CN110866702A - Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability - Google Patents
Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability Download PDFInfo
- Publication number
- CN110866702A CN110866702A CN201911142501.2A CN201911142501A CN110866702A CN 110866702 A CN110866702 A CN 110866702A CN 201911142501 A CN201911142501 A CN 201911142501A CN 110866702 A CN110866702 A CN 110866702A
- Authority
- CN
- China
- Prior art keywords
- planning
- distribution network
- power distribution
- reliability
- layer
- 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
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000004069 differentiation Effects 0.000 title claims abstract description 11
- 230000005611 electricity Effects 0.000 claims abstract description 32
- 238000010276 construction Methods 0.000 claims abstract description 21
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 8
- 230000002068 genetic effect Effects 0.000 claims abstract description 5
- 230000001737 promoting effect Effects 0.000 claims abstract description 5
- 230000004044 response Effects 0.000 claims description 15
- 230000005540 biological transmission Effects 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 238000013461 design Methods 0.000 claims description 4
- 108091026890 Coding region Proteins 0.000 claims description 3
- 210000000349 chromosome Anatomy 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- G06Q10/067—Enterprise or organisation modelling
-
- 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
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability, which comprises the following steps: s1, establishing an initial extension planning scheme of the power distribution network, selecting a set of lines to be constructed, and establishing a planning layer model taking the minimum annual comprehensive cost of the power distribution network as a target function; s2, considering the participation factors of the power distribution network, and establishing an operation layer model taking the dynamic reconstruction of the grid structure aiming at promoting the maximum power supply capacity under different operation environments into consideration; s3, solving the planning layer model and the operation layer model: the planning layer adopts a genetic algorithm to solve, and a power distribution network construction scheme is optimized; and the operation layer adopts a standard particle swarm algorithm to solve, peak-valley electricity prices are optimized, and overall model solution is completed through mutual nesting of solution algorithms of the planning part and the operation layer to obtain an optimal planning scheme. The method and the device can provide support for improving the reliability of the existing power distribution network, and are beneficial to improving the planning and management level of the urban power distribution network.
Description
Technical Field
The invention belongs to the technical field of urban network planning management, and particularly relates to a power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability.
Background
The power distribution network is an important link for producing, transmitting and using electric energy and is a link for connecting users with a power generation and transmission system. The power distribution network takes an important role of transmitting electric energy from a power supply or a power transmission network to users with different voltage grades, is directly connected with the users, and has great influence on power supply reliability. According to the statistics of electric power companies, 80% of the power failure accidents of users are caused by the faults of the power distribution system. Therefore, the reliability of the power distribution network is very important to be improved. With the continuous promotion of the innovation of the power system, the innovation of the power selling side enables users to have the right of independent selection, and the users are entitled to provide higher requirements for power supply companies, so that the safety and the reliability of self power utilization are guaranteed. Price is the most core function of the market, and in a value chain of the loop of power production to consumption, the power market can capture value fluctuation of different time, different spaces and different links and express the value fluctuation in a price form. The user is different to power supply quality, the difference of power supply reliability demand, must lead to the difference of electric energy price, and the user can be according to the difference of self demand and the difference of market electric energy price, provides the electric energy quality and the reliability requirement that are fit for oneself to power supply enterprise, and power supply enterprise's task then satisfies user differentiation reliability demand under the direction of market.
At present, partial domestic cities begin to construct and transform power distribution networks, the conditions for flexible and active operation of the power distribution networks are preliminarily met, and active management control over power distribution network frame structures, distributed power supply equipment, operation equipment and the like can be achieved. However, the existing power distribution network planning method does not fully explore and consider the differential reliability requirements of different types of power users, so that the construction of part of lines is not the optimal selection under the current condition, and the construction investment cost is increased. Therefore, when the power distribution network planning design is particularly designed for the existing power distribution network, the influence of the differentiated reliability requirements of different types of power users during the operation of the power distribution network needs to be considered. Therefore, based on the problems, the power distribution network double-layer extension planning method considering the dynamic reconstruction influence of the grid structure and the differential reliability requirements is provided, and has important practical significance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power distribution network double-layer expansion planning method considering the dynamic reconfiguration influence of a grid structure and the differential reliability requirements.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability comprises the following steps:
s1, establishing an initial extension planning scheme of the power distribution network, and selecting a set of lines to be established; the planning layer is responsible for the decision of planning the scheme, namely deciding the set of the selected lines to be constructed in the scheme. The upper-layer planning model takes the lowest annual comprehensive cost of the power distribution network as a model target, takes the differential reliability requirement as a main flexible constraint and takes the selection of a newly expanded and constructed line as a model decision variable.
For the annual combined cost of a built distribution network, the economic cost considered by the invention comprises the following parts:
i) construction cost of the newly-built line: when a new grid network circuit is expanded and constructed by the power distribution network, the construction investment cost of the newly-constructed circuit is matched with the annual operation cost of the power distribution network, and the construction cost of the expanded planning of the power distribution network is converted into an equal annual value for calculation and analysis;
ii) power purchase cost of the power distribution network: the power distribution network cannot completely bear all user loads in the region in the operation process, needs the support of a superior power grid, and generates the electricity purchasing cost for the superior power grid;
iii) loss cost of the network: network power loss which is inevitably generated during the operation of the power distribution network;
therefore, a planning layer model with the minimum annual comprehensive cost of the power distribution network as an objective function is established, and the following formula is shown:
minF＝Finv+Foper
Foper＝Fpe+Floss
in the formula, F is the annual comprehensive cost of the power distribution network; finvThe investment cost for the construction of the net rack is equal to the annual value; foperTotal annual operating costs for planning the project; fpeThe total electricity purchasing cost to the superior power grid is achieved every year; flossThe total annual network loss cost;
wherein:
in the formula, xfl,hIs a 0-1 state variable and represents the state of the h-th line to be newly built, and 1 represents the lineThe road is selected to be newly built, and 0 represents that the road is not selected to be newly built; sfl,hRepresenting the construction cost of the h line; d is the discount rate; y represents the age; c. CdPurchasing the electricity unit price for the upper-level power grid; c. ClossThe unit network loss cost; t is an operation period; smaxIs the total number of operational scenarios; omegaiRepresenting the probability of the occurrence of the ith scene; pload,iRepresenting the total power of the load in the ith scene; ploss,iRepresenting the loss power of the network under the ith scene;
s2, one of the biggest differences between power distribution network planning and traditional power distribution network planning is that a large number of distributed power supplies and controllable loads exist in the power distribution network, and the power distribution network can carry out real-time scheduling control on the operation of the whole system through advanced power electronics, communication, remote measurement, remote control and other technologies. Therefore, when planning and designing the power distribution network, the operation state is considered, and the condition under the fixed network state is not considered, and the actual flexibly-changed operation state of the power distribution network is considered. Therefore, the invention considers the participation factors of the power distribution network, and mainly takes the dynamic reconstruction of the grid structure aiming at promoting the maximum power supply capacity under different operating environments into account of the component operating layer model. And the operation layer calculates operation data of the network frame planning scheme under the condition of considering the dynamic reconfiguration of the power distribution network on the basis of the network frame planning scheme transmitted by the planning layer, and feeds the operation data as a return quantity back to the planning layer to be used as a part of the objective function value of the calculation scheme.
Considering the participation factors of the power distribution network, establishing an operation layer model taking the dynamic reconstruction of the grid structure aiming at promoting the maximum power supply capacity under different operation environments into consideration, wherein the operation layer model has the following characteristics that the maximum power supply capacity is an objective function:
in the formula, p is a main transformer; q is a feeder line; the psc is the power supply capacity of the active power distribution network; l ispqmaxThe load value of a q-th feeder line connected with a p-th main transformer which can be accessed at the peak load moment of the whole network is obtained;
s3, solving the model of the planning layer and the operation layer
In the invention, a planning layer adopts a genetic algorithm to solve and optimize a power distribution network construction scheme; and the operation layer adopts a standard particle swarm algorithm to solve, and optimizes the peak-valley electricity price. The planning layer provides a network frame planning scheme for the operation layer by aiming at the minimum annual comprehensive cost of the power distribution network, the annual comprehensive cost of the power distribution network comprises an equal-year-value cost and an annual operation cost of network construction, the equal-year-value cost of the construction is determined by a line to be constructed in the planning scheme, and the annual operation cost is obtained by simulation operation in the lower active power distribution network environment; the operation layer obtains a current planning scheme from the planning layer, determines the switching state of the power distribution network, namely the network frame operation structure, in each scene according to the maximum power supply capacity of the power distribution network, and calculates annual operation parameters such as the demand response quantity, the electricity price, the electricity purchasing quantity, the network loss and the distributed power output of the current planning scheme so that the planning layer can calculate the annual comprehensive cost of the planning scheme. The overall model solution of the whole model is completed by mutually nesting two parts of solution algorithms to obtain an optimal planning scheme, and the specific solution flow is as follows:
s301, selecting an initial power distribution network expansion planning scheme, and coding a planning layer planning scheme according to a coding mode of a genetic algorithm, namely coding the construction state of a line to be constructed in the planning scheme;
s302, transmitting a planning scheme code of a planning layer as a basic parameter to an operation layer, and optimizing distributed power supply output, demand response quantity, electricity price, total load and network loss of the active power distribution network in each operation scene, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated;
s303, performing integer coding on each switching state of the active power distribution network, and randomly generating an initial particle swarm of a running layer;
s304, optimizing an operation layer by utilizing a particle swarm algorithm to obtain the output, the demand response quantity, the electricity price, the total load and the network loss of the optimal distributed power supply of each current planning scheme, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated and obtained, and is fed back to the planning layer;
s305, calculating the operation cost of each planning scheme by the planning layer, and obtaining the fitness of each planning scheme in the population by combining the equal-year-value construction cost of each scheme;
and S306, judging whether the planning layer reaches a convergence condition, if not, performing cross variation operation on individuals of the planning layer to obtain a new planning scheme population code, returning to the step S302, otherwise, outputting an optimal result.
Further, the encoding method and the solving process in step S301 are as follows:
s3011, randomly arranging lines to be built to generate chromosome codes;
s3012, dividing the established nodes with contact relations into a plurality of subsets A1,A2,…,AnAnd classifying the subsets, wherein the grade of the subset containing the power supply nodes is 1, otherwise, the grade is 0;
s3013, judging the relation between the load nodes at the two ends of each line and the existing subset according to the coding sequence; suppose that the current line number is h and the nodes at both ends are Ki、KjThe judgment method is as follows:
if Ki、KjNot in the existing subset, then xfl,hSet 1 and establish a new subset an+1＝{Ki+Kj-rank 0;
if Ki、KjOnly one belonging to the existing subset aiIn, then xfl,h1, and Ai＝{Ai+(Ki/Kj) The subset grade is unchanged;
if Ki、KjIn the same subset, xfl,h0, avoiding forming a ring network;
if Ki、KjPresent in different subsets Ai、AjIn, then xfl,h1, and Ai＝{Ai+AjThe combined subset grade is the sum of 2 original subsets;
s3014, when the number of the nodes in all the subsets is equal to the sum of the number of the existing nodes and the number of the nodes to be built, ending the generation process and obtaining a building scheme under the current coding; otherwise, step S3013 is repeated.
Further, the planning layer model constraints include:
i) system connectivity constraints: each newly-built load point and distributed power supply access point should form contact with the upper-layer power grid power supply, and an island condition does not occur;
ii) power distribution network wiring mode constraint: when the operation layer optimizes the space truss structure, the states of the interconnection switch and the section switch need to be considered, the scheme obtained by the planning layer allows a loop to appear, and the breaking state of the interconnection switch is determined according to the principles of closed-loop design and open-loop operation during operation;
and iii) the load rate of each feeder line in the planning scheme should meet the requirement of a power distribution network planning guide rule under the current wiring mode, the load requirement of the power distribution network under each dynamic reconfiguration scheme and the constraint of N-1 verification under a fault state.
Further, the planning scheme needs to satisfy the following constraints in the operation phase:
i) constraint on differential reliability
The ASAI of the q-th feeder is as follows:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix is defined
E＝(E1,E2,…,Eq,…,Em)TIn which EqFor the q-th feeder reliability target, ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TFor an actual feeder reliability index vector, where m is the number of system feeders, the differential reliability constraint is expressed as:
ASAIfeed≥E
ii) overall reliability constraints
The system meets the reliability requirements of different feeder lines and also meets the reliability constraint of the whole network, and selects the ASAI expected value of the system as an index, which is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
iii) line flow constraints of the power distribution system:
in the formula: pGi,t、QGi,t、Li,tAnd Di,tActive output, reactive output, active load and reactive load of the node i in the time period t; u shapei,t、Uj,tIs the voltage amplitude of the nodes i, j during the time period t; gij、BijThe conductance and susceptance of branch ij; thetaij,tIs the phase angle difference of the voltage between the nodes i and j in the time period t;
iv) distribution network node voltage constraints:
Uimin＜Ui,t＜Uimax
in the formula of Ui,tIs the voltage amplitude, U, of node i during time period timinAnd UimaxThe minimum and maximum voltage at node i, respectively;
v) line transmission ampacity constraint:
in the formula IkRepresenting the magnitude of the current on the k-th line, Ik maxThe upper limit of the current-carrying capacity for line transmission; when the power distribution network operates, the transmission current of the line is constrained by thermodynamics and point dynamics;
vi) total cost constraint of electricity purchase before and after demand response:
in the formula, Cf,i、Cg,i、Cp,iRespectively representing peak-valley and mean-time electricity prices of the nodes i; t is tf、tp、tgAnd tsRespectively representing divided peak-to-valley level periods and total periods; l isi,tDemand response quantity of the node i in the time period t;
vii) peak-to-valley electricity price ratio constraint:
k1≤Cf/Cg≤k2
in the formula, k1And k2Respectively representing constraint coefficients; cf、CgRespectively representing the electricity prices in the peak-valley period;
viii) power supplier offer constraints:
in the formula, ksRepresenting a constraint coefficient;
ix) DG output constraint:
PDG,min≤PGi,t≤PDG,max
QDG,min≤QGi,t≤QDG,max
in the formula, PGi,tAnd QGi,tRepresenting DG active and reactive power output of the node i in a time period t; pDG,minAnd PDG,maxRepresenting the DG minimum and maximum active outputs of node i; qDG,minAnd QDG,maxRepresenting the DG minimum and maximum reactive power contribution for node i.
The invention has the advantages and positive effects that:
the invention aims to establish a power distribution network planning method considering demand response and difference reliability requirements, and the method can be used for considering the demand response and the difference reliability requirements in power distribution network planning on the basis of traditional power distribution network extension planning by combining the operation characteristics of a power distribution network, so that the difference reliability requirements of power users can be met, support can be provided for improving the reliability of the existing power distribution network, the urban power distribution network planning management level can be improved, and the reasonable development of the urban power distribution network can be promoted.
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any individual technical features described or implicit in the embodiments mentioned herein may still be continued in any combination or subtraction between these technical features (or their equivalents) to obtain still further embodiments of the invention that may not be mentioned directly herein.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Examples
The power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability provided by the embodiment comprises the following steps:
s1, establishing a power distribution network initial expansion planning scheme, selecting a set of lines to be established, and establishing a planning layer model taking the minimum annual comprehensive cost of the power distribution network as a target function, wherein the planning layer model is as follows:
minF＝Finv+Foper
Foper＝Fpe+Floss
in the formula, F is the annual comprehensive cost of the power distribution network; finvThe investment cost for the construction of the net rack is equal to the annual value; foperTotal annual operating costs for planning the project; fpeThe total electricity purchasing cost to the superior power grid is achieved every year; flossThe total annual network loss cost;
wherein:
in the formula, xfl,hThe state variable is 0-1, the state variable represents the state of the h-th line to be newly built, 1 represents that the line is selected to be newly built, and 0 represents that the line is not selected to be newly built; sfl,hRepresenting the construction cost of the h line; d is the discount rate; y represents the age; c. CdPurchasing the electricity unit price for the upper-level power grid; c. ClossThe unit network loss cost; t is an operation period; smaxIs the total number of operational scenarios; omegaiRepresenting the probability of the occurrence of the ith scene; pload,iRepresenting the total power of the load in the ith scene; ploss,iRepresenting the loss power of the network under the ith scene;
s2, considering distribution network participation factors, and establishing an operation layer model taking dynamic reconstruction of a grid structure aiming at promoting maximum power supply capacity under different operation environments into consideration, wherein the operation layer model has the following maximum power supply capacity function:
in the formula, p is a main transformer; q is a feeder line; the psc is the power supply capacity of the active power distribution network; l ispqmaxThe load value of a q-th feeder line connected with a p-th main transformer which can be accessed at the peak load moment of the whole network is obtained;
s3, solving the model of the planning layer and the operation layer
S301, selecting an initial power distribution network expansion planning scheme, and coding a planning layer planning scheme according to a coding mode of a genetic algorithm, namely coding the construction state of a line to be constructed in the planning scheme;
s302, transmitting a planning scheme code of a planning layer as a basic parameter to an operation layer, and optimizing distributed power supply output, demand response quantity, electricity price, total load and network loss of the active power distribution network in each operation scene, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated;
s303, performing integer coding on each switching state of the active power distribution network, and randomly generating an initial particle swarm of a running layer;
s304, optimizing an operation layer by utilizing a particle swarm algorithm to obtain the output, the demand response quantity, the electricity price, the total load and the network loss of the optimal distributed power supply of each current planning scheme, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated and obtained, and is fed back to the planning layer;
s305, calculating the operation cost of each planning scheme by the planning layer, and obtaining the fitness of each planning scheme in the population by combining the equal-year-value construction cost of each scheme;
and S306, judging whether the planning layer reaches a convergence condition, if not, performing cross variation operation on individuals of the planning layer to obtain a new planning scheme population code, returning to the step S302, otherwise, outputting an optimal result.
Further, the encoding method and the solving process in step S301 are as follows:
s3011, randomly arranging lines to be built to generate chromosome codes;
s3012, dividing the established nodes with contact relations into a plurality of subsets A1,A2,…,AnAnd classifying the subsets, wherein the grade of the subset containing the power supply nodes is 1, otherwise, the grade is 0;
s3013, judging the relation between the load nodes at the two ends of each line and the existing subset according to the coding sequence; suppose that the current line number is h and the nodes at both ends are Ki、KjThe judgment method is as follows:
if Ki、KjNot in the existing subset, then xfl,hSet 1 and establish a new subset an+1＝{Ki+Kj-rank 0;
if Ki、KjOnly one belonging to the existing subset aiIn, then xfl,h1, and Ai＝{Ai+(Ki/Kj) The subset grade is unchanged;
if Ki、KjIn the same subset, xfl,h0, avoiding forming a ring network;
if Ki、KjPresent in different subsets Ai、AjIn, then xfl,h1, and Ai＝{Ai+AjThe combined subset grade is the sum of 2 original subsets;
s3014, when the number of the nodes in all the subsets is equal to the sum of the number of the existing nodes and the number of the nodes to be built, ending the generation process and obtaining a building scheme under the current coding; otherwise, step S3013 is repeated.
Further, the planning layer model constraints include:
i) system connectivity constraints: each newly-built load point and distributed power supply access point should form contact with the upper-layer power grid power supply, and an island condition does not occur;
ii) power distribution network wiring mode constraint: when the operation layer optimizes the space truss structure, the states of the interconnection switch and the section switch need to be considered, the scheme obtained by the planning layer allows a loop to appear, and the breaking state of the interconnection switch is determined according to the principles of closed-loop design and open-loop operation during operation;
and iii) the load rate of each feeder line in the planning scheme should meet the requirement of a power distribution network planning guide rule under the current wiring mode, the load requirement of the power distribution network under each dynamic reconfiguration scheme and the constraint of N-1 verification under a fault state.
Further, the planning scheme needs to satisfy the following constraints in the operation phase:
i) constraint on differential reliability
The ASAI of the q-th feeder is as follows:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix is defined
E＝(E1,E2,…,Eq,…,Em)TIn which EqFor the q-th feeder reliability target, ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TFor an actual feeder reliability index vector, where m is the number of system feeders, the differential reliability constraint is expressed as:
ASAIfeed≥E
ii) overall reliability constraints
The system meets the reliability requirements of different feeder lines and also meets the reliability constraint of the whole network, and selects the ASAI expected value of the system as an index, which is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
iii) line flow constraints of the power distribution system:
in the formula: pGi,t、QGi,t、Li,tAnd Di,tActive output, reactive output, active load and reactive load of the node i in the time period t; u shapei,t、Uj,tIs the voltage amplitude of the nodes i, j during the time period t; gij、BijIs the conductance of branch ij,Susceptance; thetaij,tIs the phase angle difference of the voltage between the nodes i and j in the time period t;
iv) distribution network node voltage constraints:
Uimin＜Ui,t＜Uimax
in the formula of Ui,tIs the voltage amplitude, U, of node i during time period timinAnd UimaxThe minimum and maximum voltage at node i, respectively;
v) line transmission ampacity constraint:
in the formula IkRepresenting the magnitude of the current on the k-th line, Ik maxThe upper limit of the current-carrying capacity for line transmission; when the power distribution network operates, the transmission current of the line is constrained by thermodynamics and point dynamics;
vi) total cost constraint of electricity purchase before and after demand response:
in the formula, Cf,i、Cg,i、Cp,iRespectively representing peak-valley and mean-time electricity prices of the nodes i; t is tf、tp、tgAnd tsRespectively representing divided peak-to-valley level periods and total periods; l isi,tDemand response quantity of the node i in the time period t;
vii) peak-to-valley electricity price ratio constraint:
k1≤Cf/Cg≤k2
in the formula, k1And k2Respectively representing constraint coefficients; cf、CgRespectively representing the electricity prices in the peak-valley period;
viii) power supplier offer constraints:
in the formula, ksRepresenting a constraint coefficient;
ix) DG output constraint:
PDG,min≤PGi,t≤PDG,max
QDG,min≤QGi,t≤QDG,max
in the formula, PGi,tAnd QGi,tRepresenting DG active and reactive power output of the node i in a time period t; pDG,minAnd PDG,maxRepresenting the DG minimum and maximum active outputs of node i; qDG,minAnd QDG,maxRepresenting the DG minimum and maximum reactive power contribution for node i.
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (4)
1. The power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability is characterized by comprising the following steps of: the planning method comprises the following steps:
s1, establishing a power distribution network initial expansion planning scheme, selecting a set of lines to be established, and establishing a planning layer model taking the minimum annual comprehensive cost of the power distribution network as a target function, wherein the planning layer model is as follows:
minF＝Finv+Foper
Foper＝Fpe+Floss
in the formula, F is the annual comprehensive cost of the power distribution network; finvThe investment cost for the construction of the net rack is equal to the annual value; foperTotal annual operating costs for planning the project; fpeThe total electricity purchasing cost to the superior power grid is achieved every year; flossThe total annual network loss cost;
wherein:
in the formula, xfl,hThe state variable is 0-1, the state variable represents the state of the h-th line to be newly built, 1 represents that the line is selected to be newly built, and 0 represents that the line is not selected to be newly built; sfl,hRepresenting the construction cost of the h line; d is the discount rate; y represents the age; c. CdPurchasing the electricity unit price for the upper-level power grid; c. ClossThe unit network loss cost; t is an operation period; smaxIs the total number of operational scenarios; omegaiRepresenting the probability of the occurrence of the ith scene; pload,iRepresenting the total power of the load in the ith scene; ploss,iRepresenting the loss power of the network under the ith scene;
s2, considering distribution network participation factors, and establishing an operation layer model taking dynamic reconstruction of a grid structure aiming at promoting maximum power supply capacity under different operation environments into consideration, wherein the operation layer model has the following maximum power supply capacity function:
in the formula, p is a main transformer; q is a feeder line; the psc is the power supply capacity of the active power distribution network; l ispqmaxThe load value of a q-th feeder line connected with a p-th main transformer which can be accessed at the peak load moment of the whole network is obtained;
s3, solving the model of the planning layer and the operation layer
S301, selecting an initial power distribution network expansion planning scheme, and coding a planning layer planning scheme according to a coding mode of a genetic algorithm, namely coding the construction state of a line to be constructed in the planning scheme;
s302, transmitting a planning scheme code of a planning layer as a basic parameter to an operation layer, and optimizing distributed power supply output, demand response quantity, electricity price, total load and network loss of the active power distribution network in each operation scene, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated;
s303, performing integer coding on each switching state of the active power distribution network, and randomly generating an initial particle swarm of a running layer;
s304, optimizing an operation layer by utilizing a particle swarm algorithm to obtain the output, the demand response quantity, the electricity price, the total load and the network loss of the optimal distributed power supply of each current planning scheme, so that the maximum power supply capacity of the active power distribution network under the difference reliability index is calculated and obtained, and is fed back to the planning layer;
s305, calculating the operation cost of each planning scheme by the planning layer, and obtaining the fitness of each planning scheme in the population by combining the equal-year-value construction cost of each scheme;
and S306, judging whether the planning layer reaches a convergence condition, if not, performing cross variation operation on individuals of the planning layer to obtain a new planning scheme population code, returning to the step S302, otherwise, outputting an optimal result.
2. The power distribution network planning method considering dynamic grid reconfiguration and differentiation reliability according to claim 1, wherein: the encoding method and solving process in step S301 are as follows:
s3011, randomly arranging lines to be built to generate chromosome codes;
s3012, dividing the established nodes with contact relations into a plurality of subsets A1,A2,…,AnAnd classifying the subsets, wherein the grade of the subset containing the power supply nodes is 1, otherwise, the grade is 0;
s3013, judging the relation between the load nodes at the two ends of each line and the existing subset according to the coding sequence; suppose that the current line number is h and the nodes at both ends are Ki、KjThe judgment method is as follows:
if Ki、KjNot in the existing subset, then xfl,hSet 1 and establish a new subset an+1＝{Ki+Kj-rank 0;
if Ki、KjOnly one belonging to the existing subset aiIn, then xfl,h1, and Ai＝{Ai+(Ki/Kj) The subset grade is unchanged;
if Ki、KjIn the same subset, xfl,h0, avoiding forming a ring network;
if Ki、KjPresent in different subsets Ai、AjIn, then xfl,h1, and Ai＝{Ai+AjThe combined subset grade is the sum of 2 original subsets;
s3014, when the number of the nodes in all the subsets is equal to the sum of the number of the existing nodes and the number of the nodes to be built, ending the generation process and obtaining a building scheme under the current coding; otherwise, step S3013 is repeated.
3. The power distribution network planning method considering dynamic grid reconfiguration and differentiation reliability according to claim 1, wherein: the planning layer model constraints include:
i) system connectivity constraints: each newly-built load point and distributed power supply access point should form contact with the upper-layer power grid power supply, and an island condition does not occur;
ii) power distribution network wiring mode constraint: when the operation layer optimizes the space truss structure, the states of the interconnection switch and the section switch need to be considered, the scheme obtained by the planning layer allows a loop to appear, and the breaking state of the interconnection switch is determined according to the principles of closed-loop design and open-loop operation during operation;
and iii) the load rate of each feeder line in the planning scheme should meet the requirement of a power distribution network planning guide rule under the current wiring mode, the load requirement of the power distribution network under each dynamic reconfiguration scheme and the constraint of N-1 verification under a fault state.
4. The power distribution network planning method considering dynamic grid reconfiguration and differentiation reliability according to claim 1, wherein: the planning scheme needs to satisfy the following constraints in the operating phase:
i) constraint on differential reliability
The ASAI of the q-th feeder is as follows:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix E is defined as (E)1,E2,…,Eq,…,Em)TIn which EqFor the q-th feeder reliability target, ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TFor an actual feeder reliability index vector, where m is the number of system feeders, the differential reliability constraint is expressed as:
ASAIfeed≥E
ii) overall reliability constraints
The system meets the reliability requirements of different feeder lines and also meets the reliability constraint of the whole network, and selects the ASAI expected value of the system as an index, which is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
iii) line flow constraints of the power distribution system:
in the formula: pGi,t、QGi,t、Li,tAnd Di,tActive output, reactive output, active load and reactive load of the node i in the time period t; u shapei,t、Uj,tIs the voltage amplitude of the nodes i, j during the time period t; gij、BijThe conductance and susceptance of branch ij; thetaij,tIs the phase angle difference of the voltage between the nodes i and j in the time period t;
iv) distribution network node voltage constraints:
Uimin＜Ui,t＜Uimax
in the formula of Ui,tIs the voltage amplitude, U, of node i during time period timinAnd UimaxThe minimum and maximum voltage at node i, respectively;
v) line transmission ampacity constraint:
in the formula IkRepresenting the magnitude of the current on the k-th line, Ik maxThe upper limit of the current-carrying capacity for line transmission;
vi) total cost constraint of electricity purchase before and after demand response:
in the formula, Cf,i、Cg,i、Cp,iRespectively representing peak-valley and mean-time electricity prices of the nodes i; t is tf、tp、tgAnd tsRespectively representing divided peak-to-valley level periods and total periods; l isi,tDemand response quantity of the node i in the time period t;
vii) peak-to-valley electricity price ratio constraint:
k1≤Cf/Cg≤k2
in the formula, k1And k2Respectively representing constraint coefficients; cf、CgRespectively representing the electricity prices in the peak-valley period;
viii) power supplier offer constraints:
in the formula, ksRepresenting a constraint coefficient;
ix) DG output constraint:
PDG,min≤PGi,t≤PDG,max
QDG,min≤QGi,t≤QDG,max
in the formula, PGi,tAnd QGi,tRepresenting DG active and reactive power output of the node i in a time period t; pDG,minAnd PDG,maxRepresenting the DG minimum and maximum active outputs of node i; qDG,minAnd QDG,maxRepresenting the DG minimum and maximum reactive power contribution for node i.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911142501.2A CN110866702A (en) | 2019-11-20 | 2019-11-20 | Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911142501.2A CN110866702A (en) | 2019-11-20 | 2019-11-20 | Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110866702A true CN110866702A (en) | 2020-03-06 |
Family
ID=69655985
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911142501.2A Pending CN110866702A (en) | 2019-11-20 | 2019-11-20 | Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110866702A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784067A (en) * | 2020-07-09 | 2020-10-16 | 北京理工大学 | Two-stage power distribution network planning method based on genetic algorithm |
| CN111864742A (en) * | 2020-07-29 | 2020-10-30 | 国网河北省电力有限公司经济技术研究院 | Active power distribution system extension planning method and device and terminal equipment |
| CN112491037A (en) * | 2020-11-09 | 2021-03-12 | 四川大学 | Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network |
| CN112598149A (en) * | 2020-11-03 | 2021-04-02 | 上海电力大学 | Power distribution network frame construction method based on differential load matching |
| CN112600206A (en) * | 2020-12-24 | 2021-04-02 | 国网北京市电力公司 | Optimization method, system, equipment and medium for improving reliability of coal-to-electricity distribution network |
| CN112765755A (en) * | 2021-01-12 | 2021-05-07 | 国网山东省电力公司经济技术研究院 | Power distribution network planning method and device considering differential reliability requirements |
| WO2021203481A1 (en) * | 2020-04-08 | 2021-10-14 | 清华大学 | Reliability constraint power distribution network planning method based on feeder line corridor |
| CN113705863A (en) * | 2021-08-12 | 2021-11-26 | 南方电网能源发展研究院有限责任公司 | Method and device for determining capacity commissioning decision scheme and computer equipment |
| CN113919760A (en) * | 2021-11-25 | 2022-01-11 | 华北电力大学（保定） | Power distribution network frame planning method based on differentiated reliability requirements of terminal users |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392350A (en) * | 2017-06-08 | 2017-11-24 | 国网宁夏电力公司电力科学研究院 | Power distribution network Expansion Planning comprehensive optimization method containing distributed energy and charging station |
| CN108683186A (en) * | 2018-06-07 | 2018-10-19 | 华北电力大学（保定） | A kind of power distribution network bilayer Expansion Planning method promoted towards power supply capacity |
| CN109217284A (en) * | 2017-07-05 | 2019-01-15 | 南京理工大学 | A kind of reconstruction method of power distribution network based on immune binary particle swarm algorithm |
| CN109242177A (en) * | 2018-08-30 | 2019-01-18 | 国网江西省电力有限公司经济技术研究院 | Active power distribution network planing method |
-
2019
- 2019-11-20 CN CN201911142501.2A patent/CN110866702A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392350A (en) * | 2017-06-08 | 2017-11-24 | 国网宁夏电力公司电力科学研究院 | Power distribution network Expansion Planning comprehensive optimization method containing distributed energy and charging station |
| CN109217284A (en) * | 2017-07-05 | 2019-01-15 | 南京理工大学 | A kind of reconstruction method of power distribution network based on immune binary particle swarm algorithm |
| CN108683186A (en) * | 2018-06-07 | 2018-10-19 | 华北电力大学（保定） | A kind of power distribution network bilayer Expansion Planning method promoted towards power supply capacity |
| CN109242177A (en) * | 2018-08-30 | 2019-01-18 | 国网江西省电力有限公司经济技术研究院 | Active power distribution network planing method |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021203481A1 (en) * | 2020-04-08 | 2021-10-14 | 清华大学 | Reliability constraint power distribution network planning method based on feeder line corridor |
| CN111784067A (en) * | 2020-07-09 | 2020-10-16 | 北京理工大学 | Two-stage power distribution network planning method based on genetic algorithm |
| CN111864742A (en) * | 2020-07-29 | 2020-10-30 | 国网河北省电力有限公司经济技术研究院 | Active power distribution system extension planning method and device and terminal equipment |
| CN112598149A (en) * | 2020-11-03 | 2021-04-02 | 上海电力大学 | Power distribution network frame construction method based on differential load matching |
| CN112598149B (en) * | 2020-11-03 | 2023-10-31 | 上海电力大学 | Power distribution network frame construction method based on differential load matching |
| CN112491037A (en) * | 2020-11-09 | 2021-03-12 | 四川大学 | Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network |
| CN112600206A (en) * | 2020-12-24 | 2021-04-02 | 国网北京市电力公司 | Optimization method, system, equipment and medium for improving reliability of coal-to-electricity distribution network |
| CN112765755A (en) * | 2021-01-12 | 2021-05-07 | 国网山东省电力公司经济技术研究院 | Power distribution network planning method and device considering differential reliability requirements |
| CN113705863A (en) * | 2021-08-12 | 2021-11-26 | 南方电网能源发展研究院有限责任公司 | Method and device for determining capacity commissioning decision scheme and computer equipment |
| CN113919760A (en) * | 2021-11-25 | 2022-01-11 | 华北电力大学（保定） | Power distribution network frame planning method based on differentiated reliability requirements of terminal users |
| CN113919760B (en) * | 2021-11-25 | 2024-05-14 | 华北电力大学（保定） | Power distribution network rack planning method based on differentiated reliability requirements of end users |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110866702A (en) | Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability | |
| Qiu et al. | Decentralized-distributed robust electric power scheduling for multi-microgrid systems | |
| CN107301470B (en) | Double-layer optimization method for power distribution network extension planning and optical storage location and volume fixing | |
| Li et al. | Supply function game based energy management between electric vehicle charging stations and electricity distribution system considering quality of service | |
| Zhang et al. | Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets | |
| CN107316125A (en) | A kind of active distribution network economical operation evaluation method based on economical operation domain | |
| CN108683186B (en) | Power supply capacity improvement-oriented power distribution network double-layer expansion planning method | |
| CN107392395A (en) | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism | |
| CN102915472A (en) | Comprehensive power distribution network optimization planning method based on gene modified chaos genetic algorithm | |
| CN111030188A (en) | Hierarchical control strategy containing distributed and energy storage | |
| Chen et al. | Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties | |
| CN114336636B (en) | Double-layer energy storage configuration method for AC/DC hybrid power distribution network system | |
| CN112381263A (en) | Block chain distributed data storage based multi-microgrid day-ahead robust electric energy transaction method | |
| CN110690702A (en) | Active power distribution network optimal scheduling and operation method considering comprehensive bearing capacity | |
| CN110854891B (en) | Power distribution network pre-disaster resource allocation method and system | |
| CN112381421B (en) | Source network load multi-main-body game planning method considering full dimensionality of power market | |
| He et al. | Coordinated planning of distributed generation and soft open points in active distribution network based on complete information dynamic game | |
| Yang et al. | Applying multiple types of demand response to optimal day‐ahead stochastic scheduling in the distribution network | |
| Wang et al. | Bi-stage operation optimization of active distribution networks with soft open point considering violation risk | |
| CN105226649B (en) | One kind predicting improved provincial power network power generation dispatching optimization method based on bus load | |
| CN112085327B (en) | Multi-layer partition regulation and control method and system for active power distribution network participated by power distributor | |
| CN107392350B (en) | Comprehensive optimization method for power distribution network extension planning containing distributed energy and charging stations | |
| Shang et al. | Distributed V2G dispatching via LSTM network within cloud-edge collaboration framework | |
| Qiu et al. | Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm | |
| CN111654025A (en) | Transmission and distribution integrated coordination scheduling method considering interconnection between distribution networks |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200306 |