CN108631317B - Distributed dynamic optimal power flow method for active power distribution network containing multi-source main body - Google Patents

Distributed dynamic optimal power flow method for active power distribution network containing multi-source main body Download PDF

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CN108631317B
CN108631317B CN201810531091.XA CN201810531091A CN108631317B CN 108631317 B CN108631317 B CN 108631317B CN 201810531091 A CN201810531091 A CN 201810531091A CN 108631317 B CN108631317 B CN 108631317B
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distribution network
energy storage
storage device
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power distribution
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CN108631317A (en
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卢艺
戴月
梁俊文
马伟哲
张炀
史军
车诒颖
辛拓
刘明波
林舜江
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Shenzhen Power Supply Co ltd
South China University of Technology SCUT
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South China University of Technology SCUT
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a distributed dynamic optimal power flow method for an active power distribution network containing a multi-source main body, which comprises the following steps: s100, determining an optimization target and constraint conditions, and establishing a DOPF model of the active power distribution network containing multiple sources and multiple subjects; s200, performing linear approximation on a power flow equation in the DOPF model of the active power distribution network, and converting an original nonlinear programming problem into a quadratic programming problem; s300, respectively taking the power distribution network, the distributed power supply and the energy storage device as independent main bodies, and converting the DOPF model of the active power distribution network into a D-DOPF model of the active power distribution network; s400, solving the D-DOPF model of the active power distribution network by adopting an auxiliary problem principle, and decomposing the solution of the D-DOPF model of the active power distribution network into solutions of a plurality of independent main body quadratic programming submodels. By adopting the method to solve the D-DOPF model, the privacy protection among all the benefit agents is met, and the global optimum can be realized in the optimization process.

Description

Distributed dynamic optimal power flow method for active power distribution network containing multi-source main body
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a distributed dynamic optimal power flow method for an active power distribution network with multiple sources and multiple main bodies.
Background
With the distributed power sources (such as photovoltaic power sources and controllable generator sets) and energy storage devices widely connected to the power distribution network, the physical characteristics of the power distribution network are changed from pure passive to active, and the regulation and control capability of the power distribution network is obviously enhanced. Because the ratio of the resistance to the reactance of the distribution line is high, active Power and reactive Power are difficult to decouple, the Optimal scheduling problem is essentially a Dynamic Optimal Power Flow (DOPF) problem, namely the Optimal Power Flow problem coupled by 96 time sections all day. At present, a centralized optimization method is mainly adopted for solving, namely a scheduling center needs to collect global information before optimization solving, and sends control instructions to each controller after centralized calculation is carried out by using the collected information. However, with the increase of access devices, on one hand, centralized collection of global information faces challenges of communication bottlenecks and the like, and on the other hand, when controllable units participating in optimized scheduling belong to different subjects, collection of global information also faces difficulties in privacy protection. In this context, a Distributed Dynamic Optimal Power Flow (D-DOPF) arises as soon as it separates the optimization subjects, each subject performs the optimization separately, and only a small amount of information is exchanged between the subjects. The optimization mode has great advantages under the situation that a large number of distributed power supplies belonging to different subjects are connected to a power distribution network. The existing method for solving the distributed optimization problem mainly comprises a dual decomposition algorithm, an optimality condition decomposition algorithm, an equivalence decomposition algorithm and the like.
Modeling aspect: at present, the modeling modes of Distributed Optimal Power Flow (D-OPF) of an active Power distribution network can be divided into three types: the first type is that an alternating current power flow model is directly adopted to establish a non-convex nonlinear programming model; the second type is that a direct current power flow equation is adopted to approximately replace an alternating current power flow equation, and a linear programming or quadratic programming model is established; the third type is to perform convex transformation on the D-OPF or to perform high-precision linearization on the power flow equation of the D-OPF, and currently, common methods include second-order cone relaxation, semi-definite programming convex relaxation, piecewise linearization and the like.
Solving the algorithm aspect: at present, dual decomposition algorithms such as an alternating direction multiplier method, a Lagrange relaxation method, a secant plane consistency algorithm and the like are mainly adopted for solving the D-OPF of the active power distribution network.
As can be seen from the modeling modes and the solving algorithms of the three D-OPF problems, the modeling precision of the first mode is high, but the convergence of the dual decomposition type algorithm cannot be guaranteed and the global optimal solution cannot be obtained when the dual decomposition type algorithm is used for solving the non-convex nonlinear programming model. The model established by the second mode is very suitable for solving by using a dual decomposition algorithm, but neglects the coupling between active power and reactive power in the active power distribution network, and has low accuracy. The model established by the third mode has obvious advantages in the aspects of optimization precision and solving efficiency, and particularly shows a high-precision linearization method. However, the problem of high-precision linearization of D-DOPF including energy storage devices is still lacking, and is generally described as a mixed integer linear programming model or a non-convex non-linear programming model.
In summary, the modeling and solving of the D-OPF problem is yet to be further investigated.
Disclosure of Invention
In view of the above, the invention aims to provide a distributed dynamic optimal power flow method for an active power distribution network with multi-source and multi-subject aiming at solving the problem of D-DOPF of the active power distribution network with multi-source and multi-subject, so that privacy protection among all benefit subjects is met, and global optimization can be realized in the optimization process.
Therefore, the embodiment of the invention provides a distributed dynamic optimal power flow method for an active power distribution network with a multi-source main body, which comprises the following steps:
s100, determining an optimization target and constraint conditions, and establishing a DOPF model of the active power distribution network containing multiple sources and multiple subjects;
s200, performing linear approximation on a power flow equation in the DOPF model of the active power distribution network, and converting an original nonlinear programming problem into a quadratic programming problem;
s300, respectively taking the power distribution network, the distributed power supply and the energy storage device as independent main bodies, and converting the DOPF model of the active power distribution network into a D-DOPF model of the active power distribution network;
s400, solving the D-DOPF model of the active power distribution network by adopting an auxiliary problem principle, and decomposing the solution of the D-DOPF model of the active power distribution network into solutions of a plurality of independent main body quadratic programming submodels.
In step S100, the optimization objective is: minimizing the sum of the network loss cost of the power distribution network, the electricity purchasing cost of the power distribution network, the electricity generation cost of the controllable generator set, the light abandoning cost and the loss cost of the energy storage device;
the constraint conditions include: the node voltage upper and lower limits are restricted, the controllable generator set is restricted, the light abandoning amount upper and lower limits are restricted, and the energy storage device is restricted.
The energy storage device constraints in the step S100 include maximum charge-discharge power constraints, energy transfer equations, state of charge constraints, and running state complementary constraints; the step S200 includes: and the node voltage of each node in the network is linearly expressed by using the node injection power, the charge and discharge power constraint of the energy storage device is removed, and the original nonlinear programming problem is converted into a quadratic programming problem.
Wherein the step S300 includes: and splitting the DOPF model of the active power distribution network containing the multi-source and multi-main bodies into a D-DOPF model comprising a network side, a user side photovoltaic power supply, a user side energy storage device and a user side controllable generator set.
The network side consists of all nodes of the power distribution network, and the optimization aim is to minimize the sum of the network loss cost and the power purchasing cost from the main network; the user side photovoltaic power supply is composed of all nodes connected into the photovoltaic power supply, and the optimization goal is that the cost of light abandoning is minimum; the energy storage device at the user side consists of all nodes connected into the energy storage device, and the optimization aim is to minimize the loss cost of the energy storage device; the user side controllable generator set is composed of all nodes connected into the generator set, and the optimization goal is that the generation cost of the controllable generator set is minimum.
Wherein the optimization target of the D-DOPF model is as follows:
min Cu(Vj,t,Pj,t,Qj,t)+Cbuy(Vj,t,Pj,t,Qj,t)+Cpv(Pc,t,Qc,t)+Cbat(Pch,t,Pdis,t)+Cgen(PG,t,QG,t)；
the specific expression of the network loss cost of the power distribution network is as follows:
Figure BDA0001677206860000031
the specific expression of the electricity purchasing cost of the power distribution network is as follows:
Figure BDA0001677206860000032
in the formula, epsilon represents a set of distribution network lines, N is a set of all nodes, a set N' is a set of nodes except a balance node, and C0Active electricity purchasing cost coefficient for distribution network, C1A cost coefficient for the reactive power supply of the distribution network;
the specific expressions of the photovoltaic abandoned light and the reactive power output cost are as follows:
Figure BDA0001677206860000033
in the formula, NtFor the total number of periods of the scheduling cycle, Pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t; a ish、bhAnd chA cost coefficient for light abandonment;
the specific expression of the loss cost of the energy storage device is as follows:
Figure BDA0001677206860000034
in the formula, Pdis,j,tAnd Pch,j,tRespectively representing the discharge and charge powers of the energy storage device at time t node j; sigmacAnd σdRespectively reducing the cost for charging and discharging the energy storage device; sbRepresenting a set of energy storage device nodes in the power distribution network;
the specific expression of the operation cost of the controllable generator set is as follows:
Figure BDA0001677206860000035
in the formula, PG,j,tRepresenting the output of the controllable generator set at the node j in the time period t; bgRepresenting the power generation cost coefficient; sgRepresenting a set of controllable generator nodes in the power distribution network; qG,i,tAnd the reactive output of the controllable generator set at the time t node i.
Wherein, the step S300 further includes: further establishing coupling constraints among independent main bodies of the D-DOPF model, and obtaining the following D-DOPF model constraint conditions by combining DOPF model constraint conditions;
and (3) limiting the upper and lower limits of the node voltage:
Figure BDA0001677206860000036
Figure BDA0001677206860000037
Vmin≤Re{Vj,t}≤Vmax
wherein j and j1Representing a node, wherein P is active power injected by the node, and Q is reactive power injected by the node;
and (3) restraining the controllable generator set:
Figure BDA0001677206860000041
wherein the content of the first and second substances,
Figure BDA0001677206860000042
GP、
Figure BDA0001677206860000043
andQ Gthe upper limit and the lower limit of active output and reactive output of the generator set are respectively set; r isuAnd rdRespectively the climbing speed and the landslide speed of the generator set;
and (4) energy storage device restraint:
Figure BDA0001677206860000044
wherein the content of the first and second substances,
Figure BDA0001677206860000045
and
Figure BDA0001677206860000046
maximum charging and discharging power of the energy storage device respectively; eb,tThe storage capacity of the energy storage device is a time period t;
Figure BDA0001677206860000047
is the energy storage device capacity; SOCb,tThe state of charge of the energy storage device is a time period t;
Figure BDA0001677206860000048
andSOC brespectively the upper limit and the lower limit of the charge state of the energy storage device; etacAnd ηdThe charging efficiency and the discharging efficiency of the energy storage device are respectively obtained;
light abandoning restraint of the photovoltaic power supply:
Figure BDA0001677206860000049
wherein, Pav,j,tThe predicted output force of the photovoltaic power supply connected with the node j in the time period t is obtained; pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t;
Figure BDA00016772068600000410
representing a minimum power factor of the photovoltaic power inverter;
coupling constraints between independent bodies:
Figure BDA00016772068600000411
Figure BDA00016772068600000412
Figure BDA00016772068600000413
in the formula, ShRepresenting a set of photovoltaic access nodes, SgRepresenting a controllable generator set access node set, SbatRepresenting an energy storage access node set, h representing a photovoltaic node, g representing a controllable generator set node, b representing an energy storage node, x and y representing node injection power at a user side,
Figure BDA0001677206860000051
and
Figure BDA0001677206860000052
indicating that the node on the network side injects power.
Wherein, the step S400 specifically includes: and (3) relaxing coupling constraint by adopting a Lagrange relaxation method, increasing an adjacent penalty function and a coupling constraint penalty term on the basis, linearly representing the coupling constraint penalty term, and establishing respective sub-optimization models and Lagrange multiplier updating by respectively taking the network side, the user side photovoltaic power supply, the user side energy storage device and the user side controllable generator set as independent main bodies.
Wherein, the establishing of respective sub optimization models and the updating of Lagrange multipliers are as follows;
network side subject optimization model:
Figure BDA0001677206860000053
Figure BDA0001677206860000054
Figure BDA0001677206860000055
Vmin≤Re{Vj,t}≤Vmax
where k denotes the number of iterations, pi is the Lagrangian multiplier, and η and γ are penalty factors.
The method comprises the following steps of (1) optimizing a main body sub-optimization model of the photovoltaic power supply at a user side:
Figure BDA0001677206860000056
Figure BDA0001677206860000057
the user side controllable generator set main body optimization model comprises the following steps:
Figure BDA0001677206860000058
Figure BDA0001677206860000059
the user side energy storage device main body sub-optimization model comprises the following steps:
Figure BDA00016772068600000510
Figure BDA0001677206860000061
lagrange multiplier update:
Figure BDA0001677206860000062
Figure BDA0001677206860000063
Figure BDA0001677206860000064
Figure BDA0001677206860000065
Figure BDA0001677206860000066
Figure BDA0001677206860000067
wherein α ═ γ ═ η/2; pi is the lagrange multiplier.
Compared with the prior art, the implementation of the method provided by the embodiment of the invention has the following beneficial effects:
the DOPF model of the active power distribution network containing multiple sources and multiple subjects is established, the linear approximation is carried out on the alternating current power flow equation in the model, and meanwhile, the fact that the running state complementary constraint of the energy storage device can be deleted from the constraint condition under a certain condition is verified, so that the non-convex non-linear planning model is converted into the convex quadratic planning model, the solving difficulty is greatly reduced, and the solving efficiency is improved. Then, the power distribution network, the distributed power supply and the energy storage device are respectively used as independent main bodies, and boundary variable information only needs to be exchanged among the main bodies, so that the DOPF model is converted into the D-DOPF model. And solving by adopting an auxiliary problem principle, and decomposing the solution of the D-DOPF model into the solution of each independent main body quadratic programming sub model. And solving each independent main body quadratic programming sub-model by adopting a mature mathematical optimization solver CPLEX. The embodiment of the invention not only meets the privacy protection among all the benefit subjects, but also can realize global optimization in the optimization process.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a distributed dynamic optimal power flow method for an active power distribution network including a multi-source main body according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an IEEE 33 node system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a predicted output curve of the photovoltaic power supply according to an embodiment of the invention;
FIG. 4 is a schematic diagram of time-of-use electricity prices according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a normalized daily load variation curve according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a comparison of node voltage amplitudes according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating phase angle comparison of node voltages according to an embodiment of the present invention;
FIG. 8 is a first graph comparing the output of the controllable generator set according to the embodiment of the present invention;
FIG. 9 is a comparison graph of charge and discharge powers of an energy storage device according to an embodiment of the invention;
FIG. 10 is a comparison graph of reactive power generated/consumed by the photovoltaic power inverter according to the embodiment of the invention;
FIG. 11 is a graph comparing the node voltage amplitudes according to the embodiment of the present invention;
FIG. 12 is a graph comparing phase angles of voltages at nodes according to an embodiment of the present invention;
FIG. 13 is a graph illustrating a comparison of the output of the controllable generator set according to the embodiment of the present invention;
FIG. 14 is a graph comparing charge and discharge power of an energy storage device according to an embodiment of the invention;
fig. 15 is a comparison diagram of reactive power generation/consumption of the photovoltaic power inverter according to the embodiment of the invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical solution of the present invention, the following description is made by referring to the specific embodiments and the accompanying drawings.
Fig. 1 is a schematic flow chart of a distributed dynamic optimal power flow method for an active power distribution network including a multi-source main body according to an embodiment of the present invention, where the method includes the following steps:
the embodiment of the invention provides a distributed dynamic optimal power flow method for an active power distribution network containing a multi-source main body, which comprises the following steps:
s100, determining an optimization target and constraint conditions, and establishing a DOPF model of the active power distribution network containing multiple sources and multiple subjects;
s200, performing linear approximation on a power flow equation in the DOPF model of the active power distribution network, and converting an original nonlinear programming problem into a quadratic programming problem;
s300, respectively taking the power distribution network, the distributed power supply and the energy storage device as independent main bodies, and converting the DOPF model of the active power distribution network into a D-DOPF model of the active power distribution network;
s400, solving the D-DOPF model of the active power distribution network by adopting an auxiliary problem principle, and decomposing the solution of the D-DOPF model of the active power distribution network into solutions of a plurality of independent main body quadratic programming submodels.
In the embodiment of the invention, the multi-source multi-main-body is a main body such as a power distribution network, a distributed power supply and an energy storage device.
Preferably, in step S100, the optimization objectives are: minimizing the sum of the network loss cost of the power distribution network, the electricity purchasing cost of the power distribution network, the electricity generation cost of the controllable generator set, the light abandoning cost and the loss cost of the energy storage device; namely:
Figure BDA0001677206860000081
in the formula, NtFor the total number of time periods of the scheduling cycle, the day is divided into 96 time periods, and each time period is 15 min; pG,j,tRepresenting the output of the controllable generator set at the node j in the time period t; bgRepresenting the power generation cost coefficient; sgRepresenting a set of controllable generator nodes in the power distribution network; pdis,j,tAnd Pch,j,tRespectively representing the discharge and charge powers of the energy storage device at time t node j; sbRepresenting a set of energy storage device nodes in the power distribution network; sigmacAnd σdRespectively reducing the cost for charging and discharging the energy storage device; a ish、bhAnd chA cost coefficient for light abandonment; pav,j,tThe predicted output force of the photovoltaic power supply connected with the node j in the time period t is obtained; pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t; pΣ,tAnd QΣ,tInjecting active and reactive power of the power distribution network from the main network for a time period t; c0Active electricity purchasing cost coefficient for distribution network, C1A cost coefficient for the reactive power supply of the distribution network; pl,j,tRepresenting the load active power of a node j in a time period t; clThe unit cost of the active loss of the network.
The constraint conditions include: the node voltage upper and lower limits are restricted, the controllable generator set is restricted, the light abandoning amount upper and lower limits are restricted, and the energy storage device is restricted.
Node power balance equation:
Figure BDA0001677206860000082
in the formula, Vi,tAnd Vj,tVoltage amplitudes of a node i and a node j in a time period t are respectively; deltaij,tIs the voltage phase angle difference between node i and node j for time period t; gijAnd BijCorresponding elements of the node admittance matrix; qG,i,tAnd Ql,i,tAnd respectively the reactive output and the load reactive power of the controllable generator set at the time t node i.
And (3) limiting the upper and lower limits of the node voltage:
Figure BDA0001677206860000083
upper and lower limit restriction and climbing restriction of controllable generator set output power
Figure BDA0001677206860000084
In the formula (I), the compound is shown in the specification,
Figure BDA0001677206860000085
PG、
Figure BDA0001677206860000086
and QGThe upper limit and the lower limit of active output and reactive output of the generator set are respectively set; r isuAnd rdThe ramp rate and the landslide rate of the generator set are respectively. Δ t is the time interval of each period, i.e., 15 min.
And (4) energy storage device restraint:
the energy storage device constraints include maximum charge-discharge power constraint, energy transfer equation, state-of-charge constraint, running state complementary constraint (the energy storage device can only be in one of charge or discharge state in actual operation) and the like, namely:
Figure BDA0001677206860000091
in the formula (I), the compound is shown in the specification,
Figure BDA0001677206860000092
and
Figure BDA0001677206860000093
maximum charging and discharging power of the energy storage device respectively; eb,tThe storage capacity of the energy storage device is a time period t;
Figure BDA0001677206860000094
is the energy storage device capacity; SOCb,tThe state of charge of the energy storage device is a time period t;
Figure BDA0001677206860000095
andSOC brespectively the upper limit and the lower limit of the charge state of the energy storage device; etacAnd ηdThe charging and discharging efficiencies of the energy storage device are 0.85.
Light abandoning amount and reactive output constraint of the photovoltaic power supply:
Figure BDA0001677206860000096
in the formula (I), the compound is shown in the specification,
Figure BDA0001677206860000097
representing the minimum power factor of the photovoltaic power inverter.
It can be seen that in the distribution network DOPF model described by the formulas (1) to (6), the node power balance equation is in a nonlinear expression form, and meanwhile, the whole model presents strong non-convex characteristics due to the addition of the complementary constraint of the charging and discharging power of the energy storage device.
Preferably, the energy storage device constraints in step S100 include maximum charge-discharge power constraints, energy transfer equations, state of charge constraints, and running state complementary constraints; in order to reduce the difficulty of solving the model and make the solution of the model reliable and fast in calculation speed, the step S200 includes: the node voltage of each node in the network is linearly expressed by using the node injection power, and meanwhile, the charge and discharge power constraint of the energy storage device is removed, so that the original nonlinear programming problem is converted into a quadratic programming problem.
Specifically, the power flow equation is linearly approximated as follows:
suppose that the distribution network has N +1 nodes, where node 1 is connected to the main network and node N +1 is a balanced node. By using
Figure BDA0001677206860000098
And
Figure BDA0001677206860000099
representing sets of real and complex numbers, 0NAnd 1NRepresenting all 0 and all 1 vectors, its network equation can be written as:
Figure BDA00016772068600000910
in the formula (I), the compound is shown in the specification,
Figure BDA00016772068600000911
injecting a current vector for the node;
Figure BDA00016772068600000912
is a node voltage vector;
Figure BDA00016772068600000913
Figure BDA00016772068600000914
for node admittance matrix middle frontA submatrix corresponding to N nodes, which may be written in the form of Y ═ G + jB according to its definition, where
Figure BDA00016772068600000915
A sub-matrix corresponding to the (N + 1) th node in the node admittance matrix represents that elements in the sub-matrix are not zero; since the N +1 th node is a balanced node, let V0＝1、θ0＝0。
The complex power injected by a node can be expressed as:
S＝diag(V)I* (8)
the expression form of the injection current of the unbalanced node can be derived from the formula (7):
Figure BDA00016772068600000916
formula (8) and formula (9) are combined to obtain:
Figure BDA00016772068600000917
the voltage V in formula (10) may be represented as V ═ Vnom+ Δ V, where Vnom＝|Vnom|∠θnomThe voltage value in the steady state is Δ V, which is a deviation value around the steady-state voltage after the system is disturbed.
As can be seen from the formula (9),
Figure BDA0001677206860000101
order to
Figure BDA0001677206860000102
I.e., the node voltage value at which the node injection current is 0. The voltage deviation value can therefore be expressed as:
Figure BDA0001677206860000103
in the formula, Y-1Is a node impedance matrix, from which Y-1R + jX, wherein
Figure BDA0001677206860000104
The complex power injected by a node can also be expressed as:
S＝P+jQ (12)
in the formula, P is the active power injected by the node, and Q is the reactive power injected by the node. Substituting equation (12) for equation (11) and separating the real and imaginary parts of the voltage yields:
Figure BDA0001677206860000105
Figure BDA0001677206860000106
due to thetanom≈0N、︱Vnom︱＝1NEquations (13) and (14) can be simplified as follows:
ΔVre＝RP+XQ (15)
ΔVim＝XP-RQ (16)
an agent V may be represented by an approximate magnitude and phase angle of voltagenom︱+ΔVre，θ＝θnom+ΔVim. The magnitude and phase angle of this node voltage can be expressed approximately as:
Figure BDA0001677206860000107
Figure BDA0001677206860000108
in the formula, N' is a distribution network node set except a balance node.
Therefore, the net loss in the objective function (1) can be expressed as:
Figure BDA0001677206860000109
in the formula, epsilon represents a set of distribution network lines.
Meanwhile, the power P injected into the power distribution network by the main network at the moment tΣ,tAnd QΣ,tThe method can be expressed in the form of removing the sum of injected power of other nodes except the nodes connected with the main distribution network for the loss reduction of the whole network, namely:
Figure BDA00016772068600001010
Figure BDA0001677206860000111
therefore, the linear approximate form of the node power balance equation in equation (2) can be expressed as follows:
Figure BDA0001677206860000112
the upper and lower voltage limit constraints in equation (3) can be expressed as:
Vmin≤Re{Vj,t}≤Vmax (23)
the energy storage device constraint (5) comprises energy storage device charge and discharge power complementary constraint, and the existence of the constraint enables the whole model to be strong and non-convex, so that the solving difficulty is greatly increased. However, in the model constructed by the invention, the object includes the cost for wasting the energy storage device, namely:
Figure BDA0001677206860000113
it is apparent that equation (24) relates to the charging and discharging power P of the energy storage devicech,j,tAnd Pdis,j,tIs increased, so when P isch,j,tAnd Pdis,j,tSatisfies the following conditions: pch,j,t≥0，Pdis,j,tNot less than 0, the complementary constraint of the operation state is necessarily satisfiedPch,j,t·Pdis,j,tWhen 0, the following is demonstrated:
suppose { Pch,t 1,Pdis,t 1And { P }ch,t 2,Pdis,t 2Is two groups of charging and discharging power of the energy storage device, Pbat,tThe power injected into the power grid by the energy storage device at the moment t meets the following requirements:
Figure BDA0001677206860000114
this time is:
Figure BDA0001677206860000115
by
Figure BDA0001677206860000116
It is known that Pch,t 1<Pch,t 2And P isdis,t 1<Pdis,t 2While due to FbatRelates to the charging and discharging power P of the energy storage devicech,tAnd Pdis,tIs an increasing function of, knowing Fbat(Pch,t 1,Pdis,t 1)<Fbat(Pch,t 2,Pdis,t 2). Therefore, in the optimization process, when the objective function is to minimize the energy storage device loss cost, the optimal solution { P }ch,t,Pdis,tInevitably satisfy the running state complementary constraint Pch,j,t·Pdis,j,t＝0。
After the complementary constraint of the charging and discharging power of the energy storage device is removed and the linear approximate expression form of the alternating current power flow equation is adopted, the original strong non-convex non-linear programming problem can be converted into a convex quadratic programming problem, and the solving difficulty can be obviously reduced.
Preferably, the DOPF model (1) - (6) shows that the DOPF model of the power distribution network containing multiple sources and multiple subjects can be divided into four parts, namely a network side, a user side photovoltaic power supply, a user side energy storage device and a user side controllable generator set. Therefore, the step S300 includes: and splitting the DOPF model of the active power distribution network containing the multi-source and multi-main bodies into a D-DOPF model comprising a network side, a user side photovoltaic power supply, a user side energy storage device and a user side controllable generator set.
Preferably, the network side is composed of all nodes of the power distribution network, and the optimization aim is to minimize the sum of the network loss cost and the cost of purchasing power from the main network; the user side photovoltaic power supply is composed of all nodes connected into the photovoltaic power supply, and the optimization goal is that the cost of light abandoning is minimum; the energy storage device at the user side consists of all nodes connected into the energy storage device, and the optimization aim is to minimize the loss cost of the energy storage device; the user side controllable generator set is composed of all nodes connected into the generator set, and the optimization goal is that the generation cost of the controllable generator set is minimum.
To implement the distributed optimization computation, coupling constraints should first be established. Four pairs of virtual variables are introduced for this purpose:
Figure BDA0001677206860000121
and
Figure BDA0001677206860000122
indicating the node injection power on the network side, by xhAnd yhRepresenting the node injected power, x, of the photovoltaic power supply at the user sidebatAnd ybatIndicating the injected power, x, of the user-side energy storage nodegAnd ygThe node injection power of the user side controllable generator set is represented, and the DOPF models (1) - (6) can be rewritten into the following form:
the optimization target of the D-DOPF model is as follows:
min Cu(Vj,t,Pj,t,Qj,t)+Cbuy(Vj,t,Pj,t,Qj,t)+Cpv(Pc,t,Qc,t)+Cbat(Pch,t,Pdis,t)+Cgen(PG,t,QG,t) (27)
wherein, the first term is the power distribution network loss cost, and the specific expression is as follows:
Figure BDA0001677206860000123
the second item is the electricity purchasing cost of the power distribution network, and the specific expression is as follows:
Figure BDA0001677206860000124
the third term is photovoltaic abandoned light and reactive power output cost, and the specific expression is as follows:
Figure BDA0001677206860000125
the fourth term is the loss cost of the energy storage device, and the specific expression is as follows:
Figure BDA0001677206860000126
the fifth item is the operation cost of the controllable generator set, and the specific expression is as follows:
Figure BDA0001677206860000127
preferably, the step S300 further includes: further establishing coupling constraints among independent main bodies of the D-DOPF model, and obtaining the following D-DOPF model constraint conditions by combining DOPF model constraint conditions;
and (3) limiting the upper and lower limits of the node voltage:
Figure BDA0001677206860000128
Vmin≤Re{Vj,t}≤Vmax (34)
wherein j and j1Representing the node, P is the active power injected by the node, Q is the reactive power injected by the node,
Figure BDA0001677206860000129
Figure BDA0001677206860000131
and (3) restraining the controllable generator set:
Figure BDA0001677206860000132
wherein the content of the first and second substances,
Figure BDA0001677206860000133
GP、
Figure BDA0001677206860000134
andQ Gthe upper limit and the lower limit of active output and reactive output of the generator set are respectively set; r isuAnd rdRespectively the climbing speed and the landslide speed of the generator set;
and (4) energy storage device restraint:
Figure BDA0001677206860000135
wherein the content of the first and second substances,
Figure BDA0001677206860000136
and
Figure BDA0001677206860000137
maximum charging and discharging power of the energy storage device respectively; eb,tThe storage capacity of the energy storage device is a time period t;
Figure BDA0001677206860000138
is the energy storage device capacity; SOCb,tThe state of charge of the energy storage device is a time period t;
Figure BDA0001677206860000139
andSOC brespectively charge the energy storage deviceUpper and lower limits of state; etacAnd ηdThe charging efficiency and the discharging efficiency of the energy storage device are respectively obtained;
light abandoning restraint of the photovoltaic power supply:
Figure BDA00016772068600001310
wherein, Pav,j,tThe predicted output force of the photovoltaic power supply connected with the node j in the time period t is obtained; pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t;
Figure BDA00016772068600001311
representing a minimum power factor of the photovoltaic power inverter;
coupling constraints between independent bodies:
Figure BDA00016772068600001312
Figure BDA00016772068600001313
Figure BDA0001677206860000141
preferably, the step S400 specifically includes: and (3) relaxing coupling constraint by adopting a Lagrange relaxation method, increasing an adjacent penalty function and a coupling constraint penalty term on the basis, linearly representing the coupling constraint penalty term, and establishing respective sub-optimization models and Lagrange multiplier updating by respectively taking the network side, the user side photovoltaic power supply, the user side energy storage device and the user side controllable generator set as independent main bodies.
Preferably, the respective sub-optimization models and lagrangian multipliers are established and updated as follows;
1) network side main body optimization model
Figure BDA0001677206860000142
Figure BDA0001677206860000143
2) User side photovoltaic power main body sub-optimization model
Figure BDA0001677206860000144
Figure BDA0001677206860000145
3) User-side controllable generator set main body and sub-optimization model
Figure BDA0001677206860000146
Figure BDA0001677206860000147
4) User side energy storage device main body sub-optimization model
Figure BDA0001677206860000148
Figure BDA0001677206860000151
5) Lagrange multiplier update
Figure BDA0001677206860000152
In the formula, three parameters α, β, and γ are included, and in order to ensure convergence, the three parameters are generally required to satisfy the relationship α ═ γ ═ β/2; pi is the lagrange multiplier, k is the number of iterations, and η and γ are penalty factors.
As can be seen from the distributed models (30) - (38), only boundary variable values need to be transferred between the subjects, so that confidentiality among the subjects is guaranteed while each subject can be independently optimized.
The following describes in detail the application and calculation process of the method according to an embodiment of the present invention, taking an IEEE 33 node system including photovoltaic power supplies, controllable generator sets, and energy storage devices as an example, as shown in fig. 2, the system accesses two controllable generator sets at nodes 8 and 30, two energy storage devices at nodes 15 and 21, and two photovoltaic power supplies at nodes 16 and 22. The power reference value is 1MVA, the voltage reference value is 12.66kV, and the node 1 is a balanced node.
The maximum output of the controllable generator set is 600kW, the minimum output is 0kW, the climbing and landslide rates are 200kW/h, and the power generation cost coefficient bgIs 0.66 yuan/kW.h. The maximum charging and discharging power of the stored energy is 200kW, and the capacity is 800 kW.h. The predicted output curve of the photovoltaic power supply is shown in fig. 3, the capacity of the photovoltaic power supply is 600kW, the photovoltaic power supply operates at a constant power factor, and the power factor cos θ is 0.85. Main grid electricity purchasing unit price C0The time-of-use electricity rates as in fig. 4 are used. Cost per unit loss C1Is 0.68 yuan/kW.h. The individual load power changes according to the 96-point normalized daily load curve shown in fig. 5, the load of 1.0 at the 68 th time period corresponds to the original load value in the system, and the load power factor of each time period is kept unchanged. The upper and lower limits of the node voltage are set to 1.06p.u. and 0.94p.u.
Wherein the linear approximation accuracy is verified as follows:
table 1 lists the objective function calculation results of the nonlinear programming DOPF model and the quadratic programming DOPF model, and the calculation results of the voltage amplitude and the phase angle are shown in fig. 6 and fig. 7, which shows that the voltage amplitude and the phase angle calculated after the linearization method provided by the embodiment of the present invention linearizes the power flow equation and removes the complementary constraint of the charge and discharge power of the energy storage device are very small in deviation.
TABLE 1 comparison of non-linear and linear approximation model results
Figure BDA0001677206860000161
Fig. 8 to 10 show the output of the controllable generator set, the charging and discharging of the energy storage device, and the light rejection curve of the photovoltaic power supply, respectively, and it can be seen that the linear approximation of the power flow equation has little influence on the optimization result, so that the linearization manner of the power flow equation provided by the embodiment of the present invention can be considered to be accurate. Reactive power output of the controllable generator set in 96 periods is 400kW, and active light curtailment power of the photovoltaic power supply is 0. In fig. 9, P is shown in the figure for comparison of charge and discharge powerchAnd taking the negative value. It can be seen that the energy storage device does not charge and discharge simultaneously, so in the model proposed by the present invention, the complementary constraints of the charging and discharging power of the energy storage device can be removed.
The accuracy of the distributed algorithm is verified as follows:
table 2 lists the calculation results of the centralized and distributed objective functions after linear approximation, and fig. 11 to 15 are comparison diagrams of the voltage amplitude, the voltage phase angle, the controllable generator set output, the energy storage device charge and discharge power, and the photovoltaic power rejection, respectively.
TABLE 2 comparison of centralized and distributed model calculations
Figure BDA0001677206860000162
Table 3 lists the quadratic programming D-DOPF model solution time and the nonlinear programming D-DOPF model solution time, and it can be seen that the computation time of the quadratic programming D-DOPF model is greatly reduced compared to the nonlinear programming D-DOPF model. It can therefore be concluded that: the quadratic programming D-DOPF model provided by the method of the embodiment of the invention can improve the calculation efficiency while ensuring higher precision.
TABLE 3 comparison of calculated times for the two models
Figure BDA0001677206860000171
As can be seen from the above description of the embodiments, the method of the embodiments of the present invention has the following beneficial effects:
(1) the method provided by the embodiment of the invention builds the distributed convex quadratic programming dynamic optimal power flow model of the active power distribution network containing multiple sources and multiple main bodies.
The model comprises a plurality of distributed resources such as a photovoltaic power supply, a controllable generator set and an energy storage device, each distributed resource is used as an independent main body to be optimized, and the schedulability of the distributed resources is achieved. Meanwhile, a power flow equation in the model is linearized, and the complementary constraint of the charging and discharging power of the energy storage device in the model is verified to be removed, so that the original non-convex nonlinear programming model is converted into a convex quadratic programming model, and the solving difficulty is greatly reduced.
(2) The method provided by the embodiment of the invention provides a rapid algorithm for solving a distributed convex quadratic programming dynamic optimal power flow model of an active power distribution network containing multiple sources and multiple subjects based on an auxiliary problem principle.
The algorithm is based on an auxiliary problem principle, the power distribution network and each distributed resource are decoupled, the power distribution network and each distributed resource are respectively used as independent main bodies, distributed solving is carried out on sub-optimization models of the independent main bodies, and only boundary variable information needs to be transmitted between the independent main bodies. And each independent main body is solved for the quadratic programming sub-model by adopting a mature mathematical optimization solver CPLEX, so that the solving efficiency is high. Meanwhile, the algorithm not only adds an adjacent penalty function, but also introduces a coupling constraint penalty term, so that the convexity of the target function is enhanced, and the oscillation phenomenon generated by Lagrange relaxation iteration is inhibited, therefore, the distributed convex quadratic programming dynamic optimal power flow algorithm of the active power distribution network based on the auxiliary problem principle has good convergence.
The parts of the method in the embodiment of the present invention that are not developed can refer to the corresponding parts of the method in the above embodiment, and are not developed in detail here.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example" or "some examples" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing elements, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (4)

1. A distributed dynamic optimal power flow method for an active power distribution network containing a multi-source main body is characterized by comprising the following steps:
s100, determining an optimization target and constraint conditions, and establishing a DOPF model of the active power distribution network containing multiple sources and multiple subjects;
wherein the optimization objective is: minimizing the sum of the network loss cost of the power distribution network, the electricity purchasing cost of the power distribution network, the electricity generation cost of the controllable generator set, the light abandoning cost and the loss cost of the energy storage device; the constraint conditions include: node voltage upper and lower limit constraints, controllable generator set constraints, light abandoning amount upper and lower limit constraints and energy storage device constraints; the energy storage device constraints comprise maximum charge and discharge power constraints, energy transfer equations, charge state constraints and running state complementary constraints;
s200, performing linear approximation on a power flow equation in the DOPF model of the active power distribution network, performing linear expression on the node voltage of each node in the network by using node injection power, removing the charge and discharge power constraint of an energy storage device, and converting an original nonlinear programming problem into a quadratic programming problem;
s300, respectively taking the power distribution network, the distributed power supply and the energy storage device as independent main bodies, and splitting the DOPF model of the active power distribution network containing the multi-source and multi-main bodies into a D-DOPF model comprising a network side, a user side photovoltaic power supply, a user side energy storage device and a user side controllable generator set;
s400, solving the D-DOPF model of the active power distribution network by adopting an auxiliary problem principle, and decomposing the solution of the D-DOPF model of the active power distribution network into solutions of a plurality of independent main body quadratic programming submodels; the network side consists of all nodes of the power distribution network, and the optimization aim is to minimize the sum of the network loss cost and the power purchasing cost from the main network; the user side photovoltaic power supply is composed of all nodes connected into the photovoltaic power supply, and the optimization goal is that the cost of light abandoning is minimum; the energy storage device at the user side consists of all nodes connected into the energy storage device, and the optimization aim is to minimize the loss cost of the energy storage device; the user side controllable generator set is composed of all nodes connected into the generator set, and the optimization goal is that the generation cost of the controllable generator set is minimum;
the optimization target of the D-DOPF model is as follows:
min Cu(Vj,t,Pj,t,Qj,t)+Cbuy(Vj,t,Pj,t,Qj,t)+Cpv(Pc,t,Qc,t)+Cbat(Pch,t,Pdis,t)+Cgen(PG,t,QG,t)；
the specific expression of the network loss cost of the power distribution network is as follows:
Figure FDA0003151941160000011
the specific expression of the electricity purchasing cost of the power distribution network is as follows:
Figure FDA0003151941160000021
in the formula, ClThe unit cost epsilon for the active loss of the network represents the set of the lines of the power distribution network, N is the set of all nodes, N' is the set of all nodes except the balance node, C0Active electricity purchasing cost coefficient for distribution network, C1Cost factor for reactive power supply of distribution network, NtIs the total number of time periods of the scheduling cycle;
the specific expressions of the photovoltaic abandoned light and the reactive power output cost are as follows:
Figure FDA0003151941160000022
in the formula, Pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t; a ish、bhAnd chA cost coefficient for light abandonment;
the specific expression of the loss cost of the energy storage device is as follows:
Figure FDA0003151941160000023
in the formula, Pdis,j,tAnd Pch,j,tRespectively representing the discharge and charge powers of the energy storage device at time t node j; sigmacAnd σdRespectively reducing the cost for charging and discharging the energy storage device; sbRepresenting a set of energy storage device nodes in a power distribution network；
The specific expression of the operation cost of the controllable generator set is as follows:
Figure FDA0003151941160000024
in the formula, PG,j,tRepresenting the output of the controllable generator set at the node j in the time period t; bgRepresenting the power generation cost coefficient; sgRepresenting a set of controllable generator nodes in the power distribution network; qG,i,tAnd the reactive output of the controllable generator set at the time t node i.
2. The distributed dynamic optimal power flow method for the active power distribution network with the multi-source bodies according to claim 1, wherein the step S300 further comprises: further establishing coupling constraints among independent main bodies of the D-DOPF model, and obtaining the following D-DOPF model constraint conditions by combining DOPF model constraint conditions;
and (3) limiting the upper and lower limits of the node voltage:
Figure FDA0003151941160000025
Figure FDA0003151941160000026
Vmin≤Re{Vj,t}≤Vmax
wherein j and j1Representing different nodes of the same line, wherein P is active power injected by the nodes, and Q is reactive power injected by the nodes;
and (3) restraining the controllable generator set:
Figure FDA0003151941160000031
wherein the content of the first and second substances,
Figure FDA0003151941160000032
GP、
Figure FDA0003151941160000033
andQ Gthe upper limit and the lower limit of active output and reactive output of the generator set are respectively set; r isuAnd rdRespectively the climbing speed and the landslide speed of the generator set;
and (4) energy storage device restraint:
Figure FDA0003151941160000034
wherein the content of the first and second substances,
Figure FDA0003151941160000035
and
Figure FDA0003151941160000036
maximum charging and discharging power of the energy storage device respectively; eb,tThe storage capacity of the energy storage device is a time period t;
Figure FDA0003151941160000037
is the energy storage device capacity; SOCb,tThe state of charge of the energy storage device is a time period t;
Figure FDA0003151941160000038
andSOC brespectively the upper limit and the lower limit of the charge state of the energy storage device; etacAnd ηdThe charging efficiency and the discharging efficiency of the energy storage device are respectively obtained;
light abandoning restraint of the photovoltaic power supply:
Figure FDA0003151941160000039
wherein, Pav,j,tThe predicted output force of the photovoltaic power supply connected with the node j in the time period t is obtained; pc,j,tThe light abandoning amount of the photovoltaic power supply connected with the node j in the time period t; qc,j,tThe photovoltaic power supply is connected to the node j to obtain useless power generated/consumed by the inverter in a time period t;
Figure FDA00031519411600000310
representing a minimum power factor of the photovoltaic power inverter;
coupling constraints between independent bodies:
Figure FDA00031519411600000311
Figure FDA00031519411600000312
Figure FDA0003151941160000041
in the formula, ShRepresenting a set of photovoltaic access nodes, SgRepresenting a controllable generator set access node set, SbatRepresenting an energy storage access node set, h representing a photovoltaic node, g representing a controllable generator set node, b representing an energy storage node, x and y representing node injection power at a user side,
Figure FDA0003151941160000042
and
Figure FDA0003151941160000043
indicating that the node on the network side injects power.
3. The distributed dynamic optimal power flow method for the active power distribution network with the multi-source main body according to claim 2, wherein the step S400 specifically includes: and (3) relaxing coupling constraint by adopting a Lagrange relaxation method, increasing an adjacent penalty function and a coupling constraint penalty term on the basis, linearly representing the coupling constraint penalty term, and establishing respective sub-optimization models and Lagrange multiplier updating by respectively taking the network side, the user side photovoltaic power supply, the user side energy storage device and the user side controllable generator set as independent main bodies.
4. The method for distributed dynamic optimal power flow of the active power distribution network with the multi-source main body according to claim 3, wherein the respective sub-optimization models and Lagrangian multipliers are established and updated as follows;
network side subject optimization model:
Figure FDA0003151941160000044
Figure FDA0003151941160000045
Figure FDA0003151941160000046
Vmin≤Re{Vj,t}≤Vmax
in the formula, k represents the iteration times, pi is a Lagrange multiplier, and eta and gamma are penalty factors;
the method comprises the following steps of (1) optimizing a main body sub-optimization model of the photovoltaic power supply at a user side:
Figure FDA0003151941160000047
Figure FDA0003151941160000048
the user side controllable generator set main body optimization model comprises the following steps:
Figure FDA0003151941160000051
Figure FDA0003151941160000052
the user side energy storage device main body sub-optimization model comprises the following steps:
Figure FDA0003151941160000053
Figure FDA0003151941160000054
lagrange multiplier update:
Figure FDA0003151941160000055
Figure FDA0003151941160000056
Figure FDA0003151941160000057
Figure FDA0003151941160000058
Figure FDA0003151941160000059
Figure FDA00031519411600000510
wherein α ═ γ ═ η/2; pi is the lagrange multiplier.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105186500A (en) * 2015-09-17 2015-12-23 浙江工商大学 Power distribution network energy dispersion coordination and optimization method based on reweighted acceleration Lagrangian
CN105244869A (en) * 2015-10-13 2016-01-13 国网山东省电力公司电力科学研究院 Dynamic random scheduling control method for power distribution network containing micro-grid
CN105846456A (en) * 2016-05-13 2016-08-10 清华大学 Alternating current and direct current interconnected power grid wind and fire coordination dynamic economy scheduling optimization method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105186500A (en) * 2015-09-17 2015-12-23 浙江工商大学 Power distribution network energy dispersion coordination and optimization method based on reweighted acceleration Lagrangian
CN105244869A (en) * 2015-10-13 2016-01-13 国网山东省电力公司电力科学研究院 Dynamic random scheduling control method for power distribution network containing micro-grid
CN105846456A (en) * 2016-05-13 2016-08-10 清华大学 Alternating current and direct current interconnected power grid wind and fire coordination dynamic economy scheduling optimization method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Network Topology Independent Multi-Agent Dynamic Optimal Power Flow for Microgrids With Distributed Energy Storage Systems;Thomas Morstyn 等;《IEEE TRANSACTIONS ON SMART GRID》;20161122;第9卷(第4期);第3419-3429页 *

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