CN113410870B - Distributed coordination method for power distribution network based on static security domain - Google Patents
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Classifications
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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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
- 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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- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention provides a distributed coordination method of a power distribution network based on a static security domain, which mainly comprises the following steps: the method comprises the steps of defining a static voltage safety domain on an injection space, dividing a power distribution network optimization model into areas, determining an upstream area optimization model and a downstream area optimization model, and determining the flow of the whole distributed method. Since the constraint conditions formed based on the static safety domain become linear combination inequality of the variables, and the objective function is the same as the variables in the constraint conditions, the optimization process of each region and the determination of the optimal conditions become extremely simple, and the number of iterations required for the whole optimization process is small.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to a distributed coordination optimization method for a multi-region power distribution network.
Background
With the deregulation of the power market, the permeability of a large number of distributed energy sources (distributed energy source, DER) such as wind power generation and photovoltaic power generation in the distribution network is gradually increased. On one hand, DER can participate in voltage control by adjusting reactive power injection and other modes, so that adjustment measures of the system are enriched. On the other hand, the power in the power distribution network becomes bidirectional flow after DER is integrated, and the operation condition is more complex than that of the traditional power distribution network. Therefore, in order to ensure safe operation of the power distribution network, the DER with high permeability needs to be fully considered, and a reasonable scheduling method is formulated.
The conventional scheduling method is centralized [1] . The method needs to collect whole network data in a centralized way when in application, and integrates the whole optimization problem into a mixed integer nonlinear programming problem. Because the constraint condition presents nonlinearity, the data maintenance difficulty and the calculation burden are great. Meanwhile, the method needs to uniformly schedule all DERs and other controllable devices (such as reactive compensation devices, controllable loads and the like), and when the number of controllable nodes is large, the method has the problems of difficult operation and overlarge communication pressure, and is difficult to meet the requirements of real-time application. Therefore, one common class of processing methods is based on hierarchical clustering coordination architecture [2] The method is characterized in that the problem is distributed, and the method firstly decomposes the whole power distribution network into a plurality of sub-areas [3] . Later in operation, each region accounts for self-constraints and optimizes and communicates with each other forGlobal optima are sought. However, when the method is applied to a multi-region power distribution network containing DER with high permeability, the number and variety of variables to be considered in each region are still large due to the increasing number of power electronic devices and energy storage devices [4] The optimization speed in the region needs to be improved. Meanwhile, in the aspect of processing multi-region distributed optimization, the common method has to be improved in convergence [5] It is necessary to design a distributed approach with good convergence.
[ reference ]
[1]F.Capitanescu,I.Bilibin,and E.R.Ramos.A comprehensive centralized approach for voltage constraints management in active distribution grid[J].IEEE Transactions on Power Systems,2014,29(2):933-942.
[2] Yu Yixin, liu Yanli, qin Chao, sun Bing. Hierarchical clustered grid architecture [ J ]. Power system protection and control 2020, 48 (22): 1-8.
[3]Miguel F.Anjos,Andrea Lodi,and Mathieu Tanneau.A decentralized framework for the optimal coordination of distributed energy resources[J].IEEE Transactions on Power Systems,2019,34(1):349-359.
[4] Guo Qingyuan, wu Jiekang, mo Chao, xu Honghai. New energy distribution network voltage reactive power collaborative optimization model based on mixed integer second order cone planning [ J ]. Chinese motor engineering journal, 2018, 38 (5): 1385-1396.
[5]Ye Guo,Lang Tong,Wenchuan Wu,Boming Zhang and Hongbin Sun.Coordinated multi-area economic dispatch via critical region projection[J].IEEE Transactions on Power Systems,2017,32(5):3736-3746.
Disclosure of Invention
In consideration of the problem that the existing method has long solving time when processing the operation constraint condition of the multi-region power distribution network, the invention provides a distributed coordination method of the power distribution network based on a static security domain, and the invention provides a distributed optimization strategy based on the static security domain in the node power injection space.
The invention adopts the following technical means:
a distributed coordination method of a power distribution network based on a static security domain comprises the following steps:
defining a static voltage safety domain on a power injection space according to distribution network configuration characteristics, wherein the distribution network configuration characteristics comprise a node set and a branch number set, and simultaneously taking the direction of power flowing from a load and distributed power generation to a power grid as the positive direction of power injection;
constructing a centralized optimization model based on a static security domain by taking the minimum overall network loss as a power distribution network optimization target, decoupling the centralized optimization model based on the static security domain to obtain an upstream region and a downstream region of a system, and deconstructing the centralized optimization model based on the static security domain based on a tie line equivalence principle of the upstream region and the downstream region;
determining an upstream region optimization model and a downstream region optimization model based on the model structure result;
initializing operation parameters of the upstream region optimization model and the downstream region optimization model respectively, and starting the mutual communication of the upstream region and the downstream region;
generating a static security domain boundary hyperplane expression by the upstream region according to the topological structure and the root node of the region, establishing an optimization model and solving to obtain operation parameters; generating a static security domain boundary hyperplane expression by the downstream region according to the topological structure and the upstream region iterative voltage, and simultaneously establishing an optimization model and solving to obtain operation parameters; and when the system residual error converges, ending the optimization process, otherwise, updating the operation parameters of the upstream region optimization model and the downstream region optimization model, and continuing iteration.
Further, the power injection spatial static voltage safety domain is:
the node number of the power distribution network is n+1, the branch number is nb, the node 0 is a loose node, N= {1,2,.. 1 ,l 2 ,...,l nb The symbol "represents a set of all lines, V i m Calculating a node voltage lower limit; v (V) i M To calculate the upper limit of the node voltage, V 0 To relax the node voltage, P j For node j active power, Q j Reactive power for node j;for the upper limit of line current, P i m For node i active power lower limit, P i M Is the upper limit of active power; />For the lower reactive power limit of node i,for node i reactive power upper limit R l,i +jX l,i For line l i Impedance of-> For the equivalent resistance between nodes i and j, which is related to the node voltage amplitude constraint, +.>For equivalent reactance between nodes i and j related to node voltage magnitude constraint, +.>For the equivalent resistance between nodes i and j, which is related to node current magnitude constraint, +.>For the equivalent reactance between nodes i and j related to node current magnitude constraint, it can be generated according to network topology and line impedance:
wherein D is i For a set of node i and its downstream nodes,route line set B for node i to node 0 i Total impedance of->Route set B for node j to node 0 j Total impedance of->Route set B for node s to node 0 s Node s is the number of the intersection point of node j with the upstream head of node i.
Further, the centralized optimization model based on the static security domain is:
the deconstructed objective function is:
further, the upstream region optimization model is:
reference variableThen, for any node i ε N in the upstream region C1 The voltage constraint of (2) can be expressed as
For any line l in the upstream zone j ∈B C1 The current constraint of (2) can be expressed as
Node power constraints may be expressed as
The power balance constraint is:
further, the downstream region optimization model is:
wherein lambda is P,C1 And lambda (lambda) Q,C1 Respectively isAnd->The corresponding lagrangian multiplier has the following expression:
wherein,,and->Respectively, node i epsilon N C1 The upper and lower limits of the voltage restrict the corresponding multiplier; mu (mu) P,C1 Sum mu Q,C1 Is the multiplier corresponding to the upstream region power balance,
reference to boundary node additional voltage variable V C1 ：
The node voltage and line current constraints in the downstream region can then be expressed as:
wherein the method comprises the steps ofAn equivalent resistance formed according to (2) for node k as the head node, +.>Equivalent reactance formed according to (2) for node k as the head node, +.>Is the equivalent resistance formed according to (3) with node k as the first node, +.>The equivalent reactance formed according to formula (3) with node k as the first node;
node power constraints may be expressed as
Active and reactive power constraints on the tie line:
power balance constraint for downstream region:
compared with the prior art, the invention has the following advantages:
the invention provides a distributed optimization strategy based on a static security domain in a node power injection space. The optimization problem is first decomposed into a plurality of sub-problems with linear constraints based on static security domains based on distribution network region division. And further, a distributed optimization model is established by taking the minimum overall network loss of the power distribution network as a target. And then realizing the coordinated operation among the areas. Since the constraint conditions formed based on the static security domain become linear combination inequality of the variables, and the objective function is the same as the variables in the constraint conditions, the optimization process of each region becomes extremely simple, and the number of iterations required for the whole optimization process is small.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flow chart of a distributed coordination method of a power distribution network based on a static security domain.
FIG. 2 is an example power distribution network regional equivalent model of the present invention, where (a) is the entire power distribution network equivalent model and (b) is the upstream regional equivalent model; (c) is a downstream region equivalent model.
FIG. 3 is an example PG & E69 node system of the present invention.
FIG. 4 is a graph of daily load and daily active power change of a distributed energy source according to an embodiment of the present invention.
FIG. 5 is a graph showing daily variation of target values obtained by the method of the present invention and the centralized method before optimization.
FIG. 6 is an all day node voltage magnitude after optimization using the method of the present invention.
FIG. 7 is a plot of iteration process error as a function of iteration number for the implementation of the method of the present invention.
Fig. 8 is an iterative process of boundary variables between region C1 and region C2 at 13 hours of the iterative process when the method of the present invention is implemented.
Fig. 9 is an iterative process of boundary variables between region C1 and region C3 at 13 hours of the iterative process when the method of the present invention is implemented.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, the invention provides a distributed coordination method of a power distribution network based on a static security domain, which mainly comprises the following steps:
1. a static voltage safety domain over the power injection space is defined.
For a power distribution network with node number of n+1 and branch number of nb, it is assumed that all nodes of the system are numbered in order from small to large, and node 0 is a loose nodePoints, denoted by n= {1,2,..n } represents the set of all nodes; with b= { l 1 ,l 2 ,...,l nb And represents a set of all lines. Meanwhile, the direction of power flowing from the load and the distributed generation to the power grid is taken as the positive direction of power injection. By operating point x β ＝(P 1 ,...,P n ,Q 1 ,...,Q n ) T ∈R 2n The defined power injection spatially static security domain handles the optimization problem. The static safety domain Ω of all safety operation constraints (node voltage amplitude constraints, line current constraints, node active power and reactive power capacity constraints) of the grid are considered on the node power injection space ss Can be expressed as:
in the formula (1), the superscripts M and M respectively represent the upper limit and the lower limit of the variable, R l,i +jX l,i For line l i Is used to determine the impedance of the (c) signal, and->The equivalent resistance and equivalent reactance between nodes i and j, respectively, associated with node voltage magnitude and line current constraints, may be generated from the network topology and line impedance:
in the formula (2) and the formula (3), D i For node i and itsA set of downstream nodes.
The number of the first intersection point between the node j and the upstream of the node i.
2. Deconstructing an optimization model of the power distribution network:
for a power distribution network in which active and reactive equipment participate in optimization work, selecting a power distribution network optimization target as the minimum overall network loss, and a centralized optimization model based on a static security domain can be expressed as:
the above-mentioned optimization model needs to be decoupled in view of the smart grid distributed control framework. The decoupling process is described herein with respect to the power distribution network shown in fig. 1 as an example. The whole system can be divided into an upstream area C 1 And downstream region C 2 And forms the set c= { C 1 ,C 2 }. At the same time, the upstream-downstream relation is determined according to the power flow direction on the connecting line, namely C 1 Is the upstream region, C 2 Is the downstream region. C (C) 1 And C 2 Through the connecting line l k (first and last nodes are j and k respectively) interconnections with power on the interconnecting lines ofAfter equivalence, C 1 Preserve node k and store C 2 Load aggregated as node k, its value is +.>At the same time, C 2 Let node k be the root node, flow into C 2 The power of (2) is also->The corresponding node set and line set are N respectively C1 And N C2 ，B C1 And B C1 ；C 1 And C 2 The node injection vectors of (2) are +.>And->And +.>And->In addition, the objective function can be described as
3. An upstream region optimization model is determined.
For the upstream region, denoted C 1 The variables areIts corresponding optimization model can be expressed as:
reference variableAfter that, for C 1 Any node i e N within C1 The voltage constraint of (2) can be expressed as
For C 1 Any line l in j ∈B C1 The current constraint of (2) can be expressed as
From equations (8) and (9), the equivalent load is knownCan reflect the region C 1 Impact on downstream zone operating constraints.
Node power constraints may be expressed as
Furthermore, there are the following power balance constraints when ignoring the loss:
to this end, the constituent region C1 of formulas (7) - (13) is based on an optimization model of SSSR. Considering that its objective function isIs a quadratic expression of +.>The model can be solved quickly by quadratic programming.
4. A downstream region optimization model is determined.
For the downstream region, denoted C 2 The variables areAnd->The whole optimization model is as follows:
in the formula (14), lambda P,C1 And lambda (lambda) Q,C1 Respectively isAnd->The corresponding lagrangian multiplier has the following expression:
in the formulas (15) and (16),and->Respectively, node i epsilon N C1 The upper and lower limit constraints of the voltage (i.e., formula (8)) correspond to multipliers; />Is line l j ∈B C1 Current constraint (i.e., formula (9)) corresponds to the multiplier; mu (mu) P,C1 Sum mu Q,C1 Is the power balance of the region C1 (namely #)12 And (13)). They can all be quickly found in the optimization process according to the quadratic programming method.
To enable the safety domain theory to be applied to the decoupled network, the node refers to the boundary node to add the voltage variable V C1 The effect of all power injections of the entire power distribution system on the boundary node k voltage is expressed by the following:
the node voltage and line current constraints in region C2 can then be expressed as:
in the formula (17) and the formula (18)And->The node k is the first node, and is the equivalent resistance and equivalent reactance formed according to equation (2). />And->The equivalent resistance and reactance formed according to equation (3) are the nodes including node k. V (V) C1 The value of (2) reflects the upstream region C 1 Flow of water in the pair of downstream areas C 2 The influence of the operational constraints.
Node power constraints may be expressed as
Taking into account the optimization processAnd->Is a variable and therefore accounts for the active and reactive power constraints on the tie-line:
in addition, the power balance constraint of region C2 is also considered:
to this end, equations (14), (17) - (25) constitute the optimization model of C2 based on SSSR. Taking into account its objective function F 2 Is thatAnd->Is a quadratic expression of +.>And->The model can be solved quickly by quadratic programming.
5. Initializing the upstream and downstream region data.
Let iteration number s=0, initialize boundary node additional voltage, power on tie line and its corresponding multiplier, respectivelyAnd->And->
6. The upstream and downstream areas communicate with each other:
region C1 willAnd->Passing to the area C2; region C2 will->To region C1.
7. Coordinated optimization of upstream and downstream regions based on static security domains:
the upstream region generates a static security domain boundary hyperplane expression according to the topological structure and the root node of the region, establishes an optimization model and solves to obtainAnd->Downstream region according to topology and +.>A static security domain boundary hyperplane expression is generated. Simultaneously establishing an optimization model and solving to obtainThereafter, an iterative residual is calculated
If it is
ε s+1 <ε (29)
The algorithm converges and the optimization process ends. Wherein epsilon is a convergence threshold; otherwise, s=s+1, and go to step 6. The flow chart of the invention in the implementation is shown in figure 2.
To verify the method, a test was performed on a PG & E69 node distribution network as shown in fig. 3. The whole distribution network is divided into 3 subareas (areas C1, C2 and C3, boundary connecting lines are l10 and l 42), nodes 11,21,27,39,50 and 53 are provided with DER 1-DER 6 based on photovoltaic power generation, the capacity is 650kW, all DERs can adjust reactive power, and the upper limit of the controllable reactive power is 150kVar; nodes 19,38 and 47 are provided with static var compensators SVC 1-SVC 3, and the upper limit of the compensation quantity is 200kVar; nodes 12,48 and 63 are equipped with controllable loads FL 1-FL 3, with an upper controllable active power limit of 100kW. The rated voltage and V0 are both 12.66kV, the node voltage constraint range is [0.90,1.05], and the maximum line current is 400A. The total daily load curve and the total DER active power curve of the whole power grid are shown in figure 4, and the DER permeability of the power grid reaches 102.57%.
In order to illustrate the rapidity of the proposed method, for the same loss reduction model, centralized control [11] and the proposed distributed control are adopted for optimization respectively, wherein the centralized method establishes a nonlinear optimization model based on an alternating current power flow model in the optimization process. The network loss change condition corresponding to the calculation example is shown in fig. 5, and the node voltage amplitude change condition after optimization is shown in fig. 6. The overall cost/calculation time after optimization for all cases is shown in table 1. It can be seen that the optimization results of the method herein can meet the operating constraints and the obtained optimization results are substantially the same as those of the centralized method, but differ by orders of magnitude in computation time, i.e., the proposed method is more capable of meeting the requirements of online applications.
TABLE 1 optimization time
To illustrate the convergence of the algorithms selected herein, the methods herein are compared to the ADMM algorithm. Fig. 7 depicts the residual error over the number of iterations in hour 13. As can be seen from the figure, after 3 iterations, the error can be reduced to below 10-5, i.e. the global optimal solution is obtained, whereas the ADMM requires approximately 35 times. Fig. 8 shows the iterative convergence of the boundary variables between region C1 and region C2, i.e., the power on the link being transferred and its corresponding lagrangian multiplier and boundary node additional voltage magnitudes within 13 hours. While figure 9 shows the boundary variation between region C1 and region C3 within 13 hours. While the optimization time of ADMM was 53.1s (daily optimization average time was 55.8 s). It can be seen that the method presented herein has monotonic convergence properties and can quickly determine the optimal value of the boundary variable. Therefore, the iteration number required by adopting the method is less, the communication number required by each region when the global optimal solution is reached is reduced, the calculation efficiency is high, and the convergence characteristic is good.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. The distributed coordination method for the power distribution network based on the static security domain is characterized by comprising the following steps of:
defining a static voltage safety domain on a power injection space according to distribution network configuration characteristics, wherein the distribution network configuration characteristics comprise a node set and a branch number set, and simultaneously taking the direction of power flowing from a load and distributed power generation to a power grid as the positive direction of power injection;
constructing a centralized optimization model based on a static security domain by taking the minimum overall network loss as a power distribution network optimization target, decoupling the centralized optimization model based on the static security domain to obtain an upstream region and a downstream region of a system, and deconstructing the centralized optimization model based on the static security domain based on a tie line equivalence principle of the upstream region and the downstream region, wherein the centralized optimization model based on the static security domain is as follows:
the deconstructed objective function is:
determining an upstream region optimization model and a downstream region optimization model based on the model deconstructing result;
initializing operation parameters of the upstream region optimization model and the downstream region optimization model respectively, and starting the mutual communication of the upstream region and the downstream region;
generating a static security domain boundary hyperplane expression by the upstream region according to the topological structure and the root node of the region, establishing an optimization model and solving to obtain operation parameters; generating a static security domain boundary hyperplane expression by the downstream region according to the topological structure and the upstream region iterative voltage, and simultaneously establishing an optimization model and solving to obtain operation parameters; and when the system residual error converges, ending the optimization process, otherwise, updating the operation parameters of the upstream region optimization model and the downstream region optimization model, and continuing iteration.
2. The distributed coordination method of a power distribution network based on a static security domain according to claim 1, wherein the static voltage security domain in the power injection space is:
the node number of the power distribution network is n+1, the branch number is nb, the node 0 is a loose node, N= {1,2,.. 1 ,l 2 ,...,l nb The symbol "represents a set of all lines, V i m Calculating a node voltage lower limit; v (V) i M To calculate the upper limit of the node voltage, V 0 To relax the node voltage, P j For node j active power, Q j Reactive power for node j; i i M For the upper limit of line current, P i m For node i active power lower limit, P i M Is the upper limit of active power; q (Q) i m For the lower reactive power limit of node i,for node i reactive power upper limit R l,i +jX l,i For line l i Impedance of-> For the equivalent resistance between nodes i and j, which is related to the node voltage amplitude constraint, +.>For the equivalent reactance between nodes i and j that is related to the node voltage magnitude constraint,for the equivalent resistance between nodes i and j, which is related to node current magnitude constraint, +.>For the equivalent reactance between nodes i and j related to node current magnitude constraint, it can be generated according to network topology and line impedance:
wherein D is i For node i and its downstream group of nodesThe resulting collection of the components,route line set B for node i to node 0 i Total impedance of->Route set B for node j to node 0 j Total impedance of->Route set B for node s to node 0 s Node s is the number of the intersection point of node j with the upstream head of node i.
3. The static security domain-based power distribution network distributed coordination method according to claim 1, wherein the upstream region optimization model is:
reference variableThen, for any node i ε N in the upstream region C1 The voltage constraint of (2) can be expressed as
For any line l in the upstream zone j ∈B C1 The current constraint of (2) can be expressed as
Node power constraints may be expressed as
The power balance constraint is:
4. the static security domain-based power distribution network distributed coordination method according to claim 1, wherein the downstream region optimization model is:
wherein lambda is P,C1 And lambda (lambda) Q,C1 Respectively isAnd->The corresponding lagrangian multiplier has the following expression:
wherein,,and->Respectively, node i epsilon N C1 The upper and lower limits of the voltage restrict the corresponding multiplier; mu (mu) P,C1 Sum mu Q,C1 Is the multiplier corresponding to the upstream region power balance,
reference to boundary node additional voltage variable V C1 ：
The node voltage and line current constraints in the downstream region can then be expressed as:
wherein the method comprises the steps ofAn equivalent resistance formed according to (2) for node k as the head node, +.>Equivalent reactance formed according to (2) for node k as the head node, +.>Is the equivalent resistance formed according to (3) with node k as the first node, +.>The equivalent reactance formed according to formula (3) with node k as the first node;
node power constraints may be expressed as
Active and reactive power constraints on the tie line:
power balance constraint for downstream region:
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| CN107294101A (en) * | 2017-07-03 | 2017-10-24 | 武汉大学 | A kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint |
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