CN109583655B - Multi-stage combined extension planning method and system for power transmission and distribution - Google Patents
Multi-stage combined extension planning method and system for power transmission and distribution Download PDFInfo
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Abstract
The invention discloses a multi-stage combined extension planning method and a multi-stage combined extension planning system for power transmission and transmission, wherein the method comprises the following steps: the method comprises the steps of utilizing a fuzzy clustering method to reduce operation data with space-time characteristics to serve as a planning operation scene in a research period, respectively using the total life cycle cost and the cascading failure risk as economic and reliability targets, and using each horizontal year in the research period as a planning stage to establish a multi-target and multi-stage power generation and transmission combined expansion planning model for optimizing. The invention coordinates the economy and the operation reliability of the system in the whole research period, forms a progressive expansion planning mode with the network source structure matched with the load requirement, and can obtain the pareto optimal solution set and the recommended stage expansion planning sequence through optimization.
Description
Technical Field
The invention belongs to the field of power system planning, and particularly relates to a power generation and transmission multi-stage joint expansion planning method and system.
Background
In recent years, with rapid economic development, the power consumption demand is large and is in a continuous increase situation, and by 2018, the peak load of power consumption of a Shandong provincial power grid breaks through 7700 thousands of kilowatts, and the power consumption gap at the peak time ensures that the power consumption demand of partial users cannot be reliably guaranteed. In addition, with the continuous improvement of the power generation permeability of the renewable energy sources and the successive operation of a large-scale direct-current transmission system, the alternating-current system needs to have a grid structure which is reasonable and matched with the load requirement and enough system strength so as to ensure the consumption of the renewable energy sources and the stable operation of the direct-current system, and large-scale cascading failures caused by system weakness are avoided so as to avoid large-scale power failure accidents. Under the background of continuous increase of power demand and continuous penetration of uncertain factors, power grid expansion planning is beneficial to determining a reasonable grid structure and power distribution condition so as to meet the increasing load demand and ensure economic, safe and reliable operation of a power system.
Most of the existing power system expansion planning methods have the following defects:
a. investment in the initial and short periods is planned to be used as an economic target for optimization, continuous change of operation scenes in the middle and long period time is ignored, and reliability is possibly sacrificed, so that the long-term operation of the system is realized, and the fault cost is greatly increased;
b. in the research period, the planning is carried out in a single stage, the extension behavior is only limited on an initial planning point, and the unified and coordinated planning cannot be carried out in the whole research year, so that the total cost is greatly increased;
c. the independent planning of the network and the source is not beneficial to the coordinated development of the network and the source, and the economical efficiency and the reliability of the system can not be ensured on the whole.
In view of the foregoing, a power generation and transmission multi-stage joint expansion planning method and system considering the life cycle cost is needed.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-stage joint expansion planning method and system for power transmission. The method comprises the steps of utilizing a fuzzy clustering method to reduce operation data with space-time characteristics to serve as a planning operation scene in a research period, respectively using the total life cycle cost and the cascading failure risk as economic and reliability targets, and using each horizontal year in the research period as a planning stage to establish a multi-target and multi-stage power generation and transmission combined expansion planning model for optimizing.
In order to achieve the purpose, the invention adopts the following technical scheme:
in one or more embodiments, a power generation and transmission multi-stage joint expansion planning method is disclosed, which includes:
acquiring an initial grid structure of the power system, and determining an alternative amplification power supply access point, an alternative amplification power supply capacity set and an alternative amplification line set;
determining the research age of the medium and long term planning according to the life cycles of the alternative amplification power supply and the alternative amplification circuit;
considering the influence of the main functional area planning on the differential increase of loads in different areas, and carrying out load prediction in a research year on the basis of historical time sequence load data to obtain a time-space scene of the loads in each year;
reducing annual load and renewable energy power generation data within the research period by using a fuzzy clustering algorithm to obtain a planned operation scene;
the method comprises the steps of taking the life cycle cost as an economic target, taking the mean risk value of the cascading failures as a reliability target, taking each horizontal year in a research year as a planning stage, considering all constraints, and establishing a multi-objective and multi-stage optimization model.
And solving the multi-objective optimization model to obtain an optimal solution set, and performing multi-objective decision on the optimal solution set to obtain a satisfactory solution as an extended planning sequence.
Further, determining an alternative amplification power supply access point, an alternative amplification power supply capacity set and an alternative amplification line set, specifically:
selecting a large-scale power plant close to a load center as an alternative access point of the amplification power supply; and determining a set of alternative amplification lines according to the weak part of the grid structure, wherein the alternative amplification lines comprise a newly-added power transmission corridor and an original power transmission corridor.
Further, the lifetime of the device with the minimum lifetime is taken as the research age.
Further, the main body function area comprises an optimization development area, an emphasis development area, a development limiting area and a development forbidding area, and the load growth speeds of the four areas are different.
Furthermore, the bearing capacity factor of the resource environment is considered, the power supply is not considered to be added in the development-forbidden area, and if the alternative power supply access node is in the development-forbidden area, a penalty coefficient of the primary investment cost of the corresponding generator is added.
Further, the life cycle cost includes all one-time investment cost, overhaul and maintenance cost, operation loss cost, operation fault cost and scrap cost in the whole research period;
the mean risk value of cascading failures is specifically as follows: carrying out high-risk cascading failure search, evaluation and screening on each extreme planning operation mode in each year of the research period to obtain an average failure risk value;
the optimization model constraints include: the method comprises the following steps of (1) power flow constraint, decision variable constraint, short-circuit current constraint, short-circuit ratio constraint, system security constraint and connectivity constraint;
further, optimizing and solving the multi-target optimization model by using a fast non-dominated sorting genetic algorithm with an elite strategy to obtain a pareto optimal solution set;
and aiming at the pareto optimal solution set obtained by optimizing, performing multi-objective decision by using an approximate ideal solution ordering method to obtain a satisfactory solution as an extended planning sequence.
Further, two target decisions are carried out on the pareto front edge by using a TOPSIS algorithm, and the weight of an economic target and a reliability target is obtained by using an entropy weight method; the TOPSIS satisfaction solution was taken as the final recommended progressive planning solution.
In one or more embodiments, a power transmission multi-phase joint extension planning system is disclosed, which includes a server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the power transmission multi-phase joint extension planning method described above when executing the program.
A computer-readable storage medium is disclosed in one or more embodiments having a computer program stored thereon which, when executed by a processor, performs the above-described transmission power multi-stage joint extension planning method.
Compared with the prior art, the invention has the beneficial effects that:
the method of scene clustering is utilized to reduce the operation data with the time-space characteristics as the planning operation scene in the research period, so that the calculation scale is greatly reduced, and the correlation among loads and the correlation between the loads and the renewable energy power generation can be reserved to a certain extent;
the whole life cycle cost and the cascading failure risk are respectively used as an economic and reliability target, each horizontal year in a research year is used as a planning stage, a multi-target and multi-stage power generation and transmission combined expansion planning model is established, the economic efficiency and the operation reliability of a system can be coordinated in the whole research year, and a progressive expansion planning mode with a network source structure matched with a load demand is formed.
The invention coordinates the economy and the operation reliability of the system in the whole research period, forms a progressive expansion planning mode with the network source structure matched with the load requirement, and can obtain the pareto optimal solution set and the recommended stage expansion planning sequence through optimization.
The method can be used for obtaining a pareto frontier solution set and a recommended stage type extension programming sequence; compared with single-stage planning, the multi-stage planning can greatly reduce the investment cost while ensuring the reliability.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a schematic diagram of non-synchronized commissioning of equipment during multi-phase planning;
FIG. 2 is a flow chart of a multi-stage joint spread planning method for power transmission according to the present invention;
FIG. 3 is a block diagram of a multi-stage joint expansion planning system for power transmission;
FIG. 4 is a pareto frontier comparison graph of single-stage planning and multi-stage planning of a Shandong power grid, wherein the abscissa represents an expected average risk value of faults, and the ordinate represents a life cycle cost;
fig. 5 is a schematic diagram of a recommended multi-stage planning scheme obtained by simulation of a power grid in the east of shandong.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a power generation and transmission multi-stage joint expansion planning method is disclosed, which includes the following steps:
step 1: the method comprises the steps of obtaining an initial grid structure of the power system, and determining system alternative amplification power supply access points, capacity sets and alternative amplification line sets.
Selecting a large-scale power plant close to a load center as an alternative access point of an amplification power supply, and considering the influence of a differentiated development strategy among regions when selecting the alternative access point of the generator; and selecting a region near a weaker part of the grid structure or a position where the load is rapidly increased to determine a set of alternative amplification lines, wherein the alternative amplification lines comprise the extension lines of the original power transmission corridor besides the newly increased power transmission corridor.
Step 2: the study age of the medium-and-long-term plan is determined according to the life cycle of each device in the alternative augmented device set.
Because the life cycle of each alternative amplification device is different, and the situation of asynchronous operation exists in the multi-stage planning process. A uniform research age is determined for optimization planning. To avoid the situation that the equipment is changed due to insufficient service life in the planning cycle, the equipment service life with the minimum service life cycle is taken as the research age T, namely
T＝min{T1,T2,T3,…,Tx}
In the formula: t isxRepresenting the life cycle of the device x to be amplified.
And step 3: and (4) considering the influence of the main functional area planning on the differential increase of the loads in different areas, and carrying out load prediction in the research year according to historical time sequence load data to obtain the time-space scene of the loads in each year. And reducing the annual load and renewable energy power generation data within the research period by using a fuzzy clustering algorithm to obtain a planned operation scene.
Dividing the territorial space into four areas with optimized development, key development, limited development and forbidden development according to the development mode. The development here refers in particular to large-scale and high-strength industrialized town development, and provincial power grid planning needs to consider the influence of the regional differentiation development strategy.
During the period of research year T, the node load is assumed to increase linearly between years, i.e. the node load increases linearly
PDi,n+1＝μiPDi,n
In the formula: pDi,nAnd PDi,n+1The active load of a certain mode in the nth year and the (n + 1) th year of the node i; mu.siFor the multiple of the increase of the active load, for representing the uncertainty of the load increase, a probability model mu of normal distribution is usedi～N(μi0,σi 2) Generating random numbers obtains the load increase times.
According to the division mode of the main functional area, the loads of the optimized development, the key development, the limited open area and the forbidden development area show a differentiated growth trend. The optimized development and key development areas have stronger economic foundation and huge industrial and scientific development prospects, have stronger attraction to human mouth and have higher load increase speed. As the development-restricted areas and the development-prohibited areas are mostly agricultural product main production areas or ecological functional areas, the load increase is small. The annual increase times of the loads of the four regions are respectively assumed to be mui,1、μi,2、μi,3And mui,4Then there is
μi,2＞μi,1＞＞μi,3＞μi,4≈1
In the step 3, an 8760 × N dimensional operation scene sample matrix is formed by considering spatial distribution according to annual time series prediction data of renewable energy output and load. Respectively clustering all scene samples of each year by using a fuzzy C-means clustering algorithm, and reducing to obtain NsAnd (4) approximately calculating the operation loss cost and the fault cost of the planned power grid by using the clustering centers as typical operation scenes. The method has the advantages that the operation data with the time-space characteristic is utilized to carry out scene clustering, uncertain operation scenes are converted into a few confirmed operation scenes, huge calculation amount of all the operation scenes in a traversal research period can be avoided, meanwhile, the correlation among space loads and between the loads and renewable energy power generation is reserved, and the planned operation scenes are objective and reasonable.
And 4, step 4: the method comprises the steps of taking the life cycle cost as an economic target, taking the mean risk value of the cascading failures as a reliability target, taking each horizontal year in a research year as a planning stage, considering all constraints, and establishing a multi-objective and multi-stage optimization model. And (3) optimizing and solving the multi-objective optimization model by using a fast non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a pareto optimal solution set.
In step 4, the life cycle cost of the amplification equipment includes a first investment cost in the construction stage, an equipment overhaul and maintenance cost, a system loss cost, direct and indirect fault costs in the operation stage, and an equipment residual value in the scrapping stage.
The primary investment cost is mainly the cost of purchasing and constructing equipment for amplifying lines and generators. With each horizontal year within the study period as the planning phase, the augmentation behavior may occur in any year within the study period, and it is assumed that the annual investment behavior occurs at the beginning of the year. Current value f of one-time investment cost in the t yearCI,tComprises the following steps:
in the formula: (P/F, r, t) ═ 1+ r)-tThe coefficient of the recurrence value represents that the final value of the t year is converted into the present value, and r is the conversion rate; n is a radical ofL、NgRespectively, the number of alternative amplification lines and generators; c. CkAverage cost of k unit length lines for alternative lines; lkIs the length of line k; c. CgThe cost of the alternative generator g; n isk,t、ng,tAnd decision variables of the line k and the generator g in the t year are respectively represented, if the decision variables are 1, construction is represented, and if the decision variables are 0, construction is not represented.
The overhaul and maintenance cost is closely related to the number of newly added equipment, can be measured and calculated according to the proportion of the total investment of the equipment once, and the expense generated in the operation stage is assumed to occur at the end of each year. Current value f of t-year maintenance costCM,tComprises the following steps:
in the formula: λ is the maintenance cost scaling factor.
The operation loss cost is mainly the network loss cost of the system, and the annual network loss cost can be approximately solved by using the clustering center after each year of 8760h scene clustering as a planning scene. Current operating loss cost f in the t yearCO,tComprises the following steps:
in the formula: c. CpIs unit electricity price; n is a radical ofsThe number of the operation scenes, namely the number of the scene clustering centers; t ist,sThe running time of the scene s in the t year; omegatPlanning all line sets of the system for the t year; i isij,t,sThe current value of the line ij in a scene s of the t year; rijIs the resistance value of line ij.
The operation failure cost comprises direct failure cost and indirect failure cost, wherein the direct failure cost is the power shortage cost in a failure state, and is approximately obtained according to the expected electric quantity shortage value when an expected accident occurs in an operation scene; the indirect fault cost includes compensation cost, quantified cost which has adverse effect on the society and the like, and can be obtained by setting a proportionality coefficient for the direct fault cost. For a large power grid, the safety of the system N-1 or even N-2 needs to be guaranteed in the planning stage, the load loss is difficult to cause by low-weight faults, and the large power failure accidents are mostly caused by cascading faults, so that high-risk cascading fault searching is performed on each operation scene, and a high-risk cascading fault set is constructed to calculate the operation fault cost. Current operating fault cost value f in the t yearCF,tComprises the following steps:
in the formula: is an indirect fault cost scaling factor; fsAn expected accident set obtained by searching; p is a radical ofmThe probability of the cascading failure m is the product of the probabilities of all levels of failures; pshed,m,t,sThe minimum load shedding amount when the scene s has a fault m in the t year.
The equipment residual value is the residual value of the equipment at the end of the research life and needs to be added into the life cycle cost by a negative value. Calculating the residual value f of the amplification equipment at the end of the research age limit by adopting a linear depreciation methodSVComprises the following steps:
in the formula: n is a radical ofxThe number of newly added devices; f. ofCIxIs the primary investment cost of equipment x; dxIs the depreciation rate; t isxFor equipment x from commissioning to research yearThe time put into operation at the end of the time limit.
In step 4, each horizontal year within the research period is taken as a planning stage, and the equipment commissioning time is different, as shown in fig. 1. With the full life cycle cost as the economic objective of the optimization model:
taking the mean loss load risk value of cascading failures under the extreme operating scene within the research period as a reliability target:
in the formula: n is a radical ofs' is the number of extreme operating scenarios in a single horizontal year; n is a radical ofcThe high-risk cascading failure number searched under a single scene.
In step 4, the following constraints exist in the optimization model:
(1) flow restraint
In the formula: delta Pi,t,sAnd Δ Qi,t,sRespectively the active power and the reactive power of the node i in the scene s of the t year; u shapei,t,sThe voltage modulus of the node i under the scene s of the t year; thetaij,t,sThe power angle difference between the nodes i and j is obtained; G. b denotes the system node conductance and susceptance matrices, respectively.
(2) Decision variable constraints
The decision variable of the equipment to be amplified should be 1 or 0 to represent whether the equipment is put into operation or not, and the decision variable between stages should be satisfied
In the formula: n isk,t、ng,tRespectively representing decision variables of the alternative extended line k and the generator g in the t year; omega+、Г+Respectively representing the newly added line and the newly added generator alternative set.
(3) Safety restraint
Umin≤Ui,t,s≤Umax
In the formula: u shapemin、UmaxAnd Pline,maxRespectively representing node voltage and line active power flow limit values; and omega is the whole line set of the system.
(4) Short circuit current confinement
Zii≥Zii,min
In the formula: ziiIs the self-impedance of node i; zii,minTo satisfy the minimum self-impedance of the node i short circuit current constraint.
(5) Short circuit ratio constraint
For a direct current fed receiving end power grid, short circuit ratio index constraint representing alternating current system strength needs to be met.
In the formula: kMISCR,dAnd KMISCR,minRespectively representing the multi-feed short-circuit ratio of the direct current d and the minimum multi-feed short-circuit ratio required by system operation; d denotes a dc system set.
(6) Connectivity constraints
In the step 4, the multi-objective optimization model is solved by using a fast non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy, and the equipment to be put into operation and the operation age of the equipment are controlled in a chromosome specific coding mode to obtain a pareto optimal solution set.
And 5: aiming at the pareto optimal solution set obtained by optimization, a multi-objective decision is made by utilizing an approximate ideal solution ordering method (TOPSIS) to obtain a satisfactory solution as an extended planning sequence.
In step 5, two target decisions are made on the pareto frontier by using the TOPSIS algorithm, and the weights of the economic and reliability targets are obtained by using an entropy weight method. The TOPSIS satisfaction solution is taken as the final recommended progressive planning solution.
Simulation verification is performed on 500kV and above voltage class systems of the Shandong power grid, and the flow of the power generation and transmission multi-stage joint expansion planning method is described below.
S1: the method comprises the steps of obtaining an initial grid structure of the power system, and determining system alternative amplification power supply access points, capacity sets and alternative amplification line sets.
Firstly, acquiring the grid structure condition of 500kV and above voltage level of Shandong provincial power grid in 2018, determining 12 alternative access points of the amplification power supply according to factors such as a pithead power plant, a load center and the like, wherein each access point has three optional capacity units of 300, 600 and 1000 MW; and determining 36 alternative amplification lines according to the weakness degree of the grid structure, the load acceleration condition and the power generation distribution condition of the renewable energy sources, wherein 32 500kV alternative lines and 4 1000kV alternative extra-high voltage lines are selected.
S2: according to the life cycle of each device in the alternative amplification device set, the minimum device is selected as the research age, and the research period of the system is 10 years.
S3: and (4) considering the influence of the main functional area planning on the differential increase of the loads in different areas, and carrying out load prediction in the research year according to historical time sequence load data to obtain the time-space scene of the loads in each year. And reducing the annual load and renewable energy power generation data within the research period by using a fuzzy clustering algorithm to obtain a planned operation scene.
In the process of load prediction in each year within the research period, the annual load growth multiple of areas with emphasis on development, optimized development, development limitation and development prohibition is expected to be 1.06, 1.05, 1.02 and 1.01 respectively and obey normal distribution. And forecasting according to the historical load space-time data to obtain the load space-time data within 10 years of the research period. Combining the renewable energy power generation prediction data to form 10 initial scene data matrixes of 8760 x 66 dimensions.
And (3) reducing scene data of 8760h each year by using a fuzzy clustering algorithm, and obtaining 15 clustering centers so as to obtain 150 planned operation scenes and corresponding operation time in the research period. The data with the time-space characteristics are utilized to perform scene clustering, uncertain operation scenes are converted into a few confirmed operation scenes, the huge calculation amount of all the operation scenes in a traversal research period can be avoided, and meanwhile, the correlation among space loads and the correlation between the loads and renewable energy power generation are reserved.
S4: the method comprises the steps of taking the life cycle cost as an economic target, taking the mean risk value of the cascading failures as a reliability target, taking each horizontal year in a research year as a planning stage, considering all constraints, and establishing a multi-objective and multi-stage optimization model. And (3) optimizing and solving the multi-objective optimization model by using a fast non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a pareto optimal solution set.
Since the equipment commissioning time varies within the study period, each of the life cycle costs needs to be calculated in stages. By determining the amplification equipment and the operation year thereof, the system grid structure of each year can be determined, so that the one-time investment cost and the overhaul and maintenance cost of each year can be obtained; the grid structure and the corresponding load power generation scene in the current year form each operation mode in the current year together, so that the operation loss cost and the fault cost in the current year are obtained; because the life cycle and the operation time of each device are different, for the device which does not reach the life cycle at the end of the research age, the residual value of the device at the end of the research age can be calculated according to the depreciation rate and correspondingly deducted from the total cost. The mean risk of cascading failures can be obtained by searching, evaluating and screening high-risk cascading failures in various operation modes in a research period.
The multi-objective optimization model is solved by using an NSGA-II algorithm, the equipment to be put into operation and the operation time limit of the equipment are controlled by a chromosome specific coding mode, the population scale of the NSGA-II algorithm is 100, the iteration frequency is 50, the cross probability is 0.9, and the variation probability is 0.1. Iterative optimization can obtain a pareto optimal solution set which coordinates economy and reliability. In order to verify the feasibility and the effectiveness of the multi-stage planning, single-stage planning is introduced for comparison. The single-stage planning means that the amplification equipment is only put into operation once at the initial stage of planning, and the framework structure is not adjusted any more within the research period. A comparison of pareto fronts for single-and multi-phase plans for the Shandong grid is shown in FIG. 4.
S5: aiming at the pareto optimal solution set obtained by optimization, a multi-objective decision is made by utilizing an approximate ideal solution ordering method (TOPSIS) to obtain a satisfactory solution as an extended planning sequence.
Two target decisions are made on the pareto frontier obtained by the multi-stage planning, so that a TOPSIS satisfactory solution is obtained as a recommended solution, the economical efficiency and reliability targets of the recommended solution are marked and shown in figure 4, and a specific planning scheme is shown in figure 5.
Example two
In one or more embodiments, a power transmission multi-stage joint extension planning system is disclosed, which includes a server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement a power transmission multi-stage joint extension planning method according to an embodiment.
EXAMPLE III
In one or more embodiments, a computer-readable storage medium is disclosed, on which a computer program is stored, which, when executed by a processor, performs a method of multi-phase joint extension planning for power transmission according to one embodiment.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. A multi-stage joint expansion planning method for power transmission and transmission is characterized by comprising the following steps:
acquiring an initial grid structure of the power system, and determining an alternative amplification power supply access point, an alternative amplification power supply capacity set and an alternative amplification line set;
determining the research age of medium and long term planning according to the life cycles of the alternative amplification power supply and the alternative amplification circuit;
considering the influence of the main functional area planning on the differential increase of loads in different areas, and carrying out load prediction in a research year on the basis of historical time sequence load data to obtain a time-space scene of the loads in each year;
reducing annual load and renewable energy power generation data within the research period by using a fuzzy clustering algorithm to obtain a planned operation scene;
taking the total life cycle cost as an economic target, taking the mean risk value of cascading failures as a reliability target, taking each horizontal year in a research year as a planning stage, considering all constraints, and establishing a multi-target and multi-stage optimization model;
solving a multi-target optimization model to obtain an optimal solution set, carrying out multi-target decision on the optimal solution set to obtain a satisfactory solution as an extended planning sequence, namely, optimizing and solving the multi-target optimization model by utilizing a fast non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a pareto optimal solution set, carrying out multi-target decision by utilizing an approximate ideal solution sorting method aiming at the optimized pareto optimal solution set to obtain the satisfactory solution as the extended planning sequence;
the multi-stage combined extension planning method for power generation and transmission takes each horizontal year in a research year as a planning stage, the equipment operation time is different, and the total life cycle cost is taken as an economic target of an optimization model:
wherein T is the research age, fCI,tThe current value of one investment cost in the t year, fCM,tThe current value of the maintenance cost is checked and repaired for the t year,fCO,tis the current value of the running loss cost in the t year, fCF,tThe current value of the operating failure cost in the t year, fSVResidual value of amplification equipment for end of research age
Taking the mean loss load risk value of cascading failures under the extreme operating scene within the research period as a reliability target:
wherein N iss' number of extreme operating scenarios in a single horizontal year, NcHigh risk number of cascading failures, p, for searching in a single scenemProbability of occurrence of cascading failure m, Pshed,m,t,sThe minimum load shedding amount when the scene s has a fault m in the t year.
2. The power generation and transmission multi-stage joint expansion planning method according to claim 1, wherein determining alternative augmented power source access points, an alternative augmented power source capacity set, and an alternative augmented line set specifically comprises:
selecting a large-scale power plant close to a load center as an alternative access point of the amplification power supply; and determining a set of alternative amplification lines according to the weak part of the grid structure, wherein the alternative amplification lines comprise a newly-added power transmission corridor and an original power transmission corridor.
3. A multi-stage joint extension planning method for power transmission according to claim 1, wherein the equipment life with the minimum life cycle is taken as the research age.
4. The multi-stage joint expansion planning method for power generation and transmission according to claim 1, wherein the main functional areas include areas for optimization development, emphasis development, development limitation and development prohibition, and the four areas have different load growth speeds.
5. The multi-stage joint expansion planning method for power generation and transmission according to claim 1, wherein a penalty factor of one-time investment cost of a corresponding generator is increased if an alternative power access node is in a development-prohibited area without considering the load-bearing capacity factor of a resource environment and without considering the increase of power in the development-prohibited area.
6. The multi-stage joint extension planning method for power generation and transmission according to claim 1, wherein the life cycle cost includes all of the one-time investment cost, the overhaul maintenance cost, the operation loss cost, the operation failure cost and the scrapping cost in the whole research period;
the mean risk value of cascading failures is specifically as follows: carrying out high-risk cascading failure search, evaluation and screening on each extreme planning operation mode in each year of the research period to obtain an average failure risk value;
the optimization model constraints include: the method comprises the following steps of power flow constraint, decision variable constraint, short-circuit current constraint, short-circuit ratio constraint, system security constraint and connectivity constraint.
7. The multi-stage joint expansion planning method for power transmission and transmission as claimed in claim 1, wherein two target decisions are made on pareto frontier by means of TOPSIS algorithm, and weights of economic and reliability targets are obtained by means of entropy weight method; the TOPSIS satisfaction solution was taken as the final recommended progressive planning solution.
8. A power generation and transmission multi-stage joint extension planning system comprising a server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the power generation and transmission multi-stage joint extension planning method of any one of claims 1-7 when executing the program.
9. A computer-readable storage medium, characterized in that a computer program is stored thereon which, when being executed by a processor, performs the method of multi-phase joint spread planning for power transmission according to any of claims 1-7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101179196A (en) * | 2007-11-15 | 2008-05-14 | 上海交通大学 | Determined 2-layered planning model based transmission network planning method |
| JP2010282546A (en) * | 2009-06-08 | 2010-12-16 | Sekisui Chem Co Ltd | System for supporting housing-related business for estimating light and fuel expenses of housing according to various conditions, program to be used for the same, computer-readable recording medium storing the program, and method of presenting estimated light and fuel expenses in housing-related business |
| CN103514570A (en) * | 2013-08-14 | 2014-01-15 | 国家电网公司 | Expansion planning comprehensive optimization method of power distribution network with distributed power supply |
| CN106385025A (en) * | 2016-10-08 | 2017-02-08 | 四川大学 | Distributed power supply and contact line multistage coordinated planning method |
| CN108229744A (en) * | 2018-01-05 | 2018-06-29 | 国网陕西省电力公司经济技术研究院 | A kind of design method of substation planning optimization analytic modell analytical model |
-
2018
- 2018-12-04 CN CN201811473989.2A patent/CN109583655B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101179196A (en) * | 2007-11-15 | 2008-05-14 | 上海交通大学 | Determined 2-layered planning model based transmission network planning method |
| JP2010282546A (en) * | 2009-06-08 | 2010-12-16 | Sekisui Chem Co Ltd | System for supporting housing-related business for estimating light and fuel expenses of housing according to various conditions, program to be used for the same, computer-readable recording medium storing the program, and method of presenting estimated light and fuel expenses in housing-related business |
| CN103514570A (en) * | 2013-08-14 | 2014-01-15 | 国家电网公司 | Expansion planning comprehensive optimization method of power distribution network with distributed power supply |
| CN106385025A (en) * | 2016-10-08 | 2017-02-08 | 四川大学 | Distributed power supply and contact line multistage coordinated planning method |
| CN108229744A (en) * | 2018-01-05 | 2018-06-29 | 国网陕西省电力公司经济技术研究院 | A kind of design method of substation planning optimization analytic modell analytical model |
Non-Patent Citations (1)
| Title |
|---|
| 考虑全寿命周期成本的输电网多目标规划;柳璐 等;《中国电机工程学报》;20120805;第32卷(第22期);第46-54页 * |
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