CN112491090B - Power electronic transformer port configuration optimization method considering transfer path optimization - Google Patents

Power electronic transformer port configuration optimization method considering transfer path optimization Download PDF

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CN112491090B
CN112491090B CN202011120323.6A CN202011120323A CN112491090B CN 112491090 B CN112491090 B CN 112491090B CN 202011120323 A CN202011120323 A CN 202011120323A CN 112491090 B CN112491090 B CN 112491090B
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port
electronic transformer
power electronic
pet
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CN112491090A (en
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孙哲彬
萨仁高娃
闫志海
梁涛
刘鹏宣
李恒臻
王明捐
刘友波
尹航
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Sichuan University
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
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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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • 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
    • 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
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • 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
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • 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
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a power electronic transformer port configuration optimization method considering transfer path optimization, and belongs to the power system automation field. The method aims at minimizing total cost of all ports of the power electronic transformer, maximizing power generation and absorption of renewable energy sources in a system and optimizing N-1 transfer paths, and takes account of constraint conditions such as power balance constraint, network safety constraint, port power voltage constraint of various source load storage devices, self constraint of the electronic transformer, N-1 transfer path constraint and the like, so that a complete power electronic transformer port configuration optimization model considering transfer path optimization is constructed. The upper optimization calculation layer generates a feasible solution of the model by utilizing an improved multi-objective particle swarm optimization algorithm based on congestion entropy, the lower trend calculation layer performs verification of trend calculation on the current system configuration generated by the optimization algorithm layer by utilizing a PET-containing trend calculation method, and the upper optimization algorithm corrects the feasible solution search direction of the particle swarm according to the trend verification result of the lower layer.

Description

Power electronic transformer port configuration optimization method considering transfer path optimization
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a power electronic transformer port configuration optimization method considering transfer path optimization.
Background
In an AC/DC hybrid DER system, the multi-port converter equipment is applied in a large amount, and different from a double-port common converter, the multi-port converter device not only needs to convert AC/DC electric quantity, but also needs to meet the requirements of multi-terminal interconnection, flexible networking and flexible ring network layered control of an AC/DC micro-network cluster, so that the position and the capacity of the multi-port converter equipment have important significance for flexible and efficient operation of the hybrid distribution network even though the distribution network/micro-network is a local area energy supply system. The addressing of the multi-port converter belongs to discrete addressing, and the solution space is discrete, namely, the station addresses with preset alternatives are provided by an upper layer source-load storage partition and grid structure planning model. The discrete site selection problem is generally obtained by comprehensively calculating, comparing and sorting the evaluation values of the multi-dimensional indexes of the sites to be selected by adopting a comprehensive evaluation method, and then obtaining the scheme with the highest comprehensive evaluation value. The learner gives a site selection evaluation method of the multi-port converter equipment, but the scheme adopts a site selection and volume determination method of a transformer substation, which indicates that the port capacity of the converter equipment is completely determined by the position of the converter equipment, namely the port capacity is determined by the sum of the capacities of feeder lines connected by the ports, and the processing is obviously too coarse and can not support large-scale load transfer among the subnetworks of the AC/DC hybrid system and interconnection and mutual compensation in a larger range.
The technical problems existing at the present stage are:
how to consider the port operation characteristics of the power electronic transformer and solve the problem of optimizing the power supply path of the power electronic transformer system, and the requirements of multi-terminal interconnection, flexible networking and flexible ring network layered control of the AC/DC micro-network cluster are met. Aiming at the proposed multi-target double-layer planning model of the PET-containing alternating-current and direct-current system, the spatial distribution of the model solution is extremely complex and irregular, and how to seek an optimization algorithm with strong global searching capability and high local optimizing efficiency to effectively solve.
Aiming at the problems, a power electronic transformer port configuration optimization method taking transfer path optimization into consideration needs to be designed at the present stage to solve the problems.
Disclosure of Invention
The invention aims to provide a power electronic transformer port configuration optimization method considering transfer path optimization, which is used for solving the technical problems in the prior art, such as: how to consider the port operation characteristics of the power electronic transformer and solve the problem of optimizing the power supply path of the power electronic transformer system, and the requirements of multi-terminal interconnection, flexible networking and flexible ring network layered control of the AC/DC micro-network cluster are met. Aiming at the proposed multi-target double-layer planning model of the PET-containing alternating-current and direct-current system, the spatial distribution of the model solution is extremely complex and irregular, and how to seek an optimization algorithm with strong global searching capability and high local optimizing efficiency to effectively solve.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a power electronic transformer port configuration optimization method considering transfer path optimization comprises the following steps:
s1: according to the economical efficiency of the power electronic transformer, the capacity of absorbing renewable energy sources and the requirements of load transfer, respectively establishing a multi-objective model of the power electronic transformer with minimum total cost of all ports, maximum absorption of renewable energy source power generation in the system and N-1 transfer paths;
s2: taking power balance constraint, network security constraint, port power voltage constraint of various source charge storage devices, electronic and electronic transformer self constraint and N-1 transfer path constraint into consideration, constructing a multi-objective optimization model of complete power and electronic transformer port configuration considering port characteristics and transfer path optimization;
s3: for the established multi-objective optimization model, solving through double-layer iteration, namely: the upper optimization calculation layer generates a feasible solution of the multi-objective optimization model by utilizing an improved multi-objective particle swarm optimization algorithm based on congestion entropy, the lower power flow calculation layer performs power flow calculation verification on the current system configuration generated by the optimization algorithm layer by utilizing a PET-containing power flow calculation method, and the upper optimization algorithm corrects the feasible solution search direction of the particle swarm according to a power flow verification result of the lower layer.
Further, the step S1 specifically includes:
for inclusion of N PET System of power electronic transformers with N PET When the PET port fails, various devices under the corresponding PET failed port are transferred by the output ports of other power electronic transformers, so that the port capacity of the PET also needs to meet the load transfer requirement, and the objective function of the PET port capacity configuration is expressed as follows:
target 1: the total cost of all ports of the power electronic transformer is minimum, and the expression is:
Figure GDA0002899116680000021
wherein ,CH,i and ρH,i The capacity and unit capacity cost of the ith PET high-pressure port are respectively C L,i,j For the capacity of the jth low pressure port, ρ, of the ith PET L,i,j Cost for its corresponding unit capacity;
target 2: the renewable energy power generation in the system is maximally consumed, and the expression is as follows:
Figure GDA0002899116680000031
wherein ,pre0,j (i) Generating output power for renewable energy sources under j ports of the power electronic transformer in a given tracking-out force control mode at the moment i,p re,j (i) Is the actual power under the port constraint of the power electronic transformer;
target 3: the N-1 transfer path is optimal, and the expression is:
Figure GDA0002899116680000032
wherein ,NPET Representing a collection of power electronic transformers, H i Represents the port set of the ith power electronic transformer, T represents the time period of path optimization calculation, ψ represents the transferable power supply path set of the nth port at the time of the ith PET failure,
Figure GDA0002899116680000033
and obtaining the transmission power loss of the kth transferable power supply path in the t period through system power flow calculation.
Further, the step S2 specifically includes:
the constraints of the power electronic transformer and the constraints of the N-1 transfer path are expressed as follows:
1) Self-restraint of power electronic transformer
Firstly, the power electronic transformer itself needs to satisfy the power balance condition, that is, the sum of the output port power and the input port power is the internal comprehensive loss of the power electronic transformer, and the model of the power balance and the internal power balance of the power electronic transformer is simplified into:
Figure GDA0002899116680000034
wherein ,PL,j (i) Output power of the j output port of the i power electronic transformer, P loss,j (i) Representing the port equivalent power loss of the ith power electronic transformer, P H (i) Input port power for the ith power electronic transformer;
the net source-charge-storage power under each port of the PET is within the port capacity range, namely:
-C L,j ≤P ES,j (i)+P load,j (i)-P re,j (i)≤C L,j
wherein ,Ploss,j (i),P re,j (i),P ES,j (i) Respectively representing the load, the power supply and the stored charge power carried by the jth output port of the ith PET, C L,j For the capacity of the j-th output port of the power electronic transformer i, the values should be as follows:
Figure GDA0002899116680000041
i.e. the sum of the capacities of the output ports is not greater than the capacity C of the input port H ；
2) N-1 transfer path constraint
When the N-1 fault occurs, the transferable power supply path constraint is determined by the grid structure of the system and the port capacity of the power electronic transformer, namely:
-P L,j,m ≤P ES,i,n,t +P load,i,n,t -P re,i,n,t +P ES,j,m,t +P load,j,m,t -P re,j,m,t ≤P L,j,m
wherein ,PL,j,m For the capacity of the mth low-voltage power supply port of the jth power electronic transformer, P ES,j,m,t ,P load,j,m,t and Pre,j,m,t Respectively storing energy, load and power of renewable energy sources under the mth low-voltage port of the jth power electronic transformer under the t period, and determining the port transfer power requirement of the period;
the capacity constraints of a conventional transformer are expressed as follows:
Figure GDA0002899116680000042
in the formula ,
Figure GDA0002899116680000043
at the time t, when the power grid is in the f state, apparent power of an outlet of a j-th feeder line connected with the main transformer; omega (i) is a feeder connected with the ith main transformerIs a collection of (3); d (i) is a connection converter station judgment variable, d (i) =1 represents that the i-th main transformer is connected with a converter station, and d (i) =0 represents that the i-th main transformer is not connected with the converter station; />
Figure GDA0002899116680000044
At the time t, when the power grid is in the f state, the apparent power of the j-th VSC connected with the main transformer; Φ (i) is a set of VSCs connected to the ith main transformer; />
Figure GDA0002899116680000045
Capacity for the ith main transformer; n (N) trans Is a collection of traditional transformers; phi (phi) state Is a collection of power grid states, including a normal operation state and various N-1 fault states;
the capacity constraints of the power electronic transformer are expressed as follows:
Figure GDA0002899116680000046
wherein ,
Figure GDA0002899116680000047
at the time t, when the power grid is in the f state, the power of the outlet of the j-th direct current feeder connected with the PET; omega shape dc (i) A set of direct current feeders connected to the ith PET; />
Figure GDA0002899116680000048
At the time t, when the power grid is in the f state, apparent power of an outlet of a j-th alternating current feeder line connected with the PET; omega shape ac (i) A collection of ac feeders connected to the ith PET; />
Figure GDA0002899116680000049
Capacity for the ith PET; n (N) PET Is a collection of PET.
Further, the step S3 specifically includes:
using the optimized variable of PET port configuration as particle swarm individual parameter X i And randomly generating D group composition particlesGroup, constraint processing is carried out on each group of port configuration, and each objective function F is calculated i And violating the value function F viol The calculation formula of the traditional particle swarm population evolution is as follows:
Figure GDA0002899116680000051
wherein ,vi To update the velocity vector, t is the number of iterations, d is the number of particle sets, c 1 and c2 Is an acceleration coefficient; g represents the maximum number of iterations, r 1 and r2 Random numbers that are uniformly distributed subject to (0, 1); w is an inertia weight, the value of the inertia weight determines the global searching capability and the local searching capability of an algorithm, and the value of w is changed dynamically along with the iteration number, the value of w is large to indicate that the global searching capability is strong, and the value of w is small to indicate that the local searching capability is strong, and a specific calculation formula is as follows:
w(t)＝w max -(w max -w min )·t/G
wherein ,wmax and wmin Respectively taking the maximum and minimum inertia weights; aiming at the weight ratio among the 3 proposed objective functions F1, F2 and F3, weighting is given by adopting an ordinal preference method based on information entropy, and the respective weights are determined by judging the difference of each target value in a Pareto solution set, so that the influence of the subjective consciousness of a decision maker on a final decision can be avoided;
the multi-objective optimization model adopts a non-inferior particle set-based method for establishing the evolution direction of an external elite set R-guided group; in the method, the particle evolution direction can be judged by the comparison good-bad relationship of two particles, and the calculation formula is as follows: the method for determining the dominant relationship among particles comprises the following steps:
Figure GDA0002899116680000052
wherein Fviol represents a violation function; fi represents the ith objective function; nobj is the number of objective functions; the above indicates that when particle X 1 and X2 When case1 or case2 is satisfied, then particle X 1 Predominance of particle swarmTo X direction 1 Evolution in the direction;
and proposes that an elite retention mechanism based on crowding entropy measurement deletes redundant non-inferior solution sets in R, thereby ensuring uniform distribution of Pareto optimal solutions in a target space, specifically as follows,
the crowded entropy of elite individual l is defined as:
Figure GDA0002899116680000061
in the formula ：Fi,max and Fi,min Respectively represent the elite individual set in the objective function F i Maximum and minimum values of (a); dl-dl l,i and dul,i Representing elite individual l and adjacent elite individuals l-1 and l+1 in the objective function F i Difference in (c); taking the elite individual with the most extreme value for a certain objective function, wherein the crowded entropy value is infinity; when the number of individuals in R is greater than a predetermined value N l And deleting individuals with smaller crowded entropy values.
Compared with the prior art, the invention has the following beneficial effects:
in the ac/dc hybrid network, the power electronic converter has the function of actively regulating network power, so that the operation object of reconstruction and load transfer is not a simple sectionalizer and a tie switch any more, and the running state of the port of the converter should be considered. The invention provides a power electronic transformer port capacity configuration method considering transfer paths and PET port characteristics based on a partition layered architecture of an alternating current-direct current hybrid system, and meets the requirements of mutual aid, flexible networking and flexible ring network layered control of an alternating current-direct current micro-grid cluster multi-terminal interconnection.
The scheme has the innovation points that the multi-objective function with the minimum total cost of all ports of the power electronic transformer, the maximum power generation and the optimal N-1 transfer path of renewable energy sources in the system is established in detail and comprehensively, the economical efficiency of the power electronic transformer is ensured, the capacity of the renewable energy sources is improved, and the requirements of load transfer are met.
Compared with an alternating current system planning model, the dimension of the alternating current-direct current hybrid system planning model is greatly increased by introducing the direct current variable, and the decision variable has continuous variable and discrete variable; in addition, new operation constraints such as port control parameters of the converter equipment and new constraint conditions such as reliability constraints and environment compatibility constraints of the direct current system are added into constraint conditions, and the model solution space distribution is extremely complex and irregular. Based on the method, the invention provides an iterative solution method based on running power flow simulation, an upper optimization calculation layer generates a feasible solution of the model by utilizing an improved multi-objective particle swarm optimization algorithm based on congestion entropy, a lower power flow calculation layer checks the current system configuration generated by the optimization algorithm layer by utilizing a PET-containing power flow calculation method, and the upper optimization algorithm corrects the feasible solution search direction of the particle swarm according to the lower power flow check result.
Drawings
FIG. 1 is a flow chart of a PET port capacity optimization algorithm in accordance with an embodiment of the present invention.
FIG. 2 is a schematic diagram of the crowd entropy calculation of elite individuals according to an embodiment of the present invention.
FIG. 3 is a schematic flow chart of the steps of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more fully with reference to the accompanying drawings 1-3, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
in the prior art, how to consider the port operation characteristics of the power electronic transformer and solve the problem of optimizing the power supply path of the power electronic transformer system, and the requirements of multi-terminal interconnection, flexible networking and flexible ring network layered control of the AC/DC micro-grid cluster are met. Aiming at the proposed multi-target double-layer planning model of the PET-containing alternating-current and direct-current system, the spatial distribution of the model solution is extremely complex and irregular, and how to seek an optimization algorithm with strong global searching capability and high local optimizing efficiency to effectively solve.
Thus, as shown in fig. 3, a power electronic transformer port configuration optimization method considering transfer path optimization is proposed, which includes the following steps:
s1: from the viewpoints of economy of the power electronic transformer, the capacity of absorbing renewable energy sources and the requirement of load transfer, the multi-objective function with the minimum total cost of all ports of the power electronic transformer, maximum absorption of renewable energy source power generation in a system and optimal N-1 transfer path is respectively established.
S2: besides the power balance constraint, the network safety constraint and the port power voltage constraint of various source load storage devices, the self constraint and the N-1 transfer path constraint of the electronic transformer are mainly considered, and a complete power electronic transformer port configuration optimization model considering port characteristics and transfer path optimization is constructed.
S3: for the established multi-objective optimization model, a double-layer iterative solving method is provided, an upper-layer optimization calculation layer generates a feasible solution of the model by utilizing an improved multi-objective particle swarm optimization algorithm based on congestion entropy, a lower-layer power flow calculation layer checks the current system configuration generated by the optimization algorithm layer by utilizing a PET-containing power flow calculation method, and the upper-layer optimization algorithm corrects the feasible solution searching direction of the particle swarm according to the lower-layer power flow check result.
The specific process of the S1 step is as follows:
the port configuration problem of the power electronic transformer should consider a plurality of targets, firstly, the power electronic transformer has high price, and the larger the port capacity is, the higher the use cost is, so the capacity of the power electronic transformer should be reduced as much as possible; secondly, by utilizing the power routing and coordination control of the multi-port PET, the wind and light discarding is avoided as much as possible, the renewable energy sources in the system are consumed, and meanwhile, the system comprises N PET System of power electronic transformers with N PET When the PET ports are failed due to N-1 faults, various devices under the corresponding PET failed ports are transferred by the output ports of other power electronic transformers,therefore, the port capacity of PET also needs to meet the demand for load transfer, and thus, the objective function of PET port capacity configuration is expressed as follows:
target 1: the total cost of all ports of the power electronic transformer is minimum, and the expression is:
Figure GDA0002899116680000081
wherein ,CH,i and ρH,i The capacity and unit capacity cost of the ith PET high-pressure port are respectively C L,i,j For the capacity of the jth low pressure port, ρ, of the ith PET L,i,j For its corresponding cost per unit capacity.
Target 2: the renewable energy power generation in the system is maximally consumed, and the expression is as follows:
Figure GDA0002899116680000082
wherein ,pre0,j (i) Generating output power, p, of renewable energy sources under j ports of power electronic transformer in given tracking output control mode at moment i re,j (i) Is the actual power under the port constraints of the power electronic transformer.
Target 3: the N-1 transfer path is optimal, and the expression is:
Figure GDA0002899116680000083
wherein ,NPET Representing a collection of power electronic transformers, H i Represents the port set of the ith power electronic transformer, T represents the time period of path optimization calculation, ψ represents the transferable power supply path set of the nth port at the time of the ith PET failure,
Figure GDA0002899116680000084
and obtaining the transmission power loss of the kth transferable power supply path in the t period through system power flow calculation.
The specific process of the step S2 is as follows:
on the basis of the operation constraint condition of the PET-containing AC/DC power distribution network, the self constraint and the transfer path constraint of the power electronic transformer are considered, and a more accurate power electronic transformer port configuration optimization model is constructed. The constraint of the electronic transformer and the constraint of the N-1 transfer path are expressed as follows:
1) Self-restraint of power electronic transformer
Firstly, the power electronic transformer itself needs to satisfy the power balance condition, that is, the sum of the output port power and the input port power is the internal comprehensive loss of the power electronic transformer, and the detailed model of the power balance of the power electronic transformer port and the internal power balance is simplified into:
Figure GDA0002899116680000091
wherein ,PL,j (i) Output power of the j output port of the i power electronic transformer, P loss,j (i) Representing the port equivalent power loss of the ith power electronic transformer, P H (i) And inputting port power for the ith power electronic transformer.
The net source-charge-storage power under each port of the PET is within the port capacity range, namely:
-C L,j ≤P ES,j (i)+P load,j (i)-P re,j (i)≤C L,j
wherein ,Ploss,j (i),P re,j (i),P ES,j (i) Respectively representing the load, the power supply and the stored charge power carried by the jth output port of the ith PET, C L,j For the capacity of the j-th output port of the power electronic transformer i, the values should be as follows:
Figure GDA0002899116680000092
i.e. the sum of the capacities of the output ports is not greater than the capacity C of the input port H 。
2) N-1 transfer path constraint
When the N-1 fault occurs, the transferable power supply path constraint is determined by the grid structure of the system and the port capacity of the power electronic transformer, namely:
-P L,j,m ≤P ES,i,n,t +P load,i,n,t -P re,i,n,t +P ES,j,m,t +P load,j,m,t -P re,j,m,t ≤P L,j,m
wherein ,PL,j,m For the capacity of the mth low-voltage power supply port of the jth power electronic transformer, P ES,j,m,t ,P load,j,m,t and Pre,j,m,t The power of energy storage, load and renewable energy source under t period of the mth low-voltage port of the jth power electronic transformer respectively determines the port transfer power requirement of the period.
The capacity constraints of a conventional transformer are expressed as follows:
Figure GDA0002899116680000093
in the formula ,
Figure GDA0002899116680000094
at the time t, when the power grid is in the f state, apparent power of an outlet of a j-th feeder line connected with the main transformer; omega (i) is the set of feeders connected to the ith main transformer; d (i) is a connection converter station judgment variable, d (i) =1 represents that the i-th main transformer is connected with a converter station, and d (i) =0 represents that the i-th main transformer is not connected with the converter station; />
Figure GDA0002899116680000095
At the time t, when the power grid is in the f state, the apparent power of the j-th VSC connected with the main transformer; Φ (i) is a set of VSCs connected to the ith main transformer; />
Figure GDA0002899116680000101
Capacity for the ith main transformer; n (N) trans Is a collection of traditional transformers; phi (phi) state Is a collection of power grid statesAnd includes normal operation state and various N-1 fault states.
The capacity constraints of the power electronic transformer are expressed as follows:
Figure GDA0002899116680000102
wherein ,
Figure GDA0002899116680000103
at the time t, when the power grid is in the f state, the power of the outlet of the j-th direct current feeder connected with the PET; omega shape dc (i) A set of direct current feeders connected to the ith PET; />
Figure GDA0002899116680000104
At the time t, when the power grid is in the f state, apparent power of an outlet of a j-th alternating current feeder line connected with the PET; omega shape ac (i) A collection of ac feeders connected to the ith PET;
Figure GDA0002899116680000105
capacity for the ith PET; n (N) PET Is a collection of PET.
Other constraints of the model also include power balance constraints, network security constraints, and port power voltage constraints of various source load storage devices, which are not described in detail.
The specific process of the step S3 is as follows:
for the multi-objective optimization problem, a double-layer iterative solving method is provided, an upper-layer optimization calculation layer generates a feasible solution of the model by using an improved multi-objective particle swarm optimization (IMOPSO-CE) algorithm based on crowded entropy, a lower-layer tide calculation layer checks the current system configuration generated by an optimization algorithm layer by using a PET-containing tide calculation method, and the upper-layer optimization algorithm corrects the feasible solution searching direction of the particle swarm according to the tide check result of the lower-layer tide.
The specific algorithm flow chart is shown in fig. 1, and the following description is needed:
the algorithm takes PET port configuration optimization variables as particle swarm individual parameters X i Generating group D into subgroups randomly, performing constraint processing on each group of port configuration, and calculating objective functions F i And violating the value function F viol The calculation formula of the traditional particle swarm population evolution is as follows:
Figure GDA0002899116680000106
wherein ,vi To update the velocity vector, t is the number of iterations (particle swarm algebra), d is the number of particle sets, c 1 and c2 Is an acceleration coefficient; g represents the maximum number of iterations, r 1 and r2 To obey a uniformly distributed random number of (0, 1). The value of w is inertia weight, which determines the global and local searching capability of the algorithm, and dynamically changes along with the iteration times, the larger value of w indicates strong global searching capability, the smaller value of w indicates strong local searching capability, and the specific calculation formula is as follows:
w(t)＝w max -(w max -w min )·t/G
wherein ,wmax and wmin The maximum and minimum inertia weights are respectively taken as values. Aiming at the problem of the weight ratio among the 3 objective functions F1, F2 and F3, an ordinal preference method based on information entropy is adopted to give weight, and the method determines the respective weight by judging the difference of each target value in the Pareto solution set, so that the influence of the subjective consciousness of a decision maker on the final decision can be avoided.
The multi-objective optimization model of the invention adopts the method of establishing the evolution direction of the external elite set R-guided group based on the non-inferior particle set. In the method, the particle evolution direction can be judged by the comparison good-bad relationship of two particles, and the calculation formula is as follows: the method for determining the dominant relationship among particles comprises the following steps:
Figure GDA0002899116680000111
wherein Fviol represents a violation function; fi represents the ith objective function; nobj is the number of objective functions. The above indicates that when particle X 1 and X2 When case1 or case2 is satisfied, then particle X 1 Dominating the particle swarm to X 1 Evolution in the direction of the position.
The algorithm has the defect that when the iteration times are increased, a large number of non-inferior solutions can be entered into an external elite set R, and in order to overcome the problem, the invention provides an elite retaining mechanism based on crowding entropy measurement, which deletes redundant non-inferior solution sets in the R, so that the uniform distribution of Pareto optimal solutions in a target space is ensured.
As shown in fig. 2, the crowded entropy of elite individual l is defined as:
Figure GDA0002899116680000112
in the formula ：Fi,max and Fi,min Respectively represent the elite individual set in the objective function F i Maximum and minimum values of (a); dl-dl l,i and dul,i Representing elite individual l and adjacent elite individuals l-1 and l+1 in the objective function F i Difference in (c) is provided. The crowd entropy value of an elite individual taking the most extreme value for a certain objective function is infinity. When the number of individuals in R is greater than a predetermined value N l And deleting individuals with smaller crowded entropy values.
The above is a preferred embodiment of the present invention, and all changes made according to the technical solution of the present invention belong to the protection scope of the present invention when the generated functional effects do not exceed the scope of the technical solution of the present invention.

Claims (4)

1. The power electronic transformer port configuration optimization method considering transfer path optimization is characterized by comprising the following steps of:
s1: according to the economical efficiency of the power electronic transformer, the capacity of absorbing renewable energy sources and the requirements of load transfer, respectively establishing a multi-objective model of the power electronic transformer with minimum total cost of all ports, maximum absorption of renewable energy source power generation in the system and N-1 transfer paths;
s2: taking power balance constraint, network security constraint, port power voltage constraint of various source charge storage devices, power electronic transformer self constraint and N-1 transfer path constraint into consideration, and constructing a multi-objective optimization model of complete power electronic transformer port configuration considering port characteristics and transfer path optimization;
s3: for the established multi-objective optimization model, solving through double-layer iteration, namely: the upper optimization calculation layer generates a feasible solution of the multi-objective optimization model by utilizing an improved multi-objective particle swarm optimization algorithm based on congestion entropy, the lower power flow calculation layer performs power flow calculation verification on the current system configuration generated by the optimization algorithm layer by utilizing a PET-containing power flow calculation method, and the upper optimization algorithm corrects the feasible solution search direction of the particle swarm according to a power flow verification result of the lower layer.
2. The power electronic transformer port configuration optimization method considering transfer path optimization as claimed in claim 1, wherein step S1 is specifically as follows:
for inclusion of N PET System of power electronic transformers with N PET When the PET port fails, various devices under the corresponding PET failed port are transferred by the output ports of other power electronic transformers, so that the port capacity of the PET also needs to meet the load transfer requirement, and the objective function of the PET port capacity configuration is expressed as follows:
target 1: the total cost of all ports of the power electronic transformer is minimum, and the expression is:
Figure QLYQS_1
wherein ,CH,i and ρH,i The capacity and unit capacity cost of the ith PET high-pressure port are respectively C L,i,j For the capacity of the jth low pressure port, ρ, of the ith PET L,i,j Cost for its corresponding unit capacity;
target 2: the renewable energy power generation in the system is maximally consumed, and the expression is as follows:
Figure QLYQS_2
wherein ,pre0,j (i) Generating output power, p, of renewable energy sources under j ports of power electronic transformer in given tracking output control mode at moment i re,j (i) Is the actual power under the port constraint of the power electronic transformer;
target 3: the N-1 transfer path is optimal, and the expression is:
Figure QLYQS_3
wherein ,NPET Representing a collection of power electronic transformers, H i Represents the port set of the ith power electronic transformer, T represents the time period of path optimization calculation, ψ represents the transferable power supply path set of the nth port at the time of the ith PET failure,
Figure QLYQS_4
and obtaining the transmission power loss of the kth transferable power supply path in the t period through system power flow calculation.
3. The power electronic transformer port configuration optimization method considering transfer path optimization as claimed in claim 2, wherein step S2 is specifically as follows:
the constraints of the power electronic transformer and the constraints of the N-1 transfer path are expressed as follows:
1) Self-restraint of power electronic transformer
Firstly, the power electronic transformer itself needs to satisfy the power balance condition, that is, the sum of the output port power and the input port power is the internal comprehensive loss of the power electronic transformer, and the model of the power balance and the internal power balance of the power electronic transformer is simplified into:
Figure QLYQS_5
wherein ,PL,j (i) Output power of the j output port of the i power electronic transformer, P loss,j (i) Representing the port equivalent power loss of the ith power electronic transformer, P H (i) Input port power for the ith power electronic transformer;
the net source-charge-storage power under each port of the PET is within the port capacity range, namely:
-C L,j ≤P ES,j (i)+P load,j (i)-P re,j (i)≤C L,j
wherein ,Pload,j (i),P re,j (i),P ES,j (i) Respectively representing the load, the power supply and the stored charge power carried by the jth output port of the ith PET, C L,j For the capacity of the j-th output port of the power electronic transformer i, the values should be as follows:
Figure QLYQS_6
i.e. the sum of the capacities of the output ports is not greater than the capacity C of the input port H ；
2) N-1 transfer path constraint
When the N-1 fault occurs, the transferable power supply path constraint is determined by the grid structure of the system and the port capacity of the power electronic transformer, namely:
-P L,j,m ≤P ES,i,n,t +P load,i,n,t -P re,i,n,t +P ES,j,m,t +P load,j,m,t -P re,j,m,t ≤P L,j,m
wherein ,PL,j,m For the capacity of the mth low-voltage power supply port of the jth power electronic transformer, P ES,j,m,t ,P load,j,m,t and Pre,j,m,t Respectively storing energy, load and power of renewable energy sources under the mth low-voltage port of the jth power electronic transformer under the t period, and determining the port transfer power requirement of the period;
the capacity constraints of a conventional transformer are expressed as follows:
Figure QLYQS_7
in the formula ,
Figure QLYQS_8
at the time t, when the power grid is in the f state, apparent power of an outlet of a j-th feeder line connected with the main transformer; omega (i) is the set of feeders connected to the ith main transformer; d (i) is a connection converter station judgment variable, d (i) =1 represents that the i-th main transformer is connected with a converter station, and d (i) =0 represents that the i-th main transformer is not connected with the converter station; />
Figure QLYQS_9
At the time t, when the power grid is in the f state, the apparent power of the j-th VSC connected with the main transformer; Φ (i) is a set of VSCs connected to the ith main transformer;
Figure QLYQS_10
capacity for the ith main transformer; n (N) trans Is a collection of traditional transformers; phi (phi) state Is a collection of power grid states, including a normal operation state and various N-1 fault states;
the capacity constraints of the power electronic transformer are expressed as follows:
Figure QLYQS_11
wherein ,
Figure QLYQS_12
at the time t, when the power grid is in the f state, the power of the outlet of the j-th direct current feeder connected with the PET; omega shape dc (i) A set of direct current feeders connected to the ith PET; />
Figure QLYQS_13
At time t, when the power grid is in the f state, the apparent appearance of the outlet of the j-th alternating current feeder line connected with the PETA power; omega shape ac (i) A collection of ac feeders connected to the ith PET;
Figure QLYQS_14
capacity for the ith PET; n (N) PET Is a collection of PET.
4. The power electronic transformer port configuration optimization method considering transfer path optimization as claimed in claim 3, wherein step S3 is specifically as follows:
using the optimized variable of PET port configuration as particle swarm individual parameter X i Randomly generating a group D of particle swarms, carrying out constraint processing on each group of port configuration, and calculating each objective function F i And violating the value function F viol The calculation formula of the traditional particle swarm population evolution is as follows:
Figure QLYQS_15
wherein ,vi To update the velocity vector, t is the number of iterations, d is the number of particle sets, c 1 and c2 Is an acceleration coefficient; g represents the maximum number of iterations, r 1 and r2 Random numbers that are uniformly distributed subject to (0, 1); w is an inertia weight, the value of the inertia weight determines the global searching capability and the local searching capability of an algorithm, and the value of w is changed dynamically along with the iteration number, the value of w is large to indicate that the global searching capability is strong, and the value of w is small to indicate that the local searching capability is strong, and a specific calculation formula is as follows:
w(t)＝w max -(w max -w min )·t/G
wherein ,wmax and wmin Respectively taking the maximum and minimum inertia weights; aiming at the weight ratio among the 3 proposed objective functions F1, F2 and F3, weighting is given by adopting an ordinal preference method based on information entropy, and the respective weights are determined by judging the difference of each target value in a Pareto solution set, so that the influence of the subjective consciousness of a decision maker on a final decision can be avoided;
the multi-objective optimization model adopts a non-inferior particle set-based method for establishing the evolution direction of an external elite set R-guided group; in the method, the particle evolution direction can be judged by the comparison good-bad relationship of two particles, and the calculation formula is as follows: the method for determining the dominant relationship among particles comprises the following steps:
Figure QLYQS_16
wherein Fviol represents a violation function; fi represents the ith objective function; nobj is the number of objective functions; the above indicates that when particle X 1 and X2 When case1 or case2 is satisfied, then particle X 1 Dominating the particle swarm to X 1 Evolution in the direction;
and proposes that an elite retention mechanism based on crowding entropy measurement deletes redundant non-inferior solution sets in R, thereby ensuring uniform distribution of Pareto optimal solutions in a target space, specifically as follows,
the crowded entropy of elite individual l is defined as:
Figure QLYQS_17
in the formula ：Fi,max and Fi,min Respectively represent the elite individual set in the objective function F i Maximum and minimum values of (a);
dl l,i and dul,i Representing elite individual l and adjacent elite individuals l-1 and l+1 in the objective function F i Difference in (c); taking the elite individual with the most extreme value for a certain objective function, wherein the crowded entropy value is infinity; when the number of individuals in R is greater than a predetermined value N l And deleting individuals with smaller crowded entropy values.
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