CN116995719A - Active power distribution network double-layer expansion planning method considering wind-solar-load uncertainty - Google Patents

Active power distribution network double-layer expansion planning method considering wind-solar-load uncertainty Download PDF

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CN116995719A
CN116995719A CN202310733169.7A CN202310733169A CN116995719A CN 116995719 A CN116995719 A CN 116995719A CN 202310733169 A CN202310733169 A CN 202310733169A CN 116995719 A CN116995719 A CN 116995719A
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distribution network
wind
power distribution
layer
active power
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陈颂
袁明瀚
张华�
纪坤华
刘扬洋
唐啸
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State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

Abstract

The invention relates to a double-layer expansion planning method of an active power distribution network, which takes wind-light load uncertainty into account and comprises the following steps of: modeling loads and distributed power sources in the power distribution network by considering uncertainty of wind and light loads to obtain wind turbine sets, photovoltaic power generation and load output models; based on the output model, obtaining a typical daily scene of the distributed power supply and the load through Latin hypercube sampling and a backward reduction method; and establishing a double-layer planning model of the active power distribution network, wherein the upper-layer model takes the lowest annual comprehensive cost as an optimization target, the lower-layer model takes the lowest annual operation cost and the lowest node voltage offset as the optimization target, the double-layer planning model is converted into a multi-target optimization problem after being subjected to upper-layer and lower-layer associated modeling and second-order cone relaxation, and a normalization normal constraint method is adopted for solving, so that an active power distribution network expansion planning scheme is obtained. Compared with the prior art, the invention has the advantages of high system electric energy quality, fitting actual working conditions and the like.

Description

Active power distribution network double-layer expansion planning method considering wind-solar-load uncertainty
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a double-layer expansion planning method of an active power distribution network, which takes wind-light load uncertainty into account.
Background
With intermittent renewable energy source grid connection such as wind power, photovoltaic and the like, the power distribution network is changed from a passive unidirectional radial power supply network to an active network which actively controls power to flow bidirectionally, namely an active power distribution network. The traditional power distribution network planning scheme cannot meet the increasingly high-proportion distributed power supply access requirements, so that the power distribution network needs to be expanded and planned, and the expanded grid planning and equipment element configuration scheme are determined under the condition that the increasingly high-proportion load requirements are met, so that the requirements of improving the consumption rate of distributed renewable energy sources, reducing investment, increasing asset utilization rate, improving power supply quality and reliability and the like are met. On a typical day scene construction considering distributed power and load uncertainty, traditional scene reduction cannot meet the requirements in accuracy and effectiveness. The method has high requirements on the initial clustering center, as in a document CUDA technology-based massive power load curve clustering algorithm, a graph processing unit is adopted to process the distance between the partition data and the clustering center; the document 'wind/light classical scene set generation method and application' uses a K-center point clustering method for scene reduction, and the method has larger calculation amount. On a power distribution network planning model, a great deal of researches are carried out on the power distribution network planning model by students at home and abroad at present. The literature of distributed photovoltaic grid-connected planning considering time sequence and correlation in an active power distribution network establishes a double-layer model of an upper planning layer and a lower operation layer, but the solving precision is still to be improved. Therefore, the precision, timeliness and other aspects of the power distribution network planning method are still to be further improved.
Disclosure of Invention
The invention aims to provide a double-layer expansion planning method of an active power distribution network, which takes the uncertainty of wind, light and load into account, so that the output of distributed energy sources such as wind power, photovoltaic and the like is more matched with the load demand, the solving precision is improved, and the higher system power quality is ensured.
The aim of the invention can be achieved by the following technical scheme:
a double-layer expansion planning method of an active power distribution network considering wind-light load uncertainty comprises the following steps:
step 1), modeling loads and distributed power sources in a power distribution network by considering uncertainty of wind and light loads to obtain wind turbine sets, photovoltaic power generation and load output models;
step 2) obtaining a typical daily scene of a distributed power supply and load through Latin hypercube sampling and a backward reduction method based on an output model;
and 3) establishing a double-layer planning model of the active power distribution network, wherein the upper layer model takes the lowest annual comprehensive cost as an optimization target, the lower layer model takes the lowest annual operation cost and the lowest node voltage offset as an optimization target, the double-layer planning model is converted into a multi-target optimization problem after being subjected to upper and lower layer associated modeling and second-order cone relaxation, and a normalization normal constraint method is adopted for solving, so that an active power distribution network expansion planning scheme is obtained.
The modeling of the load and the distributed power supply in the power distribution network by considering the uncertainty of the wind and light load specifically comprises the following steps: considering uncertainty of output power of the wind turbine caused by uncertainty of wind speed, describing wind speed characteristics by using Weibull distribution, and establishing a wind turbine output model according to the relation between wind speed and wind power output; and (3) considering uncertainty of the solar radiation intensity of the horizontal plane, describing the intensity of the solar radiation of the horizontal plane by utilizing a Beta distribution function, and establishing a photovoltaic generator set output model according to the relationship between the solar radiation of the horizontal plane and the output of the photovoltaic generator set.
Wind speed characteristics are described as follows using the weibull distribution:
wherein: v is wind speed, r is a shape parameter, and c is a scale parameter;
the probability calculation formula of the wind speed v is as follows:
wherein: p (v) is the occurrence probability of the wind speed v; v a And v b The wind speed v is respectively the upper limit value and the lower limit value;
let v t For the actual wind speed at time t, the formula is:
v t ＝(v a,t +v b,t )/2
the wind turbine output model is expressed as:
wherein: g w,t Generating power for the wind turbine at the time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v rated Is the rated wind speed; g R And rated output power of the wind turbine generator.
The intensity of the horizontal plane solar radiation is described by a Beta distribution function as:
wherein: alpha and Beta are shape parameters of Beta distribution functions; θ is the intensity of the photovoltaic radiation;
wherein the mean μ and standard deviation σ of α, β are obtained using simulations of historical data:
the probability of occurrence of the photovoltaic radiation θ is:
wherein: θ c 、θ d The upper limit and the lower limit of the photovoltaic radiation theta are respectively set;
the photovoltaic power generation output model is as follows:
g PV,t ＝η PV S PV θ t
wherein: η (eta) PV Power generation efficiency for PV; s is S PV The total area of the photovoltaic panel that is the PV; θ t The solar photovoltaic radiation intensity at time t.
The step 2) specifically comprises the following steps:
step 2-1) generating random scenes by Latin hypercube sampling based on an output model, fitting an hourly wind-light load distribution function, and generating a large number of scenes by inverse function operation;
step 2-2) constructing a scene reduction model, and reducing and merging the scenes by using a backward reduction method to reduce the number of similar scenes;
step 2-3) obtaining a typical daily scene of the distributed power supply and the load by adopting a typical daily scene fitting method based on normal distribution.
The annual comprehensive cost comprises annual investment cost and annual operation cost.
The objective function of the upper layer model of the active power distribution network double-layer planning model is expressed as follows:
minC＝C in +C op
C in ＝C line +C DG +C ESS
annual investment cost C in Including the cost C of the newly built line line Investment cost C in DG years DG Investment cost C for energy storage year ESS The method comprises the steps of carrying out a first treatment on the surface of the Annual running cost C op Including network loss cost C loss Cost of DG operationEnergy storage operation comprehensive cost->Higher-level power grid electricity purchasing cost C grid Cost of discarding light and wind>
The objective function of the lower model of the active power distribution network double-layer planning model is expressed as follows:
min(C op ,ΔU)
wherein C is op For annual operating costs, ΔU is the node voltage offset.
The active power distribution network double-layer planning model meets line investment selection constraint, DG installation quantity constraint, radial communication state constraint, tide constraint, operation constraint, photovoltaic capacity constraint and energy storage constraint.
The solving by adopting the normalization normal constraint method comprises the following specific steps: and sequentially solving each single-target optimization problem to obtain respective optimal solutions, normalizing the original multi-target optimization problem to obtain normalized subspaces, sequentially judging whether the solution of each single-target problem is better than the respective optimal solution, outputting the pareto optimal solution if yes, obtaining an active power distribution network expansion planning scheme, and if not, solving the points on the other pareto front distribution, and iterating.
Compared with the prior art, the invention has the following beneficial effects:
(1) The active power distribution network double-layer expansion planning method considering wind-light load uncertainty, which is designed by the invention, can enable the output of distributed energy sources such as wind power, photovoltaic and the like to be more matched with the load demand, and has the advantages of high solving precision, better economical efficiency and higher system power quality according to the output scheme.
(2) The typical daily scene analysis method based on Latin hypercube sampling and backward reduction method provided by the invention is superior to the traditional scene analysis method, and the obtained scene can better reflect the original scene and part of extreme scenes.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of an exemplary daily scene generation based on Latin hypercube sampling and backward reduction;
FIG. 3 is a representative day scene analysis result in one embodiment;
FIG. 4 is a flowchart of a multi-objective optimization model solution based on a normalized normal constraint method.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
A double-layer expansion planning method of an active power distribution network considering wind-light load uncertainty comprises the following steps:
step 1) modeling loads and distributed power sources in a power distribution network by considering uncertainty of wind and light loads to obtain wind turbine sets, photovoltaic power generation and load output models.
Because the uncertainty of the wind speed can cause the uncertainty of the output power of the wind turbine, the wind speed characteristics are described by using Weibull distribution, and a wind turbine output model is built according to the relation between the wind speed and the wind power output.
Wind speed characteristics are described as follows using the weibull distribution:
wherein: v is wind speed, r is a shape parameter, and c is a scale parameter;
the probability calculation formula of the wind speed v is as follows:
wherein: p (v) is the occurrence probability of the wind speed v; v a And v b The wind speed v is respectively the upper limit value and the lower limit value;
let v t For the actual wind speed at time t, the formula is:
v t ＝(v a,t +v b,t )/2
the wind turbine output model is expressed as:
wherein: g w,t Generating power for the wind turbine at the time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v rated Is the rated wind speed; g R And rated output power of the wind turbine generator.
The output level of the photovoltaic generator set mainly depends on the intensity of the horizontal plane solar radiation, so that the Beta distribution function is used for describing the intensity of the horizontal plane solar radiation.
In this embodiment, referring to literature "power distribution network planning considering DG timing characteristics and EV space-time characteristics", the intensity of solar radiation in the horizontal plane is described by a Beta distribution function as follows:
wherein: alpha and Beta are shape parameters of Beta distribution functions; θ is the intensity of the photovoltaic radiation;
wherein the mean μ and standard deviation σ of α, β are obtained using simulations of historical data:
the probability of occurrence of the photovoltaic radiation θ is:
wherein: θ c 、θ d The upper limit and the lower limit of the photovoltaic radiation theta are respectively set;
the photovoltaic power generation output model is as follows:
g PV,t ＝η PV S PV θ t
wherein: η (eta) PV Power generation efficiency for PV; s is S PV The total area of the photovoltaic panel that is the PV; θ t The solar photovoltaic radiation intensity at time t.
For uncertainty of the load, a typical load daily fitting method based on normal distribution is adopted, the frequency of the load at the same moment every day is used as probability, and a maximum likelihood estimation method is used for fitting into a normal distribution curve.
Step 2) obtaining a typical daily scene of the distributed power supply and load through Latin hypercube sampling and a backward reduction method based on the output model.
As shown in fig. 2, step 2) includes the steps of:
step 2-1) generating random scenes by Latin hypercube sampling based on an output model, fitting an hourly wind-light load distribution function, and generating a large number of scenes by inverse function operation;
step 2-2) constructing a scene reduction model, performing scene reduction and merging by using a backward reduction method, and reducing the number of similar scenes to obtain a scene set capable of representing the characteristics of original scene data to a greater extent;
step 2-3) obtaining a typical daily scene of the distributed power supply and the load by adopting a typical daily scene fitting method based on normal distribution.
The fitting method in step 2-3) is specifically referred to "typical load day fitting method based on normal distribution" proposed by Han Hongzhi et al, and is not described herein for avoiding ambiguity of the object of the present invention.
As shown in fig. 3, the result of typical daily scene analysis in one embodiment is shown, and according to fig. 3, it can be seen that the method can obtain a wind-solar load output curve which retains data diversity and is effectively fitted.
And 3) establishing a double-layer planning model of the active power distribution network, wherein the upper layer model takes the lowest annual comprehensive cost as an optimization target, the lower layer model takes the lowest annual operation cost and the lowest node voltage offset as an optimization target, the double-layer planning model is converted into a multi-target optimization problem after being subjected to upper and lower layer associated modeling and second-order cone relaxation, and a normalization normal constraint method is adopted for solving, so that an active power distribution network expansion planning scheme is obtained.
The upper model takes the annual comprehensive cost Cminimum as an objective function and comprises annual investment cost C in Cost of annual operation C op Expressed as:
minC＝C in +C op
C in ＝C line +C DG +C ESS
annual investment cost C in Including the cost C of the newly built line line Investment cost C in DG years DG Investment cost C for energy storage year ESS The method comprises the steps of carrying out a first treatment on the surface of the Annual running cost C op Including network loss cost C loss Cost of DG operationEnergy storage operation comprehensive cost->Higher-level power grid electricity purchasing cost C grid Cost of discarding light and wind>
The objective function of the underlying model is expressed as:
min(C op ,ΔU)
wherein C is op For annual operating costs, ΔU is the node voltage offset.
The active power distribution network double-layer planning model meets the following constraint:
(1) Line investment selection constraint
In omega line As a candidate line set, Φ line For the original set of lines,and (5) the investment type is a line candidate.
(2) DG installation quantity constraint
The number of accesses of the distributed photovoltaic units cannot exceed the upper limit and the lower limit:
wherein, the liquid crystal display device comprises a liquid crystal display device,the number of the distributed photovoltaic units to be connected is the number of the distributed photovoltaic units to be connected.
(3) Radial communication-like confinement
In the planning process, the power distribution network needs to meet connectivity and radial conditions:
n＝m+1
wherein: n and m are the number of system nodes and the number of branches, respectively.
(4) Tidal current constraint
Wherein: r is (r) ij And x ij The resistance and reactance of branch ij; p (P) i And Q i Respectively the sum of active power and reactive power injected by the node i; p (P) ij And Q ij Active/reactive power flowing from node i to node j on the branch;and->Active/reactive power injected by the distributed power supply on the node i respectively; />And->Respectively, hairActive/reactive power of the motor; />Active power injected for energy storage; />And->Active/reactive power consumed by the load, respectively.
(5) Operational constraints
Wherein: u (U) max And U min Respectively a maximum allowable voltage and a minimum allowable voltage of the system; i max The maximum allowable branch current value of the system.
(6) Photovoltaic capacity constraint
Wherein:the capacity of the node i for installing photovoltaic; />Is the photovoltaic capacity maximum installed at node i.
(7) Energy storage constraint
Wherein:and->Respectively charging and discharging the storage battery; />And->Respectively the maximum charge and discharge quantity of the storage battery; />And->Respectively charging and discharging marks of the storage battery; />And->Respectively charging and discharging active power of the storage battery; />The maximum value of the charge and discharge power of the storage battery.
Wherein the constraints (1) - (4) are constraints of the upper model, and the constraints (5) - (7) are constraints of the lower model.
When solving the double-layer planning model, the method of upper and lower layer association modeling is firstly used for converting the double-layer planning model into a mixed integer second order cone planning model, and the specific conversion process is described in the document of the double-layer planning model of an alternating current and direct current mixed power distribution network based on the second order cone planning and NNC method and the solving method thereof, and is not described in detail herein. The model with second order cone relaxation is essentially a multi-objective optimization problem, a normalization normal constraint method is adopted to solve the multi-objective optimization problem, as shown in fig. 4, each single-objective optimization problem is solved in sequence to obtain respective optimal solutions, then normalization is carried out on the original multi-objective optimization problem to obtain a normalization subspace, whether the solutions of each single-objective problem are better than the respective optimal solutions is judged in sequence, if yes, the pareto optimal solutions are output to obtain an active power distribution network expansion planning scheme, if not, points on other pareto front distribution are solved, iteration is carried out until the pareto optimal solutions of the whole problem are obtained.
In order to verify the validity of the extended planning model, three schemes are set for comparison in this embodiment:
scheme one: the distributed power supply planning is not considered, and the energy storage planning is considered;
scheme II: the energy storage planning is not considered, and the distributed power supply planning is considered;
scheme III: and meanwhile, distributed power supply planning and energy storage planning are considered.
And the validity of the present invention was verified using matlab platform, the results are shown in tables 1 and 2. Compared with the scheme one without DG planning, the economic efficiency of the scheme three using the proposed comprehensive planning model is superior to that of the scheme one, and the main appearance is that the electricity purchasing cost is reduced by 68 percent, and the grid loss cost is reduced by 45 percent, because DG provides active power; in addition, the average voltage offset for scheme three is also much lower than for scheme one. The total cost in the second scheme is lower than that in the third scheme, but the purchase cost and the network loss cost are higher, the PV income is lower, and the node voltage offset of the second scheme is higher than that of the third scheme, so that the total system income is lower than that of the third scheme.
TABLE 1
Cost of each item Scheme one Scheme II Scheme III
Cost of new line/10 6 Meta 4.01 4.01 4.01
Annual electricity purchase cost/10 6 Meta 22.75 8.21 7.17
ESS cost/10 6 Meta 2.76 0 2.76
Cost per 10 net loss 6 Meta 0.11 0.07 0.06
WT profit/10 6 Meta 0 0.80 0.80
PV benefit/10 6 Meta 0 2.27 2.42
Cost of discarding light and wind 10 6 Meta 0 0.01 0.01
Total cost/10 6 Meta 28.40 9.22 9.95
TABLE 2
Voltage quality parameter Scheme one Scheme II Scheme III
Average voltage offset 0.00386 0.00310 0.00245
In summary, the active power distribution network double-layer expansion planning method considering wind-light load uncertainty designed by the invention considers the uncertainty of distributed power supply and load output, the expansion of a line and the planning of the distributed power supply and energy storage, and an example result shows that the typical daily scene analysis method based on Latin hypercube sampling and backward reduction is superior to the traditional scene analysis method, and the obtained scene can better reflect the original scene and part of extreme scenes. The active power distribution network double-layer expansion planning model considering wind-light load uncertainty is closer to the actual working condition, so that not only can better economy be obtained, but also higher system power quality can be obtained. The wind power and photovoltaic output provide a feasible scheme for promoting energy green transformation, and have high practical significance for protecting the environment.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

Claims (10)

1. The active power distribution network double-layer expansion planning method considering wind-light load uncertainty is characterized by comprising the following steps of:
step 1), modeling loads and distributed power sources in a power distribution network by considering uncertainty of wind and light loads to obtain wind turbine sets, photovoltaic power generation and load output models;
step 2) obtaining a typical daily scene of a distributed power supply and load through Latin hypercube sampling and a backward reduction method based on an output model;
and 3) establishing a double-layer planning model of the active power distribution network, wherein the upper layer model takes the lowest annual comprehensive cost as an optimization target, the lower layer model takes the lowest annual operation cost and the lowest node voltage offset as an optimization target, the double-layer planning model is converted into a multi-target optimization problem after being subjected to upper and lower layer associated modeling and second-order cone relaxation, and a normalization normal constraint method is adopted for solving, so that an active power distribution network expansion planning scheme is obtained.
2. The method for planning double-layer expansion of an active power distribution network according to claim 1, wherein the modeling of the load and the distributed power supply in the power distribution network by considering the uncertainty of the wind and the light load is specifically as follows: considering uncertainty of output power of the wind turbine caused by uncertainty of wind speed, describing wind speed characteristics by using Weibull distribution, and establishing a wind turbine output model according to the relation between wind speed and wind power output; and (3) considering uncertainty of the solar radiation intensity of the horizontal plane, describing the intensity of the solar radiation of the horizontal plane by utilizing a Beta distribution function, and establishing a photovoltaic generator set output model according to the relationship between the solar radiation of the horizontal plane and the output of the photovoltaic generator set.
3. The active power distribution network double-layer expansion planning method considering wind-light load uncertainty according to claim 2, wherein wind speed characteristics are described as follows by using Weibull distribution:
wherein: v is wind speed, r is a shape parameter, and c is a scale parameter;
the probability calculation formula of the wind speed v is as follows:
wherein: p (v) is the occurrence probability of the wind speed v; v a And v b The wind speed v is respectively the upper limit value and the lower limit value;
let v t For the actual wind speed at time t, the formula is:
v t ＝(v a,t +v b,t )/2
the wind turbine output model is expressed as:
wherein: g w,t Generating power for the wind turbine at the time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v rated Is the rated wind speed; g R And rated output power of the wind turbine generator.
4. The active power distribution network double-layer expansion planning method considering wind-solar load uncertainty according to claim 2, wherein the intensity of the horizontal plane solar radiation is described by Beta distribution function as follows:
wherein: alpha and Beta are shape parameters of Beta distribution functions; θ is the intensity of the photovoltaic radiation;
wherein the mean μ and standard deviation σ of α, β are obtained using simulations of historical data:
the probability of occurrence of the photovoltaic radiation θ is:
wherein: θ c 、θ d The upper limit and the lower limit of the photovoltaic radiation theta are respectively set;
the photovoltaic power generation output model is as follows:
g PV,t ＝η PV S PV θ t
wherein: η (eta) PV Power generation efficiency for PV; s is S PV The total area of the photovoltaic panel that is the PV; θ t The solar photovoltaic radiation intensity at time t.
5. The method for planning double-layer expansion of an active power distribution network according to claim 1, wherein the step 2) is specifically:
step 2-1) generating random scenes by Latin hypercube sampling based on an output model, fitting an hourly wind-light load distribution function, and generating a large number of scenes by inverse function operation;
step 2-2) constructing a scene reduction model, and reducing and merging the scenes by using a backward reduction method to reduce the number of similar scenes;
step 2-3) obtaining a typical daily scene of the distributed power supply and the load by adopting a typical daily scene fitting method based on normal distribution.
6. The method for planning double-layer expansion of an active power distribution network taking account of uncertainty of wind, light and load according to claim 1, wherein the annual comprehensive cost comprises annual investment cost and annual operation cost.
7. The active power distribution network double-layer expansion planning method considering wind-solar load uncertainty according to claim 1, wherein an objective function of an upper layer model of the active power distribution network double-layer planning model is represented as:
minC＝C in +C op
C in ＝C line +C DG +C ESS
annual investment cost C in Including the cost C of the newly built line line Investment cost C in DG years DG Investment cost C for energy storage year ESS The method comprises the steps of carrying out a first treatment on the surface of the Annual running cost C op Including network loss cost C loss Cost of DG operationEnergy storage operation comprehensive cost->Higher-level power grid electricity purchasing cost C grid Cost of discarding light and wind>
8. The active power distribution network double-layer expansion planning method considering wind-solar load uncertainty according to claim 1, wherein an objective function of a lower layer model of the active power distribution network double-layer planning model is represented as:
min(C op ,ΔU)
wherein C is op For annual operating costs, ΔU is the node voltage offset.
9. The active power distribution network double-layer expansion planning method considering wind-solar load uncertainty according to claim 1, wherein the active power distribution network double-layer planning model meets line investment type selection constraint, DG installation quantity constraint, radial communication state constraint, tide constraint, operation constraint, photovoltaic capacity constraint and energy storage constraint.
10. The method for planning double-layer expansion of an active power distribution network according to claim 1, wherein the solving by adopting a normalized normal constraint method is specifically as follows: and sequentially solving each single-target optimization problem to obtain respective optimal solutions, normalizing the original multi-target optimization problem to obtain normalized subspaces, sequentially judging whether the solution of each single-target problem is better than the respective optimal solution, outputting the pareto optimal solution if yes, obtaining an active power distribution network expansion planning scheme, and if not, solving the points on the other pareto front distribution, and iterating.
CN202310733169.7A 2023-06-19 2023-06-19 Active power distribution network double-layer expansion planning method considering wind-solar-load uncertainty Pending CN116995719A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117374999A (en) * 2023-12-07 2024-01-09 国网辽宁省电力有限公司经济技术研究院 Voltage regulation resource double-layer optimal configuration method and system for power distribution network

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117374999A (en) * 2023-12-07 2024-01-09 国网辽宁省电力有限公司经济技术研究院 Voltage regulation resource double-layer optimal configuration method and system for power distribution network
CN117374999B (en) * 2023-12-07 2024-03-19 国网辽宁省电力有限公司经济技术研究院 Voltage regulation resource double-layer optimal configuration method and system for power distribution network

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