CN117674276A - New energy power distribution network collaborative optimization method and system based on distributed regulation and control architecture - Google Patents
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Abstract
The invention relates to the field of optimal power flow of power distribution networks, and provides a new energy power distribution network collaborative optimization method and system based on a distributed regulation and control architecture. The method comprises the steps of respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, and constructing a medium-low voltage interconnection new energy power distribution network coordination scheduling model according to the medium-low voltage interconnection new energy power distribution network power flow model; based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors; constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
Description
Technical Field
The invention relates to the field of optimal power flow of power distribution networks, in particular to a new energy power distribution network collaborative optimization method and system based on a distributed regulation and control architecture.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the continuous expansion of the power grid scale and the gradual complexity of the operation mode, the traditional mode of carrying out management scheduling and modeling analysis by power distribution network partitioning independently can not be gradually adapted to the requirements of the development of power grid scheduling operation business in the future, and a multi-voltage-class interconnection system such as a large-scale urban power grid is widely developed, and a medium-low voltage interconnection new energy power distribution system is also continuously increased. In order to ensure safe and economical operation of a novel power system containing high-proportion renewable energy sources, a collaborative optimization method of a medium-low voltage interconnection new energy power distribution system is necessary to be studied.
In addition, with the gradual exhaustion of fossil energy and the improvement of environmental protection consciousness of people, the permeability of new energy power generation such as wind power, photoelectricity and the like in a power system is continuously improved, the proportion of distributed power generation in a power distribution network is also continuously increased, and the randomness and the fluctuation of the distributed power generation provide great challenges for the safe, stable and economic operation of the power system. On the other hand, as the distributed energy sources, the cold heat pump and other flexible loads in the power distribution network have good controllable capability, the occurrence and the proliferation of the loads provide a new means for large-scale and clustered new energy grid-connected capacity adjustment with flexible controllability.
The middle-low voltage interconnection new energy distribution network has the characteristics of dispersibility and uncertainty, and the active output of the new energy distribution network has certain uncertainty, such as that photovoltaic power generation is influenced by weather; the flexible load such as the cold heat pump and the like also has certain uncertainty, for example, the spare capacity provided by an air conditioner is a function of temperature and a user operation mode, so that the spare capacity of various resources in the medium-low voltage interconnection new energy power distribution network is uncertain.
Moreover, the traditional power distribution network mostly adopts a block independent management scheduling method, and most of the medium-low voltage distribution networks lack a cooperative mechanism during scheduling, which can lead to insufficient and unreasonable utilization of resources.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a new energy power distribution network collaborative optimization method and a system based on a distributed regulation and control architecture, which realize optimal control of various resources by optimizing and scheduling on the whole power distribution network level, fully mine the potential of the schedulable resources, ensure the economic, safe and stable operation of the power distribution network, and further realize optimal power flow calculation and controllable resource collaborative optimization of a medium-low voltage interconnected new energy power distribution network.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a new energy power distribution network collaborative optimization method based on a distributed regulation and control architecture.
A new energy power distribution network collaborative optimization method based on a distributed regulation and control architecture comprises the following steps:
respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, so as to construct a medium-low voltage interconnection new energy power distribution network coordination scheduling model;
based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors;
constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
Further, the new energy power distribution network collaborative optimization method further comprises the following steps: when unbalanced power occurs, based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, the minimum cost of standby calling and energy production in the medium-low voltage interconnection new energy power distribution network is taken as a target, and the unbalanced power is compensated by adjusting the output and load consumption of a generator in the power distribution network.
Further, the process of constructing the fuzzy set containing the second moment information based on the historical data and introducing the opportunity constraint further comprises the following steps: introducing opportunity constraint based on a fuzzy set established by the mean value and covariance of historical data, and optimizing the opportunity constraint by adjusting the probability of the violation of the opportunity constraint; and constructing an optimization model based on the optimized opportunity constraint and the fuzzy set.
Further, the construction process of the linearization direct current load flow calculation model of the medium-voltage distribution network comprises the following steps: constructing a medium-voltage distribution network tide model according to predicted power balance constraint, system real-time power unbalance constraint, conventional generator output range constraint, conventional generator active standby range constraint, load consumption maximum value and minimum value constraint, load active standby range constraint and line power flow constraint;
further, the predicted power balance constraint is:
wherein P is G,i Represents the output force of a conventional generator of the medium-voltage distribution network,representing predicted photovoltaic power generation output, P D，h Representing the active power of the low-voltage distribution network at the PCC node,/->Representing predicted ac/dc load consumption;
further, the system real-time power imbalance constraint is:
wherein P is mis Representing the amount of real-time power imbalance in a medium voltage distribution network,representing predicted ac/dc load consumption,/->Representing the actual load consumption in the transport network, +.>Representing predicted photovoltaic power generation output, +.>Representing the output of photovoltaic power generation;
further, the conventional generator output range constraint is:
wherein P is G Represents the output force R of a conventional generator of a medium-voltage distribution network G Indicating that the generator is actually ready for use, G P、respectively representing the minimum and maximum output values of a conventional generator of the medium-voltage distribution network;
further, the active standby range constraint of the conventional generator is as follows:
wherein R is G Indicating that the generator is actually ready for use, R G respectively representing the maximum value and the minimum value of the standby capacity of the generator;
further, the load consumption maximum and minimum constraints are:
wherein R is L Indicating that the load is actually ready for use, L P、representing the minimum and maximum value of load consumption, respectively, < >>Representing the actual load consumption in the transport network;
further, the load active standby range constraint is:
wherein R is L Indicating that the load is actually ready for use, R L respectively are provided withRepresenting maximum and minimum load reserve capacity values;
further, the line power flow constraint is:
wherein P is line Representing line capacity, B bus ，B flow Respectively representing a node admittance matrix and a branch admittance matrix,representing net power injection at each node, N B For the number of nodes>Is P inj Last N of (2) B -1 few rows.
Further, the construction process of the DistFlow power flow calculation model of the low-voltage power distribution network comprises the following steps: and constructing a DistFlow tide calculation model of the low-voltage power distribution network according to the network power flow constraint, the low-voltage power distribution network power constraint and the low-voltage power distribution network voltage constraint.
Further, the network power flow constraints include: line active power constraints, line reactive power constraints, and node voltage constraints;
further, the line active power constraint is:
wherein p is DG,r Representing active power output by an access generator on a node r, p D.r Representing the active load of r on the node, p rs Representing the active power, p, on the branch (r, s) kr Representing the active power on branch (k, r), r kr Representing the resistance of the branch (k, r), l kr Representing the square of the current on the branch (k, r);
further, the line reactive power constraint is:
wherein q DG,r Representing reactive power output by the generator connected to node r, q D,r Representing the reactive load of r on the node, q kr Representing reactive power, x, on branch (k, r) kr Representing the reactance of the branch (k, r), l kr Representing the square of the current on the branch (k, r);
further, the node voltage constraint is:
v s ＝v r -2(r rs p rs +x rs q rs )+(r rs 2 +x rs 2 )l rs
wherein v is r Representing the square of the voltage at node r, p rs 、q rs 、l rs Representing the square of the active and reactive power, respectively, on a branch (r, s), r rs 、x rs The resistances and reactances of the branches (r, s) are represented, respectively.
Further, the low-voltage distribution network power constraint comprises a line power flow constraint, a reactive power constraint of the small-sized generator, an active power constraint of the small-sized generator and an actual active power constraint at the PCC node;
further, the low voltage distribution network voltage constraints include node voltage magnitude constraints and voltage constraints at the PCC nodes.
The second aspect of the invention provides a new energy power distribution network collaborative optimization system based on a distributed regulation and control architecture.
New energy power distribution network collaborative optimization system based on distributed regulation and control architecture includes:
a model building module configured to: respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, so as to construct a medium-low voltage interconnection new energy power distribution network coordination scheduling model;
a first scheduling module configured to: based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors;
a second scheduling module configured to: constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the new energy distribution network collaborative optimization method based on a distributed regulatory architecture as described in the first aspect above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the distributed regulatory architecture based new energy distribution network collaborative optimization method according to the first aspect described above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
the new energy power distribution network collaborative optimization method and system based on the distributed regulation and control architecture can more fully and reasonably utilize various schedulable resources, improve economy, have good universality for various medium-low voltage interconnection new energy power distribution networks, and enhance the possibility of being applied to practice.
The invention starts from the whole new energy power distribution network level, builds a distributed regulation and control framework, performs unified modeling and collaborative scheduling on the medium-voltage power distribution network and the low-voltage power distribution network, and realizes the economic, safe and stable operation of the whole system by integrating controllable resources when the system has power fluctuation.
The invention is suitable for various medium-low voltage interconnection new energy power distribution network systems, such as large-scale urban power grids, suburban remote power distribution networks and the like, and has good universality. For an emerging AC/DC hybrid power distribution network, collaborative scheduling can be realized through simple model transformation. In general, the method has universality and safety while improving economy, and has higher use value.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a new energy distribution network collaborative optimization method based on a distributed regulation architecture;
FIG. 2 is a block diagram of a coordination scheduling model of a medium-low voltage interconnected new energy power distribution network;
fig. 3 is a branch flow model diagram.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Example 1
The embodiment provides a new energy power distribution network collaborative optimization method based on a distributed regulation and control architecture.
As shown in fig. 1, the embodiment constructs a distributed intelligent regulation and control architecture for a medium-low voltage interconnection new energy power distribution network on the basis of the existing power distribution network optimal scheduling method, and performs coordinated control on the whole new energy power distribution network level. And constructing a linearization direct current load flow calculation model according to the characteristics of the medium-voltage distribution network, and constructing a DistFlow load flow calculation model on the low-voltage distribution network. And sharing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage distribution network and the low-voltage distribution network by using the allocation vector, wherein a specific scheduling model block diagram is shown in fig. 2. The uncertainty such as instability of renewable energy output and fluctuation of user load is considered, distribution robust optimization is introduced, a fuzzy set containing second moment information is constructed based on historical data, comprehensive optimization is carried out on all possible scenes, and high reliability of an optimization result under all uncertain scenes is guaranteed. However, since individual extremely bad scenes occur with very little probability, the performance of the result is severely affected. Therefore, the opportunity constraint is introduced to replace the common constraint, so that each constraint containing random variables is allowed to have certain violation possibility, namely that each constraint is not guaranteed to be satisfied, and the conservation of the result is reduced. A good trade-off between reliability and conservation of the results can be achieved by adjusting the probability of opportunistic constraint violation.
The invention provides a new energy power distribution network collaborative optimization method based on a distributed intelligent regulation and control architecture, which comprises the following steps:
step 1: and establishing a tidal current model of the medium-voltage distribution network. With reference to the existing work on optimal power flow, we use a three-phase balanced direct current power flow approximation, ignoring the impact of reactive power on the medium voltage distribution network. The constraints in the medium voltage distribution network are mainly active power constraints, as follows:
predicting a power balance constraint:
real-time power imbalance of system:
conventional generator output range limits:
active standby range limit for conventional generators:
load consumption maximum and minimum limits:
load active standby range limit:
line power flow limit:
among them, we useTo represent a random variable, P G Representing the output force, P, of a conventional generator of a medium-voltage distribution network mis Representing the real-time power unbalance in a medium voltage distribution network,/->Representing predicted ac/dc load consumption,/->Representing the actual load consumption in the transport network, +.>Representing predicted photovoltaic power generation output, +.>Representing the output of photovoltaic power generation, P D Representing the active power of a low-voltage distribution network at a PCC node, R G 、R L Representing the actual standby and the actual load standby of the generator respectively, < + >> R G Represents maximum and minimum generator standby capacity, respectively, < >> R L Respectively represent the maximum value and the minimum value of the load reserve capacity, P line Representing line capacity, B bus ，B flow Representing node admittance matrix and branch admittance matrix, respectively,/->Representing net power injection at each node, N B Is the number of nodes. />Is P inj Last N of (2) B -1 few rows.
Step 2: and establishing a low-voltage distribution network tide model. Because the low voltage distribution network has a low voltage class, the low voltage distribution network should consider reactive power constraints in addition to active power constraints. We assume that the low voltage distribution network is balanced in three phases, modeled using DistFlow model. Taking a certain low-voltage distribution network h connected with a medium-voltage distribution network as an example, the specific constraints are as follows:
(1) Network power flow constraint equation:
line active power constraint:
line reactive power constraint:
node voltage constraint:
v s ＝v r -2(r rs p rs +x rs q rs )+(r rs 2 +x rs 2 )l rs
power, voltage, current relationship:
wherein M, N represents a collection of branches and nodes, respectively, p DG,r 、q DG,r Respectively represents active power and reactive power which are output by the generator connected to the node r, p D.r 、q D,r Representing the active and reactive loads, p, of r on the node, respectively rs 、q rs 、l rs Representing the square of the active and reactive power, respectively, on a branch (r, s), r rs 、x rs Representing the resistance and reactance of the branches (r, s), v, respectively r Representing the square of the voltage at node r. For convenience, node 1 may be set as a PCC (point of common coupling), with a branch tidal current model as shown in fig. 3.
(2) Constraint of low voltage distribution network power and voltage limitations:
line tide constraint:
it can be approximated as four linear constraints:
reactive power limitation of small generators:
active power limitation of small generators:
node voltage magnitude limitation:
voltage limitation at PCC node:
the actual active power equation at the PCC node:
wherein S is rs,max Representing the line capacity of the branch (r, s), q DG,r represents the maximum/minimum value of the reactive power of the generator r at the bus bar, respectively,/>p DG,r Representing the maximum/minimum value of the active power of the generator at busbar r, respectively +.> s vRepresenting the maximum/minimum value of the square of the voltage at node r, < >>Is the square of the boundary voltage, R D,h And the actual standby of the low-voltage distribution network h for the medium-voltage distribution network scheduling is represented.
Step 3: unbalanced power distribution. In order to ensure that the cost for calling standby and energy production in the medium-low voltage interconnection new energy distribution network is minimum, when unbalanced power occurs to the system, the system compensates by adjusting the output and load consumption of a generator in the distribution network:
distribution ratio equation of real-time power imbalance:
the conventional generator of the medium-voltage distribution network is actually used for standby:
the conventional generator of the low-voltage distribution network is actually used for standby:
actual standby provided by the load:
cost function:
wherein N is G 、N D 、N L Respectively representing the number of conventional generators, the number of low-voltage distribution networks connected to the medium-voltage distribution network and the number of loads, d L up-down distribution vector of load respectively, +.> d G Respectively representing the up-down distribution vector of the generator, and c represents the cost vector. {.
Step 4: the formula transformation is robustly optimized using a distribution of opportunistic constraints. All possible scenarios of uncertain parameters are fully considered to ensure that the requested scheduling instruction is viable in a variety of scenarios. However, some very bad cases with very little probability can seriously affect the performance of the result. In the embodiment, an opportunity constraint method is adopted to replace original uncertainty constraint, and a distributed robust optimization method is adopted to improve the performance of the result, and meanwhile, a certain confidence level is met.
Constraint on uncertainty inequality in this embodiment:
using opportunistic constraints instead of:
where m is the number of opportunity constraints,is an uncertain parameter in the ith opportunity constraint, p is the probability that the opportunity constraint is satisfied, ε i Is the probability of violation of the opportunity constraint i, allowing the constraint to be unsatisfied in extreme cases and to be more relaxed. The use of opportunistic constraints may reduce conservation compared to common constraints.
The x is:
assume that:
wherein, xi i To constrain the vector of all random variables contained in i.
The DR variants of the opportunity constraint are:
d is a fuzzy set established based on the mean value and covariance of the historical data, and probability density functions, mean values and covariance of real distribution of uncertain variables are respectively described in the following specific forms:
where f (ζ) is a probability density function of a random variable, and E [ ] is the mean value in the mean brackets.
For simplicity, the xi is omitted i I of (a) in (b). N sets of data samples for a given ζCalculating an empirical mean vector u 0 Sum covariance matrix Σ 0 ：
Definition of the definitionAnd->Is a random variable xi i Affine function of (c):
wherein A is i0 、b i0 Respectively areAnd->Deterministic portion of (A) ik 、b ik Is affine coefficient, K i Is xi i Is a dimension of (c).
Definition of the definitionThe method comprises the following steps:
thus, we can restate the internal constraints of the opportunity constraint as:
step 5: the SOCP solution in a robust form is distributed. Based on the fuzzy set and the chance constraint conversion in step 4, the distributed robust optimization problem can be restated as SOCP optimization:
consider the mean vector as u and the covariance vector as sigma 2 A variant of the chebyshev inequality of the random variable X:
where δ is a parameter belonging to 0 to 1.
And (3) making:
the SOCP constraint can be deformed using the chebyshev inequality as:
thus, by constructing the fuzzy set D, we can finally obtain an SOCP model, which can be solved using a CVX toolbox in MATLAB with MOSEK as the optimization solver. The decision variables can be used as the basis for issuing instructions by a cooperative dispatching center based on a distributed intelligent regulation and control framework, and the potential of various resources in the medium-low voltage interconnected new energy power distribution network is fully excavated by issuing the optimal instructions, so that the safe, stable and economic operation of the system is ensured.
Example two
The embodiment provides a new energy power distribution network collaborative optimization system based on a distributed regulation and control architecture.
New energy power distribution network collaborative optimization system based on distributed regulation and control architecture includes:
a model building module configured to: respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, so as to construct a medium-low voltage interconnection new energy power distribution network coordination scheduling model;
a first scheduling module configured to: based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors;
a second scheduling module configured to: constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
It should be noted that, the model building module, the first scheduling module, and the second scheduling module are the same as the examples and application scenarios implemented by the steps in the first embodiment, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the new energy power distribution network collaborative optimization method based on the distributed control architecture according to the above embodiment.
Example IV
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the new energy power distribution network collaborative optimization method based on the distributed control architecture according to the embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The new energy power distribution network collaborative optimization method based on the distributed regulation and control architecture is characterized by comprising the following steps of:
respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, so as to construct a medium-low voltage interconnection new energy power distribution network coordination scheduling model;
based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors;
constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
2. The collaborative optimization method for a new energy power distribution network based on a distributed control architecture according to claim 1, wherein the collaborative optimization method for the new energy power distribution network further comprises: when unbalanced power occurs, based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, the minimum cost of standby calling and energy production in the medium-low voltage interconnection new energy power distribution network is taken as a target, and the unbalanced power is compensated by adjusting the output and load consumption of a generator in the power distribution network.
3. The collaborative optimization method for a new energy power distribution network based on a distributed regulation and control architecture according to claim 1, wherein the process of constructing a fuzzy set containing second moment information based on historical data and introducing opportunity constraints further comprises: introducing opportunity constraint based on a fuzzy set established by the mean value and covariance of historical data, and optimizing the opportunity constraint by adjusting the probability of the violation of the opportunity constraint; and constructing an optimization model based on the optimized opportunity constraint and the fuzzy set.
4. The new energy power distribution network collaborative optimization method based on the distributed regulation and control architecture according to claim 1, wherein the construction process of the medium-voltage power distribution network tide model comprises the following steps: constructing a medium-voltage distribution network tide model according to predicted power balance constraint, system real-time power unbalance constraint, conventional generator output range constraint, conventional generator active standby range constraint, load consumption maximum value and minimum value constraint, load active standby range constraint and line power flow constraint;
or, the predicted power balance constraint is:
wherein P is G,i Represents the output force of a conventional generator of the medium-voltage distribution network,representing predicted photovoltaic power generation output, P D,h Representing the active power of the low-voltage distribution network at the PCC node,/->Representing predicted ac/dc load consumption;
or, the real-time power unbalance constraint of the system is as follows:
wherein P is mis Representing the amount of real-time power imbalance in a medium voltage distribution network,represents the predicted ac/dc load consumption amount,representing the actual load consumption in the transport network, +.>Representing predicted photovoltaic power generation output, +.>Representing the output of photovoltaic power generation;
or, the conventional generator output range constraint is:
wherein P is G Represents the output force R of a conventional generator of a medium-voltage distribution network G Indicating that the generator is actually ready for use, G P、respectively representing the minimum and maximum output values of a conventional generator of the medium-voltage distribution network;
or, the active standby range constraint of the conventional generator is as follows:
wherein R is G Indicating that the generator is actually ready for use, R G respectively representing the maximum value and the minimum value of the standby capacity of the generator;
or, the load consumption maximum and minimum constraints are:
wherein R is L Indicating that the load is actually ready for use, L P、representing the minimum and maximum value of load consumption, respectively, < >>Representing the actual load consumption in the transport network;
or, the load active standby range constraint is:
wherein R is L Indicating that the load is actually ready for use, R L separate tableShowing the maximum and minimum load reserve capacity values;
or, the line power flow constraint is:
wherein P is line Representing line capacity, B bus ，B flow Respectively representing a node admittance matrix and a branch admittance matrix,representing net power injection at each node, N B For the number of nodes>Is P inj Last N of (2) B -1 few rows.
5. The new energy power distribution network collaborative optimization method based on the distributed regulation and control architecture according to claim 1, wherein the construction process of the low-voltage power distribution network tide model comprises the following steps: and constructing a low-voltage distribution network tide model according to the network power flow constraint, the low-voltage distribution network power constraint and the low-voltage distribution network voltage constraint.
6. The method for collaborative optimization of a new energy power distribution network based on a distributed regulatory architecture according to claim 5, wherein the network power flow constraints include: line active power constraints, line reactive power constraints, and node voltage constraints;
or, the line active power constraint is:
wherein p is DG,r Representing active power output by an access generator on a node r, p D.r Representing the active load of r on a node，p rs Representing the active power, p, on the branch (r, s) kr Representing the active power on branch (k, r), r kr Representing the resistance of the branch (k, r), l kr Representing the square of the current on the branch (k, r);
or, the line reactive power constraint is:
wherein q DG,r Representing reactive power output by the generator connected to node r, q D,r Representing the reactive load of r on the node, q kr Representing reactive power, x, on branch (k, r) kr Representing the reactance of the branch (k, r), l kr Representing the square of the current on the branch (k, r);
or, the node voltage constraint is:
v s ＝v r -2(r rs p rs +x rs q rs )+(r rs 2 +x rs 2 )l rs
wherein v is r Representing the square of the voltage at node r, p rs 、q rs 、l rs Representing the square of the active and reactive power, respectively, on a branch (r, s), r rs 、x rs The resistances and reactances of the branches (r, s) are represented, respectively.
7. The collaborative optimization method for a new energy power distribution network based on a distributed regulation and control architecture according to claim 5, wherein the low-voltage power distribution network power constraint comprises a line power flow constraint, a reactive power constraint of a small generator, an active power constraint of the small generator and an actual active power constraint at a PCC node;
or, the low-voltage distribution network voltage constraint comprises node voltage amplitude constraint and PCC node voltage constraint.
8. New energy power distribution network collaborative optimization system based on distributed regulation and control framework, which is characterized by comprising:
a model building module configured to: respectively constructing a medium-voltage power distribution network power flow model and a low-voltage power distribution network power flow model, so as to construct a medium-low voltage interconnection new energy power distribution network coordination scheduling model;
a first scheduling module configured to: based on a coordination scheduling model of the medium-low voltage interconnection new energy power distribution network, distributing the real-time power mismatch quantity of the system to various schedulable resources of the medium-voltage power distribution network and various schedulable resources of the low-voltage power distribution network by using distribution vectors;
a second scheduling module configured to: constructing a fuzzy set containing second moment information based on historical data, introducing opportunistic constraint, and constructing an optimization model; and based on the optimization model, obtaining a cooperative scheduling instruction of a coordination scheduling model of the medium-low voltage interconnected new energy power distribution network, so as to perform resource scheduling.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the new energy distribution network collaborative optimization method based on a distributed regulatory architecture according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the new energy distribution network co-optimization method based on a distributed regulatory architecture according to any one of claims 1-7 when the program is executed.
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| CN106549378A (en) * | 2016-12-09 | 2017-03-29 | 国网江苏省电力公司金湖县供电公司 | It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source |
| CN114530892A (en) * | 2022-02-11 | 2022-05-24 | 山东大学 | AGC cooperative control optimization method and system considering power stability of tie line |
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| CN106549378A (en) * | 2016-12-09 | 2017-03-29 | 国网江苏省电力公司金湖县供电公司 | It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source |
| CN114530892A (en) * | 2022-02-11 | 2022-05-24 | 山东大学 | AGC cooperative control optimization method and system considering power stability of tie line |
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