CN115018140A - Active power distribution network light storage multi-target planning method and system - Google Patents
Active power distribution network light storage multi-target planning method and system Download PDFInfo
- Publication number
- CN115018140A CN115018140A CN202210585106.7A CN202210585106A CN115018140A CN 115018140 A CN115018140 A CN 115018140A CN 202210585106 A CN202210585106 A CN 202210585106A CN 115018140 A CN115018140 A CN 115018140A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power distribution
- active power
- storage multi
- switch
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 243
- 238000003860 storage Methods 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 40
- 230000035772 mutation Effects 0.000 claims abstract description 37
- 230000003287 optical effect Effects 0.000 claims abstract description 27
- 230000002068 genetic effect Effects 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 18
- 238000007726 management method Methods 0.000 claims abstract description 15
- 210000000349 chromosome Anatomy 0.000 claims description 61
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 claims description 49
- 101150092616 mth gene Proteins 0.000 claims description 19
- 238000005457 optimization Methods 0.000 claims description 18
- 238000012423 maintenance Methods 0.000 claims description 13
- 230000005611 electricity Effects 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 11
- 230000015654 memory Effects 0.000 claims description 8
- 101150076211 TH gene Proteins 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 101150067055 minC gene Proteins 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 14
- 238000004146 energy storage Methods 0.000 description 14
- 238000004590 computer program Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000005855 radiation Effects 0.000 description 4
- 238000009434 installation Methods 0.000 description 3
- 108090000623 proteins and genes Proteins 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007142 ring opening reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Mathematical Physics (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Optimization (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- Software Systems (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an active power distribution network light storage multi-target planning method and system, wherein an active power distribution network light storage multi-target planning model considering demand side management and net rack reconstruction is constructed, active power distribution network light storage multi-target planning is realized by solving the model, and the model is a multi-target planning model and overcomes the one-sidedness of the traditional single-target planning; the method improves the mutation operator and the redistribution operator in the ring network coding and power distribution network frame reconstruction genetic algorithm, avoids the appearance of the infeasible solutions of 'island' and 'ring network' in the active power distribution network frame reconstruction process, and effectively solves the problem of solving the active power distribution network optical storage multi-target planning model.
Description
Technical Field
The invention relates to a light storage multi-target planning method and system for an active power distribution network, and belongs to the field of intermittent distributed power supply planning.
Background
With the access of a large amount of optical storage to the power Distribution Network, the conventional Distribution Network (TDN) gradually shows the problems of weak grid structure, low automation level, insufficient regulation and control capability and the like, and the high permeability access of the optical storage in the power Distribution Network is severely restricted. In order to optimize the operation mode of the power Distribution Network and improve the intelligent level and the self-regulation capability of the power Distribution Network, Active Distribution Networks (ADNs) are widely concerned. Compared with the TDN, the ADN can fully play the role of all adjustable resources by applying various active management measures, and realize the safe and efficient operation of the power distribution network under different scenes by coordinating different types of light storage in different areas. The occurrence of the ADN brings new challenges to the optimal configuration work of the optical storage in the power distribution network while improving the operation economy and stability of the power distribution network. Different from the traditional optical storage planning process in the power distribution network, the optimization configuration work of the optical storage in the ADN is not a single optical storage planning problem, but a planning-operation integrated problem of the power distribution network operation capacity is involved at the same time, and a corresponding planning method does not exist at present.
Disclosure of Invention
The invention provides a light storage multi-target planning method and a light storage multi-target planning system for an active power distribution network, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an active power distribution network light storage multi-target planning method comprises the following steps:
constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active power network loss and average voltage offset of the active power distribution network;
solving an active power distribution network light storage multi-target planning model by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
The objective function of the active power distribution network light storage multi-objective planning model is as follows:
minC＝C inv +C op +C b -C s +C DSM
wherein C is the annual comprehensive cost of the active power distribution network, C inv Fixed investment costs for light storage, C op Operating maintenance costs for light stores, C b Cost for purchasing electricity to the upper-level grid, C s Subsidizing expenses for governments of photovoltaic power generation, C DSM Managing expenses for the demand side of the active power distribution network;
wherein, P loss For active distribution network annual active network loss, N l For the number of branches of the active distribution network, N s The number of scenes to be considered is the photovoltaic-load combined output scene, T is the time number, G l(i,j) For the conductance value of a branch l in the active power distribution network, i and j represent the node number of the branch l, V i,t,s Is the voltage amplitude, V, of the node i at time t under the scene s j,t,s Is the voltage amplitude, delta, of the node j at the moment t under the scene s ij,t,s Is the voltage phase angle difference of node i and node j at time T under scene s, T s The number of days of the scene s in one year;
wherein, Delta U is the average voltage offset of the active power distribution network, N is the number of nodes in the active power distribution network, and p s Is the probability of occurrence of scene s, V i spec Is the desired value of the voltage at node i, V i min Is the minimum value of the voltage at node i, V i max Is the maximum value of the voltage at node i.
And solving the light storage multi-target planning model of the active power distribution network by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme, which comprises the following steps:
decomposing the active power distribution network light storage multi-target planning model into an upper layer planning model, a middle layer planning model and a lower layer planning model by adopting a multi-layer optimization theory; the upper-layer planning model is used for optimizing the access position and the access capacity of the optical storage in the active power distribution network by taking the annual comprehensive cost, the annual active network loss and the average voltage offset of the active power distribution network as targets; the middle-layer planning model is used for optimizing a network topology structure in the active power distribution network by taking the minimum operation and maintenance cost of the active power distribution network in each scene as a target; the lower-layer planning model is used for optimizing the output of the interruptible load of the active power distribution network in each scene by taking the minimum operation and maintenance cost of the active power distribution network in each scene as a target;
and solving the upper layer planning model by adopting NSGA-III, solving the middle layer planning model by adopting a power distribution network frame reconstruction genetic algorithm, and solving the lower layer planning model by adopting an optimal power flow algorithm.
The power distribution network frame reconstruction genetic algorithm is based on ring network coding, and in the ring network coding stage, the process of determining the chromosome is as follows:
1) closing all interconnection switches in the active power distribution network, calculating a basic loop of the active power distribution network based on a graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of an mth gene position of a chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
2) disconnecting the branch switch corresponding to the mth gene position, recalculating the basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of the (m + 1) th gene position of the chromosome;
3) m is M +1, judging whether M is equal to M, if so, determining the chromosome is finished, otherwise, turning to 2); wherein M is the number of loci in the chromosome.
In the power distribution network frame reconstruction genetic algorithm, the mutation operation of chromosomes is carried out through a single-point mutation operator, and the cross operation of parent chromosomes is carried out through a redistribution operator.
The mutation operation comprises the following steps:
in the chrom p In (1), randomly selecting the mth gene site to executechrom p Mutation operation of (3); wherein, chrom p Is chromosome before mutation operation;
closed ADN p The branch switch corresponding to the mth gene in the network obtains a new active power distribution network topological structure ADN m Calculation of ADN based on graph theory m A basic loop of (a); wherein, ADN p Is chrom p The corresponding active power distribution network topological structure, and the branch switch is a tie switch or a section switch on a branch;
from ADN m In the basic loop, a closed branch switch is randomly opened to obtain a new active power distribution network topological structure ADN c According to ADN c Obtaining corresponding chromosome chrom after mutation operation c 。
The interleaving operation comprises the following steps:
11) randomly selecting two chromosomes from the parent population, and merging the two chromosomes into a chromosome set chrom;
12) closing all interconnection switches and section switches in the active power distribution network, and randomly selecting a closed branch switch from the chrom as a switch of the mth gene position of a descendant chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
13) disconnecting the branch switch corresponding to the mth gene locus, recalculating the basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch belonging to the chrom from the basic loop as a switch of the (m + 1) th gene locus of the chromosome;
14) m is M +1, judging whether M is equal to M, if so, finishing the cross operation to obtain a progeny chromosome, and if not, turning to 13); wherein M is the number of loci in the chromosome.
An active power distribution network optical storage multi-objective planning system, comprising:
the model construction module is used for constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active network loss and average voltage offset of the active power distribution network;
the model solving module is used for solving the active power distribution network light storage multi-target planning model by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform an active power distribution network optical storage multi-objective planning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing an active power distribution network light-storage multi-objective planning method
The invention achieves the following beneficial effects: 1. the method constructs an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction, realizes active power distribution network light storage multi-target planning by solving the model, and overcomes the one-sidedness of the traditional single-target planning by the model being a multi-target planning model; 2. the method improves the mutation operator and the redistribution operator in the ring network coding and power distribution network frame reconstruction genetic algorithm, avoids the appearance of the infeasible solutions of 'island' and 'ring network' in the active power distribution network frame reconstruction process, and effectively solves the problem of solving the active power distribution network optical storage multi-target planning model.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an exploded three-level model diagram;
FIG. 3 is a 7-node active power distribution network;
fig. 4 is a coding operation diagram of a 7-node active power distribution network structure;
fig. 5 is a variation operation diagram of the grid structure of the 7-node active power distribution network;
fig. 6 is a 7-node active power distribution network structure redistribution operation diagram;
FIG. 7 is a flow chart of model solution;
FIG. 8 is an IEEE-33 node active distribution network;
fig. 9 is a topology structure diagram of the active power distribution network frame after the network frame is reconstructed.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a method for planning multiple optical storage targets in an active power distribution network includes the following steps:
step1, constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active power network loss and average voltage offset of the active power distribution network;
step2, solving the light storage multi-target planning model of the active power distribution network by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
According to the method, an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction is established, active power distribution network light storage multi-target planning is achieved through a solving model, meanwhile, the model is a multi-target planning model, and the one-sidedness of traditional single-target planning is overcome.
The method comprises the steps of carrying out multi-scene analysis on output characteristic curves of photovoltaic and load in typical four seasons in the four seasons based on a scene analysis method, establishing a photovoltaic-load combined output scene, comprehensively considering the economy and stability of the operation of the active power distribution network, and constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction.
The model takes the annual comprehensive cost, the annual active network loss and the average voltage offset of the active power distribution network as targets, considers the power balance constraint, the node voltage constraint, the branch power constraint, the photovoltaic rated power constraint, the photovoltaic real-time output constraint, the energy storage charging and discharging power constraint, the energy storage SOC constraint, the load interruption amount constraint of interruptible loads and the network topology constraint of the active power distribution network, and can be expressed as follows by a formula:
the annual comprehensive cost of the active power distribution network mainly comprises fixed investment cost of light storage, operation maintenance cost of the light storage, cost of purchasing electricity to an upper-level power grid, government subsidy cost of photovoltaic power generation and 5 parts of demand side management cost of the active power distribution network, and the corresponding objective function is as follows:
minC＝C inv +C op +C b -C s +C DSM
wherein C is the annual comprehensive cost of the active power distribution network.
C inv For a fixed investment cost of optical storage, a specific formula can be as follows:
wherein r is pv Is the rate of the PV (solar cell panel) pasting, r ess Current rate of energy storage, N pv For a collection of photovoltaic nodes to be installed, N ess Set of nodes to be installed for energy storage, P pv,i For the rated power of the PV installed at node i,
fixed investment cost for PV unit active power, P ess,i Rated power to be installed at node i for energy storage, E ess,i To store the rated capacity to be installed at node i,
for a fixed investment cost of unit power of energy storage,
the fixed investment cost of the unit capacity of the energy storage is obtained.
C op For maintenance of light-storage operationsThe cost may be specifically formulated as follows:
wherein N is s The number of scenes to be considered is the combined output scene of the photovoltaic load, T is the time number, P pv,i,t,s Is the actual active power output of PV at a t time node i under a scene s, P ess,i,t,s The actual active output of the stored energy at the node i at the moment t under the scene s,
for the operating and maintenance cost per unit of PV power,
for operating and maintenance costs of stored energy per unit of electricity, T s ＝365p s The number of days of scene s in a year, p s Is the probability of scene s occurring.
C b To purchase the cost of electricity to the upper-level power grid, a specific formula can be as follows:
wherein, P c,t,s The power purchasing power C of the active power distribution network to the upper-level power grid at the moment t under the scene s t,s The electricity purchase price at the moment t under the scene s is shown.
C s For government subsidy of the photovoltaic power generation, the specific formula may be as follows:
wherein,
the government subsidy cost of the PV unit generated energy is provided.
C DSM For managing the cost on the demand side of the active power distribution network, a specific formula can be as follows:
wherein N is DSM For interruptible load node sets, P DSM,i,t,s Load interruption amount, ζ, of interruptible load at node i at time t under scene s DSM The charge is managed on the demand side for the interruption amount of the interruptible load per unit load.
Annual active power network loss is used as an important index for reflecting the operation economy of the power distribution network, and the corresponding objective function is as follows:
wherein, P loss For active distribution network annual active network loss, N l For the number of branches of the active distribution network, G l(i,j) For the conductance value of a branch l in the active power distribution network, i and j represent the node number of the branch l, V i,t,s Is the voltage amplitude, V, of the node i at the time t under the scene s j,t,s Is the voltage amplitude, delta, of the node j at the moment t under the scene s ij,t,s Is the voltage phase angle difference at time t for node i and node j under scenario s.
The average voltage offset is used as an important index for reflecting the operation stability of the power distribution network, and the corresponding objective function is as follows:
wherein, delta U is the average voltage offset of the active power distribution network, N is the number of nodes in the active power distribution network, and V i spec Is the desired value of the voltage at node i, V i min Is the minimum value of the voltage at node i, V i max Is the maximum value of the voltage at node i.
Constraint conditions are as follows:
and (3) power balance constraint of the active power distribution network:
wherein, P G,i,t,s Is the active power, Q, of the power supply at node i at time t under scene s G,i,t,s Is the reactive power of the power supply at node i at time t under scene s, P load,i,t,s Active power, Q, at time t for the resident load at node i under scene s load,i,t,s Active power at time t for the resident load at node i under scene s, G ij And B ij The system admittance value of the active power distribution network.
Node voltage constraint:
V i min ≤V i,t,s ≤V i max
wherein, V i min 、V i max Are each V i,t,s Lower and upper limits of.
Branch power constraint:
wherein S is l,t,s For the apparent power flowing through branch l at time t under scene s,
are respectively S l,t,s Lower and upper limits of.
Photovoltaic rated power constraint:
P pv,i ＝n pv,i P pv
wherein n is pv,i Number of PV installed for node i, P pv For the power rating of a single PV,
an upper limit value for PV active power allowed to be installed at node i.
Photovoltaic real-time output constraint:
P pv,i,t,s ＝P pv,i ·α pv,t,s
wherein alpha is pv,t,s And the photovoltaic output coefficient at the moment t under the scene s.
Energy storage charge and discharge power constraint:
P ess,i,t,s |≤P ess,i
wherein,
representing the charging power of the energy storage battery at the node i at the moment t under the scene s,
and (4) representing the discharge power of the energy storage battery at a node i at the time t under the scene s.
Energy storage SOC restraint:
therein, SOC i,t,s The number of charges stored at the node i at the time t under the scene s,
is SOC i,t,s Lower and upper limits of.
Load interruption amount constraint of interruptible load:
wherein, ω is dsm,i,t,s Is a fieldThe proportion of interruptions at time t of the interruptible load at node i under view s,
is omega dsm,i,t,s Lower and upper limits of.
Network topology constraint of the active power distribution network: in the process of reconstructing the network frame of the active power distribution network, the network topology structure of the active power distribution network is ensured to meet the constraints of radiancy and connectivity.
The constructed model is a multi-target model, so a hybrid solving strategy is adopted for solving, and the process can be as follows:
s1) decomposing the active power distribution network light storage multi-target planning model by adopting a multi-layer optimization theory.
Considering the complexity of the model, the model is decomposed according to a multi-layer optimization theory, and a three-layer planning model shown in fig. 2 can be established, and specifically decomposed into an upper-layer planning model, a middle-layer planning model and a lower-layer planning model.
The upper-layer planning model aims at minimizing annual comprehensive cost, annual active network loss and average voltage offset of the active power distribution network and is used for optimizing the access position and the access capacity of the optical storage in the active power distribution network.
The middle-lower layer planning model belongs to the sub-problem of optimizing operation of the active power distribution network, and mainly aims to optimize the operation mode of the active power distribution network under different scenes through extremely strong active adjustment capacity of the active power distribution network on the basis of the upper layer planning model; the middle-layer planning model is used for optimizing a network topology structure in the active power distribution network by taking the minimum operation and maintenance cost of the active power distribution network in each scene as a target; the lower-layer planning model aims at minimizing the operation and maintenance cost of the active power distribution network in each scene and is used for optimizing the output of the interruptible load of the active power distribution network in each scene.
As can be seen from fig. 2, for the sub-problem of the optimization operation of the active power distribution network, the minimum operation and maintenance cost of the active power distribution network in each scene is taken as an objective function to be optimized, and the objective functions of the middle and lower layer planning models can be represented in the following form in a merged manner:
s2) adopting NSGA-III to solve an upper layer planning model, adopting a power distribution network frame reconstruction genetic algorithm to solve a middle layer planning model, adopting an optimal power flow algorithm to solve a lower layer planning model, and obtaining a light storage multi-target planning scheme; wherein the optimal power flow algorithm adopts a primal-dual interior point method.
Both NSGA-III and the proto-dual interior point methods are known and will not be described in detail here.
When the traditional power distribution network frame reconstruction genetic algorithm is used for solving the network frame reconstruction problem, the switching-on and switching-off problem can be converted into the optimization problem of 0-1 decision variable, so that the states of a contact switch and a section switch are often represented by using a binary coding mode, the complexity of the problem is simplified, but when the binary coding mode is adopted for representing the states of the switches, great hidden danger exists. Firstly, because the number of interconnection switches in the active power distribution network is large, when the states of all the switches are expressed by using a binary coding mode, the coding dimension of an individual can be obviously improved, and further, the iterative convergence speed of an algorithm is influenced; in addition, the binary-based active power distribution network frame structure coding mode may cause that individuals generated in the optimization process cannot meet the network frame topological structure constraint of the configured network, thereby causing the generation of infeasible solutions such as 'isolated island' and 'ring network', and seriously affecting the accuracy and convergence rate of active power distribution network frame reconstruction.
Therefore, the traditional power distribution network frame reconstruction genetic algorithm is not suitable for the active power distribution network frame reconstruction problem, and needs to be improved, and the specific improvement mainly comprises the following two parts: one is to improve the ring network coding, and the other is to improve the mutation operator and the reassignment operator in the genetic algorithm.
According to the requirement of 'closed loop design and open loop operation' of an active power distribution network, the active power distribution network needs to meet the radiation and connectivity constraints during operation, namely, an island and a looped network form do not exist, in order to meet the requirement, through introducing 'successive ring opening and edge selection' operation in a coding stage, in the looped network coding stage, the process of determining chromosomes can be as follows:
1) closing all interconnection switches in the active power distribution network, calculating a basic loop of the active power distribution network based on a graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of an mth gene position of a chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
2) disconnecting the branch switch corresponding to the mth gene position, recalculating the basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of the (m + 1) th gene position of the chromosome;
3) judging whether M is equal to M or not when M is equal to M +1, if so, finishing chromosome determination, and otherwise, turning to 2); wherein M is the number of loci in the chromosome.
Taking the 7-node active power distribution network of fig. 3 as an example, in the figure, the solid line represents the branch with the section switch, the dotted line represents the branch with the interconnection switch, and the number represents the switch number of each branch.
As can be seen from fig. 3, the number of nodes in the active power distribution network is 7, the number of branches is 9, and in order to satisfy the radiation and connectivity constraints of the operation of the active power distribution network, it can be known from the related knowledge of graph theory that it is necessary to perform the disconnection operation on 3 sides, that is, to connect the branches of the switch, the chromosome is set to be chroma ═ l 1 ,l 2 ,l 3 Denotes wherein l m (m is 1,2,3) represents the switch of the mth gene position, then l m The determination is made by the following steps:
step 1: closing all interconnection switches in the active power distribution network, calculating basic loops of the active power distribution network based on the relevant knowledge of graph theory, and randomly selecting a branch switch from any one basic loop as l 1 ；
Step 2: branch switch l is opened on the basis of Step1 1 And recalculating basic loops of the active power distribution network, and randomly selecting a branch switch from any one basic loop as l 2 ；
Step 3: branch switch l is opened on the basis of Step2 2 Recalculating the basic loop of the distribution network, selecting one branch switch as l 3 ；
Step 4: opening branch switch l based on Step3 3 And obtaining a new active power distribution network frame topological structure form after the network frame is reconstructed.
As can be seen from the above steps, when the chroma is {2,3,4}, the encoding process can be represented by fig. 4. As can be seen from fig. 4, by performing the network frame reconstruction operation on the active power distribution network in the network frame coding form of the "improved ring network coding", the obtained new network frame topology structure of the active power distribution network necessarily meets the constraints of radiability and connectivity, which illustrates the effectiveness of the coding method.
In the power distribution network frame reconstruction genetic algorithm, the mutation operation of chromosomes is carried out through a single-point mutation operator, and the cross operation of parent chromosomes is carried out through a redistribution operator; wherein,
by using a single-point mutation operator in a traditional genetic algorithm for carrying out chromosome mutation operation, the mutation operation can be as follows:
11) in the chrom p In (1), selecting the mth gene site at random to execute chrom p Mutation operation of (3); wherein, chrom p Is chromosome before mutation operation;
12) closed ADN p A branch switch corresponding to the mth gene in the network is used for obtaining a new active power distribution network topological structure ADN m Calculation of ADN based on graph theory m A basic loop of (a); wherein, ADN p Is chrom p The branch switch is a tie switch or a section switch on a branch;
13) from ADN m In the basic loop, a closed branch switch is randomly opened to obtain a new active power distribution network topological structure ADN c According to ADN c Obtaining corresponding chromosome chrom after mutation operation c 。
Also taking the 7-node active power distribution network as an example, it is assumed that chromosomes are respectively chrom before and after mutation operation is performed p And chrom c Is represented by chrom p And chrom c The corresponding active power distribution network topology structures are respectively expressed as ADN p 、ADN c Then, the specific process of mutation operation is as follows:
step 1: in the chrom p In the method, a certain gene position m (m is 1,2,3) is randomly selected to execute chrom p Mutation operation of (3);
step 2: closed ADN p Branch switch l in m New active distribution network topology ADN m And calculating ADN m A basic loop of (a);
step 3: from ADN m Randomly selecting a branch switch in the basic loop to execute the on-off operation to obtain the ADN c Further, chromosome chrom is obtained c Is shown in (a).
In order to more intuitively represent the above steps, fig. 5 shows a specific process of the chromosome single point mutation operation. In FIG. 5, chrom is taken p And {2,3,4}, and the effectiveness of the single point mutation operator is studied by performing mutation operations on the locus No. 2. Through analysis of mutation results, after mutation operation is executed, the active power distribution network can still meet the radiation and connectivity constraints of a network topology structure while changing the grid structure, and therefore the effectiveness of the mutation operator is demonstrated.
The conventional crossover operator in the traditional genetic algorithm is improved, and the crossover operation of parent chromosomes is executed by using the reassignment operator, and the crossover operation can be as follows:
21) randomly selecting two chromosomes from the father generation population, and merging the two chromosomes into a chromosome set chrom;
22) closing all interconnection switches and section switches in the active power distribution network, and randomly selecting a closed branch switch from the chrom as a switch of the mth gene position of a descendant chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
23) disconnecting the branch switch corresponding to the mth gene locus, recalculating the basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch belonging to the chrom from the basic loop as a switch of the (m + 1) th gene locus of the chromosome;
24) m is M +1, judging whether M is equal to M, if so, finishing the cross operation to obtain a progeny chromosome, and otherwise, turning to 23); wherein M is the number of loci in the chromosome.
Taking the 7-node active power distribution network as an example, the specific process of implementing chromosome reallocation operation by using the reallocation operator is as follows:
step 1: randomly selecting two chromosomes from the parent generation population, and respectively marking as chrom 1 And chrom 2 Then the chrom is put into 1 And chrom 2 Merging, marking as chrom, and if chrom is chrom 1 ∪chrom 2 ；
Step 2: all tie switches and section switches in the active power distribution network are closed, and a branch switch l is randomly selected from the chrom 1 Performing a breaking operation;
step 3: calculating the basic loop of the active distribution network at the moment, and selecting a branch switch l from the basic loop 2 (l 2 E.g. chrom) to perform the on-off operation;
step 4: step3 is repeated to obtain branch switch l 3 Wherein l is 2 Belongs to chrom, and then obtains the descendant chromosome chrom after executing the redistribution operation s Wherein, chrom s ＝(l 1 ,l 2 ,l 3 )。
In order to more intuitively represent the above steps, fig. 6 shows a specific process for performing the reallocation operation. In FIG. 6, it is assumed that the parent chromosome before the reassignment operation is performed is chrom 1 2,5,8 and chrom 2 After performing the reassignment operation, the child chromosome is designated as chrom {3,5,6} s Then, as can be seen from FIG. 6, chrom s While retaining partial genes of the parent chromosome, a new gene combination is generated, the optimization space of the algorithm is enlarged, and the chromosome chrom after reallocation s The corresponding grid structure still meets the radiation and connectivity constraints, and the effectiveness of the reassignment operator is illustrated.
Solving work of the optical storage multi-target planning three-layer planning model is carried out by adopting a hybrid solving strategy based on NSGA-III and a combination of an Improved ring network coding genetic algorithm (IRC-GA) and a Primal-dual interior point method (PDIMP), and a specific solving flow is shown in FIG. 7.
According to the method, the looped network coding and the mutation operator and the redistribution operator in the power distribution network frame reconstruction genetic algorithm are improved, the occurrence of infeasible solutions such as 'isolated island' and 'looped network' in the active power distribution network frame reconstruction process is avoided, and the problem of solving the active power distribution network optical storage multi-target planning model is effectively solved.
To further illustrate the above process, a comparative experiment was performed:
in the experiment, an IEEE-33 node active power distribution network shown in figure 8 is adopted to carry out simulation analysis work of the method, the dotted line in the figure is a connecting line, and the node marked with DSM represents a user side load node capable of executing interrupt operation.
Assuming that the PV installation nodes to be selected are 6, 10, 14, 17, 28 and 32 and the upper limit of power installation is 0.5MW, in order to improve the absorption capacity of the PV and reduce the influence of load fluctuation on the distribution network, the optimal configuration of the energy storage can be performed at the PV installation nodes, and the economic parameters and the operating parameters of the PV and the energy storage are shown in table 1.
TABLE 1 economic and operational parameters of photovoltaic and energy storage
In consideration of the actual operation condition of the active power distribution network, in order to improve the programming rationality, a time-of-use electricity price mechanism is adopted to carry out multi-target programming work of the light storage in the active power distribution network, wherein the unit electricity purchasing cost in different time periods is shown in a table 2.
TABLE 2 cost per electricity purchase in different periods
As can be seen from table 2, in the off-peak electricity consumption period, the load is light because the current value is late at night, so the unit electricity purchase cost is low; and in the peak power consumption period, the power supply pressure of the active power distribution network is higher due to the higher load level of the power distribution network, so that the higher unit power purchase cost is implemented at the moment, unnecessary power consumption loads can be reduced to a certain extent, and the effect of relieving the power consumption pressure of the power distribution network is achieved.
The load interruption cost of the interruptible load is set to be 0.5 yuan/kW.h, and the interruption proportion range of the interruptible load is 0-100%. The maximum iteration number of NSGA-III is 50, the population plan is 40, the crossover and mutation operators respectively adopt a single-point crossover and single-point mutation mode, and meanwhile, in order to improve the capability of the algorithm in early global search and later local search, the crossover probability p of the crossover and mutation operators is set c And the probability of variation p m Linearly decreasing with the number of iterations, where p is assumed c Gradually decreases from 0.9 to 0.2, p m Gradually decreasing from 0.5 to 0.1; the maximum iteration number of IRC-GA is 50, the population size is 30, the variation rate of single-point variation operators is 0.1, and the redistribution rate of redistribution operators is linearly reduced to 0.1 from 0.9.
And solving the active power distribution network optical storage multi-target planning model to obtain a Pareto optimal solution set of the optical storage optimal configuration scheme shown in the table 3.
TABLE 3 Pareto optimal solution set for optimal configuration scheme of optical storage
By comprehensively considering various operation indexes of the active power distribution network under different light storage optimization configuration schemes in the table 3, selecting the light storage optimization configuration scheme corresponding to the serial number 4 as an optimal planning result of the light storage multi-objective planning problem, and under the light storage optimization configuration scheme corresponding to the serial number 4, solving the sub-problem of the optimal operation of the active power distribution network in the solving flow of fig. 7, the optimal operation mode of the active power distribution network under the planning scheme can be obtained, wherein the annual interruption condition of the interruptable load under the planning scheme is shown in the table 4.
TABLE 4 interruptible year load interruption
Fig. 9 shows a net rack topology structure diagram of the active power distribution network after the net rack reconstruction corresponding to the optical storage planning scheme is performed, and it can be seen from fig. 9 that the reconstructed net rack topology structure of the active power distribution network still meets the constraints of radiativity and connectivity, and the situations of "isolated island" and "ring network" do not occur, thereby explaining the effectiveness of the active power distribution network rack reconstruction algorithm in solving the active power distribution network rack reconstruction problem.
On the basis of the light storage multi-target planning scheme, the following four planning schemes are compared and analyzed, and the light storage multi-target planning scheme which considers the demand side management measures and the net rack reconstruction measures in the active power distribution network is further researched to play an important role in optimizing the operation indexes of the active power distribution network.
The first scheme comprises the following steps: the light storage multi-objective optimization configuration of demand side management measures and net rack reconstruction measures are not considered in the active power distribution network;
scheme II: only grid reconstruction measures are considered in the active power distribution network, and light storage multi-objective optimization configuration of demand side management measures is not considered;
and a third scheme is as follows: only considering the management measures of the demand side in the active power distribution network, but not considering the light storage multi-objective optimization configuration of the network frame reconstruction measures;
and the scheme is as follows: and the light storage multi-objective optimization configuration of the demand side management measures and the net rack reconstruction measures is considered in the active power distribution network.
The planning schemes are subjected to simulation analysis, and the optimal configuration results of the cost conditions and the light storage under different planning schemes can be obtained and are respectively shown in tables 5 and 6.
TABLE 5 optimal configuration results of light storage under different planning schemes
TABLE 6 cost status of each item in the annual integrated costs of different planning schemes
The optimization result is analyzed to obtain that:
compared with the first scheme, the second scheme has the advantages that the grid structure of the active power distribution network is optimized, so that the operating economy of the power distribution network is improved, and meanwhile, the voltage crossing risk of the power distribution network is reduced;
compared with the first scheme, the third scheme remarkably reduces the fixed investment cost and the electricity purchasing cost of the optical storage through the management measures on the demand side, and mainly meets the operation constraint of the power distribution network by partially cutting off the load instead of adding a new optical storage mode at certain heavy load moments, so that the method has important significance for improving the operation economy of the active power distribution network;
meanwhile, considering the demand side management and the net rack reconstruction measures, the annual comprehensive cost, the annual active network loss and the average voltage offset of the power distribution network can be obviously reduced by optimizing the net rack structure and the load output mode of the power distribution network, and compared with the scheme II and the scheme III, the annual comprehensive cost, the annual active network loss and the average voltage offset reduction corresponding to the scheme IV respectively reach 3.69%, 14.31%, 6.25%, 3.25%, 21.76% and 30.92%, so that the light storage optimization configuration scheme considering the demand side management and the net rack reconstruction plays an important role in improving the operating economy and stability of the active power distribution network.
The invention takes an active power distribution network as a research object, and researches the light storage multi-target planning problem in the active power distribution network by considering demand side management and network frame reconstruction, thereby obtaining the following conclusion:
1. by comprehensively considering the economy and stability of the operation of the power distribution network, a light storage multi-target planning three-layer planning model considering demand side management and network frame reconstruction in the active power distribution network is provided, and the one-sidedness of the traditional single-target planning is overcome; meanwhile, aiming at the complexity of the planning model, a hybrid solving strategy based on the combination of NSGA-III, IRC-GA and PDIMP is provided, and the solving problem of the provided model is effectively solved.
2. Aiming at the power distribution network frame reconstruction algorithm in the hybrid solving strategy, the invention provides a power distribution network frame structure coding form based on an improved looped network coding method based on the relevant knowledge of a graph theory, avoids the occurrence of infeasible solutions such as 'isolated island' and 'looped network' in the active power distribution network frame reconstruction process by improving a mutation operator and a redistribution operator in the network frame reconstruction algorithm, and effectively solves the solving problem of the provided model.
3. By comparing and analyzing the optimal configuration result of the optical storage under different planning schemes and the economic efficiency and stability indexes of the operation of the active power distribution network, the effectiveness of the proposed planning model and the hybrid solving strategy is verified, and meanwhile, the important roles of the demand side management measures and the network frame reconstruction measures in improving the operation quality of the active power distribution network are explained.
Based on the same technical scheme, the invention also discloses a software system of the method, and an active power distribution network light storage multi-target planning system comprises:
the model construction module is used for constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active power network loss and average voltage offset of the active power distribution network.
The model solving module is used for solving the active power distribution network light storage multi-target planning model by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
The data processing flow and method of each module in the software system are consistent, and the description is not repeated here.
Based on the same technical scheme, the invention also discloses a computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, and when the instructions are executed by the computing equipment, the computing equipment executes the active power distribution network optical storage multi-target planning method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing an active power distribution grid optical storage multi-objective planning method.
As will be appreciated by one skilled in the art, 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 an entirely hardware embodiment, an entirely 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, disk storage, CD-ROM, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (10)
1. The active power distribution network light storage multi-target planning method is characterized by comprising the following steps:
constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active power network loss and average voltage offset of the active power distribution network;
solving an active power distribution network light storage multi-target planning model by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
2. The active power distribution network light-storage multi-objective planning method according to claim 1, wherein an objective function of the active power distribution network light-storage multi-objective planning model is as follows:
minC＝C inv +C op +C b -C s +C DSM
wherein C is the annual comprehensive cost of the active power distribution network inv Fixed investment costs for light storage, C op Operating maintenance costs for light stores, C b Cost for purchasing electricity to the upper-level grid, C s Subsidizing expenses for governments of photovoltaic power generation, C DSM Managing expenses for a demand side of the active power distribution network;
wherein, P loss For active distribution network year active network loss, N l Number of branches for active distribution network, N s The number of scenes to be considered is the combined output scene of the photovoltaic load, T is the time number, G l(i,j) For the conductance value of a branch l in the active power distribution network, i and j represent the node number of the branch l, V i,t,s Is the voltage amplitude, V, of the node i at time t under the scene s j,t,s Is the voltage amplitude, delta, of the node j at the moment t under the scene s ij,t,s Is the voltage phase angle difference of node i and node j at time T under scene s, T s The number of days of the scene s in one year;
wherein, Delta U is the average voltage offset of the active power distribution network, N is the number of nodes in the active power distribution network, and p s Is the probability of occurrence of scene s, V i spec Is the desired value of the voltage at node i, V i min Is the minimum value of the voltage at node i, V i max Is the maximum value of the voltage at node i.
3. The active power distribution network light-storage multi-objective planning method according to claim 1, wherein a hybrid solving strategy is adopted to solve the active power distribution network light-storage multi-objective planning model to obtain a light-storage multi-objective planning scheme, and the method comprises the following steps:
decomposing the active power distribution network light storage multi-target planning model into an upper layer planning model, a middle layer planning model and a lower layer planning model by adopting a multi-layer optimization theory; the upper-layer planning model is used for optimizing the access position and the access capacity of the optical storage in the active power distribution network by taking the annual comprehensive cost, the annual active network loss and the average voltage offset of the active power distribution network as targets; the middle-layer planning model is used for optimizing a network topology structure in the active power distribution network by taking the minimum operation and maintenance cost of the active power distribution network in each scene as a target; the lower-layer planning model is used for optimizing the output of the interruptible load of the active power distribution network in each scene by taking the minimum operation and maintenance cost of the active power distribution network in each scene as a target;
and solving the upper layer planning model by adopting NSGA-III, solving the middle layer planning model by adopting a power distribution network frame reconstruction genetic algorithm, and solving the lower layer planning model by adopting an optimal power flow algorithm.
4. The active power distribution network light storage multi-target planning method according to claim 3, wherein the power distribution network frame reconstruction genetic algorithm is a power distribution network frame reconstruction genetic algorithm based on ring network coding, and in a ring network coding stage, the process of determining chromosomes is as follows:
1) closing all interconnection switches in the active power distribution network, calculating a basic loop of the active power distribution network based on a graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of an mth gene position of a chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
2) disconnecting a branch switch corresponding to the mth gene position, recalculating a basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch from the basic loop as a switch of the (m + 1) th gene position of the chromosome;
3) m is M +1, judging whether M is equal to M, if so, determining the chromosome is finished, otherwise, turning to 2); wherein M is the number of loci in the chromosome.
5. The active power distribution network light storage multi-objective planning method of claim 1, wherein in the power distribution network frame reconstruction genetic algorithm, a single-point mutation operator is used for carrying out chromosome mutation, and a redistribution operator is used for carrying out crossover operation of parent chromosomes.
6. The active power distribution network light-storage multi-objective planning method according to claim 5, wherein the mutation operation comprises:
in the chrom p In (1), selecting the mth gene site at random to execute chrom p Mutation operation of (3); wherein, chrom p Is chromosome before mutation operation;
closed ADN p The branch switch corresponding to the mth gene in the network obtains a new active power distribution network topological structure ADN m Calculation of ADN based on graph theory m A basic loop of (a); wherein, ADN p Is chrom p The corresponding active power distribution network topological structure, and the branch switch is a tie switch or a section switch on a branch;
from ADN m In the basic loop, a closed branch switch is randomly opened to obtain a new active power distribution network topological structure ADN c According to ADN c Obtaining corresponding chromosome chrom after mutation operation c 。
7. The active power distribution network light-storage multi-objective planning method according to claim 5, wherein the crossover operation comprises:
11) randomly selecting two chromosomes from the parent population, and merging the two chromosomes into a chromosome set chrom;
12) closing all interconnection switches and section switches in the active power distribution network, and randomly selecting a closed branch switch from the chrom as a switch of the mth gene position of a descendant chromosome, wherein m is 1; the branch switch is a tie switch or a section switch on the branch;
13) disconnecting the branch switch corresponding to the mth gene locus, recalculating the basic loop of the active power distribution network based on graph theory, and randomly selecting a closed branch switch belonging to the chrom from the basic loop as a switch of the (m + 1) th gene locus of the chromosome;
14) m is M +1, judging whether M is equal to M, if so, finishing the cross operation to obtain a progeny chromosome, and if not, turning to 13); wherein M is the number of loci in the chromosome.
8. The utility model provides an active power distribution network light stores up multiobjective planning system which characterized in that includes:
the model construction module is used for constructing an active power distribution network light storage multi-target planning model considering demand side management and network frame reconstruction; the active power distribution network light storage multi-target planning model aims at minimizing annual comprehensive cost, annual active power network loss and average voltage offset of the active power distribution network;
the model solving module is used for solving the active power distribution network light storage multi-target planning model by adopting a hybrid solving strategy to obtain a light storage multi-target planning scheme; the hybrid solving strategy is a hybrid solving strategy combining NSGA-III, a power distribution network frame reconstruction genetic algorithm and an optimal power flow algorithm.
9. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising:
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210585106.7A CN115018140A (en) | 2022-05-27 | 2022-05-27 | Active power distribution network light storage multi-target planning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210585106.7A CN115018140A (en) | 2022-05-27 | 2022-05-27 | Active power distribution network light storage multi-target planning method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115018140A true CN115018140A (en) | 2022-09-06 |
Family
ID=83071308
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210585106.7A Pending CN115018140A (en) | 2022-05-27 | 2022-05-27 | Active power distribution network light storage multi-target planning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115018140A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545465A (en) * | 2022-09-30 | 2022-12-30 | 国网山东省电力公司潍坊供电公司 | Multi-target joint planning method and system for distributed photovoltaic and energy storage |
-
2022
- 2022-05-27 CN CN202210585106.7A patent/CN115018140A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545465A (en) * | 2022-09-30 | 2022-12-30 | 国网山东省电力公司潍坊供电公司 | Multi-target joint planning method and system for distributed photovoltaic and energy storage |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106230026B (en) | Power distribution network double-layer coordination planning method containing distributed power supply based on time sequence characteristic analysis | |
| CN103955864B (en) | Based on the electric system multiple target differentiation planing method for improving harmonic search algorithm | |
| CN109768573A (en) | Var Optimization Method in Network Distribution based on multiple target difference grey wolf algorithm | |
| Yan et al. | Planning of Electric Vehicle charging station based on hierarchic genetic algorithm | |
| CN105488593A (en) | Constant capacity distributed power generation optimal site selection and capacity allocation method based on genetic algorithm | |
| CN113378100B (en) | Power distribution network source network load storage collaborative optimization scheduling model and method considering carbon emission | |
| CN106570579B (en) | Water, fire and electricity economic dispatching method based on improved quantum particle swarm algorithm | |
| CN108667054B (en) | Energy storage planning method and device | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN111291978A (en) | Two-stage energy storage method and system based on Benders decomposition | |
| CN112381262B (en) | Micro-grid planning method considering load voltage characteristics and uncertainty of new energy intensity | |
| CN112446533A (en) | Multi-target planning method for AC/DC hybrid power distribution network | |
| Li et al. | Multiobjective optimization configuration of a prosumer’s energy storage system based on an improved fast nondominated sorting genetic algorithm | |
| CN115018140A (en) | Active power distribution network light storage multi-target planning method and system | |
| Li et al. | Impact on traditional hydropower under a multi-energy complementary operation scheme: An illustrative case of a ‘wind–photovoltaic–cascaded hydropower plants’ system | |
| Salkuti | Binary bat algorithm for optimal operation of radial distribution networks | |
| Zou et al. | A MOEA/D approach using two crossover strategies for the optimal dispatches of the combined cooling, heating, and power systems | |
| CN118117572A (en) | High-reliability improving method for integrated renewable energy comprehensive energy supply system | |
| CN116882105A (en) | Method for analyzing boundaries of power supply modes of large-scale photovoltaic access AC/DC power distribution network | |
| Zhao et al. | Research on Multiobjective Optimal Operation Strategy for Wind‐Photovoltaic‐Hydro Complementary Power System | |
| CN115496427A (en) | Flexible interconnection investment planning decision method for multi-microgrid system | |
| Li et al. | A novel optimization framework for integrated local energy system multi-objective dispatch problem based on dynamic knowledge base | |
| Su et al. | A Data-Driven Multi-Time Scale Coordinated Economic Dispatch Model for Flexible Interconnection Low-voltage Distribution Station Areas | |
| Shao et al. | Optimization method based on load forecasting for three-phase imbalance mitigation in low-voltage distribution network | |
| Du et al. | Distribution Network Planning with Comprehensive Economy and Reliability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |