
Similarly, a new variable is introduced into the initial deterministic constraint condition of the fourth chance constraint
Its deterministic constraint is:
according to
DkIs a covariance matrix.
The initial deterministic constraint of the fifth opportunity constraint is:
the constraint is still a non-linear constraint, and a new variable is introduced
This deterministic constraint can be translated into:
similarly, a new variable is introduced into the initial deterministic constraint condition of the sixth-chance constraint
The deterministic constraint of the sixth chance constraint is:
the initial deterministic constraint of the fifth opportunity constraint and the initial deterministic constraint of the sixth opportunity constraint indicate that the node voltage of the power distribution network still does not exceed the limit after the new power balance is achieved.
Is a new variable introduced for converting the node voltage nonlinear constraint into a linear constraint and a second order cone constraint.
The initial deterministic constraint of the seventh opportunity constraint and the initial deterministic constraint of the fifth opportunity constraint and the initial deterministic constraint of the sixth opportunity constraint are similar in structure, and a covariance matrix DlmAnd DkThe construction method is similar. Introducing a new variable
The deterministic constraint of the seventh chance constraint is:
and is provided with a plurality of groups of the following components,
the seventh opportunity constraint represents the probability that the branch flow is still not out-of-limit after the distribution network reaches the new power balance.
The method is a new variable introduced for converting the nonlinear constraint of the branch power flow into the linear matrix inequality and the second-order cone constraint.
Finally adding constraint, wherein W is more than or equal to 0;
the solution of the specific optimal solution in this embodiment is accomplished by calling the semi-positive solver SEDUMI under the tool box YALMIP of MATLAB. YALMIP can automatically convert the second order cone constraint into a linear matrix inequality, so that the original problem is a semi-positive definite convex optimization problem.
And updating the sensitivity factor matrix after the opportunity constraint optimal power flow is solved.
The specific step of judging whether the opportunity constraint in the optimal power flow model based on the opportunity constraint is converged in the step (4) is as follows: and judging whether the opportunity constraint in the optimal power flow model based on the opportunity constraint is converged or not according to the superior power grid, the controllable load active power injection and the reactive power injection.
Simulation results as shown in fig. 3, the power of the controllable load tends to the midpoint of its adjustable range as the flexibility cost of the upper grid increases.
Example 2:
a method for constructing the sensitivity factor matrix in the step (2):
obtaining a vector X formed by the real part and the imaginary part of the node voltage according to the voltage complex vector V,
X:＝[Re{V}T Im{V}T]T
wherein Re { } and Im { } respectively represent the operation of taking a real part and an imaginary part;
the sensitivity factor matrix
Other embodiments of this example are the same as those of example 1.
Example 3:
another construction method of the sensitivity factor matrix in the step (2) comprises the following steps:
according to Yk:＝ek(ek)TY
Wherein the content of the first and second substances,
a node admittance matrix is represented, which is,
which represents the basis of a standard vector,
representing a set of n nodes of the distribution network,
obtaining:
according to
Wherein the content of the first and second substances,
represents the capacitance to ground of the pi-type equivalent circuit (l, m); y islmRepresents the admittance of the line (l, m);
representing n in a distribution networklA set of lines;
obtaining:
then:
the node injected active and reactive power can be expressed as:
the branch power flow active and reactive can be expressed as:
wherein Tr { } represents the trace of the matrix, and YlmRepresents a matrix constructed from the admittances of the lines (l, m).
Will Pk,inj、Qk,injRespectively carrying out derivation on the X signals,
obtaining the sensitivity factor matrix:
other embodiments of this example are the same as those of example 1.
The invention has the beneficial effects that:
1. according to the model and the method for optimizing and scheduling the active power distribution network, which are disclosed by the invention, various uncertain parameters in a physical-information system, such as uncertainties of non-ideal communication and wind power output, are comprehensively considered, and the influence of the information system on the optimizing and scheduling of the active power distribution network in a non-ideal communication environment is effectively considered;
2. according to the model and the method for optimizing and scheduling the active power distribution network, which are disclosed by the invention, the flexibility provided by a superior power grid and a controllable load and changing along with the wind power output is decided according to the network characteristics, and the conventional optimal power flow which is heavier than economy is expanded into the opportunity constraint optimal power flow which comprehensively considers the economy and the safety.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An active power distribution network optimization scheduling model considering uncertainty of an information system is characterized in that: the optimal scheduling model of the active power distribution network adopts an optimal power flow model based on opportunity constraint; the optimal power flow model based on opportunity constraint assumes that the renewable energy power prediction error, the upper limit of the controllable load and the lower limit of the controllable load all obey normal distribution, and is constructed according to a node injection power variation vector formed by power generation transfer factors and a sensitivity factor matrix constructed by the optimal solution of the optimal power flow model of the power distribution network;
the power generation transfer factor expresses the relationship between the power change of the renewable energy source and the flexibility provided by a superior power grid and the flexibility provided by a controllable load; according to the formula Δ Pinj＝D·ΔPωConstruction of the Power Generation transfer factor, Δ PωRepresenting the change of active power of renewable energy source nodes, D is a matrix formed by combining variables and constants, and the 1 st line to the N th linePThe behavior is variable, and the elements of each row are equal, with dq(q＝1,…,NP) Denotes the remainder of NωLine NωThe column elements form Nω×NωOrder identity matrix, NωRepresents the number of renewable energy nodes, and has,
-1≤dq≤0(q＝1,…,NP)，NP＝NB-Nω，NBis a set
The number of the elements (c) is,
represents a set consisting of a superior grid connection node, a controllable load node, and a renewable energy node,
the elements are arranged according to the sequence of the three; the sensitivity factor matrix expresses the linear relation between the node injection power variation and the real part and the imaginary part of the node voltage, and the branch power flow active variation and the reactive variation.
2. An active power distribution network optimal scheduling method considering uncertainty of an information system, which is based on the active power distribution network optimal scheduling model considering uncertainty of the information system according to claim 1, and is characterized in that: the method comprises the following specific steps:
(1) constructing a power generation transfer factor, wherein the power generation transfer factor expresses the relationship between the power change of renewable energy sources and the flexibility provided by a superior power grid and the flexibility provided by a controllable load; according to the formula Δ Pinj＝D·ΔPωConstruction of the Power Generation transfer factor, Δ PωRepresenting the change of active power of renewable energy source nodes, D is a matrix formed by combining variables and constants, and the 1 st line to the N th linePThe behavior is variable, and the elements of each row are equal, with dq(q＝1,…,NP) Denotes the remainder of NωLine NωThe column elements form Nω×NωThe order of the identity matrix, and in addition,
-1≤dq≤0(q＝1,…,NP) (ii) a (2) Solving an optimal solution of a conventional optimal power flow model of the power distribution network by adopting the conventional optimal power flow model of the power distribution network, and constructing a sensitivity factor matrix, wherein the sensitivity factor matrix expresses the linear relation between the variable quantity of node injection power and the real part and the imaginary part of node voltage as well as the active variable quantity and the reactive variable quantity of branch power flow;
(3) constructing an optimal power flow model based on opportunity constraint by combining the step (1) and the step (2), converting the optimal power flow model into a deterministic optimal power flow model, solving a deterministic optimal power flow problem, and updating a sensitivity factor matrix;
(4) judging whether the optimal power flow model converted into the certainty is converged or not, and if so, obtaining an optimal solution; and if not, returning to the step (3).
3. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 2, wherein: the specific steps of constructing the power generation transfer factor in the step (1) are as follows:
according to the formula Δ Pinj＝D·ΔPωConstruction of Power Generation transfer factor
Wherein the content of the first and second substances,
representation collection
The change in the active injection of the medium node,
represents a set consisting of a superior grid connection node, a controllable load node, and a renewable energy node,
the elements are arranged and integrated according to the sequence of the three
The number of elements of (2) is NBThe number of the renewable energy source nodes is NωLet N stand forP＝NB-Nω；
The arrangement order of elements in (1) and
the arrangement sequence of the elements is consistent;
the active power variation of a node connected with a superior power grid is represented, namely the flexibility provided by the superior power grid is represented;
representing the variable quantity of the active power of the controllable load node, namely the flexibility provided by the controllable load;
representing the change of active power of the renewable energy source node;
is a matrix formed by combining variables and constants;
wherein, wmRepresenting the change of the active power of the mth renewable energy source node;
wherein, line 1 to NPThe behavior is variable, and the elements of each row are equal, with dq(q＝1,…,NP) It is shown that,
-1≤dq≤0(q＝1,…,NP) (ii) a The rest of NωLine NωThe column elements form NωAn order unit matrix.
4. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 2, wherein: the conventional optimal power flow model of the power distribution network in the step (2) is as follows:
min PUNC·Δt
wherein, PUNCΔ t denotes the power P in the scheduling period Δ tUNCPurchasing electric power from a superior grid; in the step (2) forThe conventional optimal power flow model of the power distribution network has a plurality of constraint conditions, wherein the constraint conditions comprise power flow constraint, reactive power injection limitation of a superior power grid and a controllable load, active power injection limitation of the superior power grid and the controllable load, constraint of node voltage and constraint of branch power flow;
the power flow constraint is an active and reactive injection equation of the power distribution network node:
wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
Pk、Qkrespectively representing the active injection quantity and the reactive injection quantity of the node, wherein V represents the voltage of the node;
and limiting the upper-level power grid and the controllable load reactive power injection:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
upper-level power grid and controllable load reactive power injectionMaximum value of input amount;
and limiting the upper-level power grid and the controllable load active power injection:
wherein the content of the first and second substances,
representing a set of superior grid connection nodes, controllable load nodes, PkThe active injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the active injection quantity of the upper-level power grid and the controllable load,
the maximum value of the active injection quantity of the superior power grid and the controllable load;
constraint of the node voltage:
wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
respectively representing the real and imaginary parts of the node voltage,
respectively representing nodesMaximum and minimum allowable values of voltage;
and the constraint of the branch flow is as follows:
wherein the content of the first and second substances,
representing n in a distribution networklSet of lines, l and m representing lines in a distribution network, Plm、QlmRespectively representing the active and the reactive in the branch,
representing the maximum allowable value of the branch flow.
5. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 2, wherein: the optimal power flow model based on the opportunity constraint is constructed in the step (3)
Introducing a new variable tobjEliminating quadratic terms in the objective function of the optimal power flow model based on the opportunity constraint,
obj＝PUNC+(βσ2)·tobj
wherein, PUNCRepresents the power purchased from the upper power grid, beta is a constant, d1Is composed of
The variables in the first row, assuming that the prediction error of renewable energy power follows a normal distribution, i.e.
m＝1,…,Nω,
6. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 5, wherein: in the step (3), there are several opportunity constraints for the optimal power flow model based on opportunity constraints, where the opportunity constraints include:
a first constraint:
the first constraint is obtained according to the transformation of an objective function;
a second constraint: the power flow constraint is an active and reactive injection equation of the power distribution network node: g (P)k,Qk,V)＝0；
Wherein the content of the first and second substances,
representing a set of n nodes of the distribution network,
Pk、Qkrespectively representing the active injection quantity and the reactive injection quantity of the node, wherein V represents the voltage of the node;
the second constraint is a power flow constraint, namely node active and reactive injection balance;
and a third constraint:
wherein the content of the first and second substances,
representing a collection of superior grid connection nodes, controllable load nodes, QkThe reactive injection quantity of the upper-level power grid and the controllable load is represented,
the minimum value of the reactive injection quantity of the upper-level power grid and the controllable load,
the maximum value of the reactive injection quantity of the upper-level power grid and the controllable load;
the third constraint is the reactive power injection limit of a superior power grid and a controllable load;
the first opportunity constrains:
wherein ε represents the probability of violating a constraint;
the second chance constraint:
wherein ε represents the probability of violating a constraint;
the first and second opportunistic constraints represent superior grid active injection limits, wherein
Representing the flexibility offered by the superior grid,
represents the maximum value of the electric quantity purchased from the upper-level power grid,
representing the minimum value of the electricity purchased from the superior power grid;
the third chance constrains:
the fourth chance constrains:
the third and fourth opportunity constraints represent controllable load active injection limits, whose upper and lower power limits are uncertainty parameters, whose physical meaning is the impact of non-ideal communication on demand response, wherein,
the flexibility offered by the controllable load is expressed,
and
lower and upper limits, P, respectively, of active power of the controllable loadCL,iIndicating that the controllable load is active;
the fifth opportunity constrains:
the sixth opportunity constrains:
the fifth opportunity constraint and the sixth opportunity constraint represent probabilities that the node voltage of the power distribution network still does not exceed the limit after the new power balance is reached;
respectively representing the influence of renewable energy power fluctuation on the real part and the imaginary part of the node voltage, wherein
Represents the maximum allowed value of the node voltage,
expressed as a minimum allowed value of the node voltage,
expressed as the real part of the node voltage,
expressed as the imaginary part of the node voltage;
the seventh opportunity constrains:
the seventh opportunity constraint represents the probability that the branch power flow still does not exceed the limit after the power distribution network reaches the new power balance;
respectively representing the influence of power fluctuation of renewable energy sources on the active power and the reactive power of branch power flow, wherein
Representing n in a distribution networklA set of one or more lines of wire,
representing the maximum allowable value, P, of the branch power flowlm、QlmRespectively representing active power and reactive power in the branch circuits, and l and m representing lines in the power distribution network.
7. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 5, wherein: solving the optimal solution of the optimal power flow model of the opportunity constraint by using an iteration method, and during initial iteration, solving the optimal solution by using the conventional optimal power flow model of the power distribution network in the step (2) to obtain a sensitivity factor matrix;
and (4) updating the sensitivity factor matrix after the optimal solution of the optimal power flow model of the opportunity constraint in the step (3) is calculated.
8. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 6, wherein: according to (x + Δ x)2+(y+Δy)2≈x2+2xΔx+y2+2 yDeltay, x in the formula can be replaced by a node voltage real part or a branch power flow active part, and y in the formula can be replaced by a node voltage imaginary part or a branch power flow reactive part; solving the optimal solution of the optimal power flow model of the opportunity constraint by using an iterative method, wherein 2x delta x +2y delta y is approximately equal to 2x*Δx+2y*Δ y, wherein x*、y*Is the solution of the previous optimal power flow; in iteratively solving the optimal solution of the optimal power flow model for the opportunity constraints,
the fifth opportunity constraint is:
the sixth opportunity constraint is:
the seventh opportunity constraint is:
in the formula
The voltage real part and the imaginary part of the previous optimal power flow node are respectively provided with branch power flowsSolutions to work and reactive, parameters in the fifth/sixth opportunity constraints
Is the real part of the node voltage,
is the imaginary part of the voltage at the node,
is the maximum allowed value of the node voltage,
expressed as a minimum allowed value of the node voltage,
respectively representing the influence of renewable energy power fluctuation on the real part and the imaginary part of the node voltage, epsilon represents the probability of violating the constraint, and k represents
Is connected to the network node in the network,
representing a set of n nodes of the distribution network,
parameter P in the seventh opportunity constraintlm、QlmRespectively representing the active and the reactive in the branch,
respectively representing the influence of the power fluctuation of the renewable energy source on the active power and the reactive power of the branch power flow,
representing the maximum allowable value of branch power flow, epsilonRepresenting the probability of violating the constraint, l, m representing the line in the distribution network,
representing n in a distribution networklA set of lines;
only the change of the connection node with the superior power grid, the controllable load and the active power of the fan node is considered, so that the method only needs to be integrated
Medium element extraction matrix LV、LlmThe corresponding column can obtain a new matrix
Therefore, the temperature of the molten metal is controlled,
similarly, the lines (L, m) are respectively active and reactive with the matrix LlmI th of (1)lmRow and ithlm+nlThe correlation of the rows is carried out,
9. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 6, wherein: converting the first chance constraint, the second chance constraint, the third chance constraint, the fourth chance constraint, the fifth chance constraint, the sixth chance constraint and the seventh chance constraint in the optimal power flow model based on chance constraints into deterministic constraint conditions,
the deterministic constraints of the first chance constraint are:
wherein phi-1(1-epsilon) represents a constant corresponding to the standard normal distribution quantile epsilon;
the deterministic constraint of the second chance constraint is:
the deterministic constraint of the third chance constraint is:
the deterministic constraint of the fourth chance constraint is:
the deterministic constraint of the fifth chance constraint is:
the deterministic constraint of the sixth chance constraint is:
the deterministic constraint of the seventh chance constraint is:
wherein the content of the first and second substances,
represents the maximum value of the electric quantity purchased from the upper-level power grid,
represents the minimum value of the power purchased from the superior power grid,
and
is a new variable introduced to convert a non-linear constraint into a linear constraint and a second order cone constraint,
and
is a new variable introduced for converting the node voltage nonlinear constraint into a linear constraint and a second-order cone constraint,
and
respectively the lower limit and the upper limit of the active power of the controllable load, and when the upper limit and the lower limit of the controllable load obey normal distribution, the lower limit and the upper limit of the controllable load are respectively the lower limit and the upper limit of the active power of the controllable load
Expressed as the real part of the node voltage,
expressed as the imaginary part of the node voltage,
represents the maximum allowed value of the node voltage,
representing the minimum permissible value of the node voltage, DkIs a covariance matrix, Plm、QlmRespectively representing the active and the reactive in the branch,
representing the maximum allowable value of the branch flow, DlmIn the form of a covariance matrix,
is a new variable introduced for converting the nonlinear constraint of the branch power flow into the linear matrix inequality and the second-order cone constraint,
representing n in a distribution networklA set of one or more lines of wire,
-1≤dq≤0(q＝1,…,NP) And l and m represent lines in the power distribution network.
10. The active power distribution network optimal scheduling method considering the uncertainty of the information system as claimed in claim 2, wherein: the specific step of judging whether the optimal power flow model converted into the certainty converges in the step (4) is as follows: and judging whether the opportunity constraint in the optimal power flow model based on the opportunity constraint is converged or not according to the superior power grid, the controllable load active power injection and the reactive power injection.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101548447A (en) * | 2006-10-05 | 2009-09-30 | Abb研究有限公司 | Determination and use of power system sensitivities for power flow control |
| CN104022533A (en) * | 2014-04-15 | 2014-09-03 | 广西电网公司 | Cutoff measure cooperated power flow monitoring method of electromagnetic ring network |
| CN104578157A (en) * | 2015-01-04 | 2015-04-29 | 云南电网有限责任公司电力科学研究院 | Load flow calculation method of distributed power supply connection power grid |
-
2017
- 2017-04-07 CN CN201710224623.0A patent/CN108695853B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101548447A (en) * | 2006-10-05 | 2009-09-30 | Abb研究有限公司 | Determination and use of power system sensitivities for power flow control |
| CN104022533A (en) * | 2014-04-15 | 2014-09-03 | 广西电网公司 | Cutoff measure cooperated power flow monitoring method of electromagnetic ring network |
| CN104578157A (en) * | 2015-01-04 | 2015-04-29 | 云南电网有限责任公司电力科学研究院 | Load flow calculation method of distributed power supply connection power grid |
Non-Patent Citations (3)
| Title |
|---|
| Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem;Dimitris Bertsimas等;《IEEE TRANSACTIONS ON POWER SYSTEMS》;20130228;第28卷(第1期);第52-63页 * |
| Reserve Requirements in AC Power Systems With Uncertain Generation;Vahab Rostampour等;《2013 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)》;20140106;第1-5页 * |
| 考虑注入功率分布的随机最优潮流方法;易驰韡等;《电网技术》;20130228;第37卷(第2期);第367-371页 * |
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