CN116565832A - Non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method - Google Patents

Non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method Download PDF

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CN116565832A
CN116565832A CN202310140964.5A CN202310140964A CN116565832A CN 116565832 A CN116565832 A CN 116565832A CN 202310140964 A CN202310140964 A CN 202310140964A CN 116565832 A CN116565832 A CN 116565832A
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distribution network
power distribution
renewable energy
power
bearing capacity
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Inventor
施进平
吴梦凯
邱逸
文洪君
叶尚兴
叶吉超
章寒冰
徐文军
王笑棠
徐非非
吴新华
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Lishui Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Lishui Power Supply Co of State Grid Zhejiang 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
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a method for analyzing the bearing capacity of renewable energy sources of a power distribution network based on nonparametric probability prediction, which comprises the steps of setting a confidence level based on wind power and photovoltaic output nonparametric probability prediction results and selecting a wind power and photovoltaic output interval; setting constraint conditions with the aim of maximizing renewable energy access capacity, and constructing a renewable energy bearing capacity analysis model of the power distribution network; based on interval probability analysis, deterministic conversion is carried out on a renewable energy bearing capacity analysis model of the power distribution network, so that the maximum renewable energy bearing capacity is obtained, and the operation of the power distribution network is optimized. According to the invention, the renewable energy bearing capacity analysis model of the power distribution network is constructed, the renewable energy bearing capacity in the power distribution network is estimated, and the uncertainty of the distributed power supply output is processed, so that the renewable energy bearing capacity of the power distribution network can be accurately analyzed, and the technical scheme of balanced, coordinated and stable operation of the power distribution network is optimized, so that the comprehensive efficiency of operation of the power distribution network is improved.

Description

Non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a method for analyzing and evaluating the renewable energy bearing capacity of a power distribution network.
Background
Under the background of the construction of a novel power system, the access proportion of renewable energy sources such as wind power, photovoltaic and the like in the power distribution network continuously rises. However, the wind power and the photovoltaic output have extremely strong uncertainty and bring adverse effects to the operation of the power distribution network under the influence of the chaotic characteristic of the weather system. The influence of the fluctuation of the renewable energy output on the safety and stability of the system is large, and the higher the renewable energy access proportion in the power distribution network is, the worse the stability and controllability of the system are. Analyzing and evaluating renewable energy bearing capacity in a power distribution network is an effective solution for improving renewable energy capacity and maintaining stable operation of the power distribution network.
The renewable energy bearing capacity refers to the maximum capacity of the power distribution network capable of absorbing or receiving renewable energy under the conditions that the power grid is not overloaded continuously and short-circuit current, voltage deviation and harmonic waves are not out of standard. Meanwhile, because the prediction error of the output of renewable energy sources such as wind power, photovoltaic and the like is difficult to avoid, the prediction method based on parameterized model assumption has the characteristics of non-stability and heterology, and has larger error and stronger limitation.
For example, in the chinese patent document CN114759615B, a method for analyzing and reducing the distributed photovoltaic load capacity of a distribution network based on hybrid simulation is described, which is a prediction based on parameterized model assumption, and has large error and large limitation. Meanwhile, the technical scheme only considers the single renewable energy bearing capacity of photovoltaic output, and is not suitable for the electric power scene of wind power and photovoltaic integrated application. Therefore, research on an analysis method of the renewable energy bearing capacity of the power distribution network based on non-parameter renewable energy output uncertainty prediction is needed to guide the operation control optimization of the power distribution network of the distributed power grid connection, and ensure safe and reliable operation of the power distribution network.
Disclosure of Invention
The invention aims to solve the technical problems that the method is suitable for a comprehensive scene of a power distribution network with wind power, photovoltaic output and the like integrated with a distributed power supply, and solves the problems that the output fluctuation of renewable energy sources is uncertain and the capacity of the renewable energy sources is effectively utilized. The invention aims to provide a method for analyzing renewable energy bearing capacity of a power distribution network based on nonparametric probability prediction, which is based on wind power and photovoltaic output nonparametric probability prediction results, considers active/reactive power constraint, node voltage constraint, gas generator set output constraint and energy storage operation constraint of a power distribution network line, analyzes the renewable energy bearing capacity of the power distribution network by using an interval optimization method, processes uncertainty of distributed power supply output by evaluating the renewable energy bearing capacity in the power distribution network, and realizes a technical scheme of balanced, coordinated and stable operation of the power distribution network so as to improve comprehensive efficiency of operation of the power distribution network.
The technical scheme adopted by the invention is as follows: a method for analyzing the bearing capacity of renewable energy sources of a power distribution network based on non-parametric probability prediction comprises the following steps:
step S1: and setting a confidence level based on the nonparametric probability prediction result of the wind power and the photovoltaic output, and selecting a wind power and photovoltaic output interval.
Step S2: and setting constraint conditions with the aim of maximizing the renewable energy access capacity, and constructing a renewable energy bearing capacity analysis model of the power distribution network.
Step S3: based on interval probability analysis, deterministic conversion is carried out on a renewable energy bearing capacity analysis model of the power distribution network, so that the maximum renewable energy bearing capacity is obtained, and the operation of the power distribution network is optimized.
According to the technical scheme, an objective function and constraint conditions are set on the basis of wind power and photovoltaic output nonparametric probability prediction results, a renewable energy bearing capacity analysis model of the power distribution network is constructed, reasonable limits of active/reactive power constraint, node voltage constraint, gas generator set output constraint and energy storage operation constraint of a power distribution network line are considered, the renewable energy bearing capacity of the power distribution network is analyzed by using an interval optimization method, uncertainty of distributed power output is processed by evaluating the renewable energy bearing capacity in the power distribution network, and therefore the technical scheme of balanced, coordinated and stable operation of the power distribution network is optimized, and comprehensive efficiency of operation of the power distribution network is improved.
In this technical solution, further, the non-parameter probability prediction result in step S1 is:
wherein: f (F) t+h∣t For discretized wind power or photovoltaic future output conditional probability distribution, a series of discretized quantile level quantiles are used forConstructing; a, a i The specific division is horizontal, t is the predicted time, and h is the advance time.
The quantiles are defined as:
q (α) ＝inf{y∣F t+h|t (y)＞α}
wherein: y is a random variable.
Setting a confidence level in the step S1, and selecting a wind power and photovoltaic output interval, wherein the specific steps are as follows:
after setting the confidence level (1-epsilon)%, selecting the corresponding confidence intervalFor the range of wind power and photovoltaic output intervals, the end points of the intervals should meet the following conditions:
wherein:the left and right endpoints of the interval; /> αDividing bit levels corresponding to the interval endpoints respectively;
after selecting a proper confidence interval, the wind power and photovoltaic output are expressed by interval numbers:
wherein: p (P) t WT 、P t PV Wind power and photovoltaic output;the number of the wind power and photovoltaic output intervals is respectively;the left end point and the right end point of the wind power output section are respectively; />The left end point and the right end point of the photovoltaic output section;α WT 、/>、α PV 、/>the level of the bit corresponding to each endpoint.
In this technical solution, further, the power distribution network renewable energy source bearing capacity analysis model in step S2 is:
s.t.h i (x,u)＝0,i＝1,2,...,m
g j (x,u)≥0,j＝1,2,...,n
u∈U I ＝[U - ,U + ]
wherein:and->The wind power and photovoltaic access capacity of each node of the power distribution system is achieved; h is a i (x, u) =0 is a system equality constraint, the equality constraint number is m; g j (x, u) is equal to or greater than 0 and is a system inequality constraint, and the number of the inequality constraints is n; />A power distribution system node numbering set, wherein N is the number of system nodes; x is a system decision variable; u is an uncertainty parameter in the system, and is wind power and photovoltaic output; u (U) I U as a set of interval numbers - And U + Is the left and right end points of the interval.
In this solution, further, the constraint condition in step S2 includes: power flow constraint of a power distribution network, equipment operation constraint and wind power and photovoltaic output constraint; the equipment operation constraint comprises the output constraint of the gas generator set and the energy storage equipment operation constraint.
Still further, the power distribution network power flow constraint is:
wherein: p (P) jk 、Q jk Active power and reactive power flowing from node j to node k on the line; p (P) j 、Q j Active power and reactive power injected for node j; v (V) i For the voltage of node i, V 0 Is the reference voltage; r is R ij 、X ij The resistance and reactance of the line ij, respectively.
The gas generating set output constraint is as follows:
wherein: p (P) t GT 、、P t gas，GT The active output, the reactive output and the fuel gas consumption power of the fuel gas generator set are respectively; />The lower limit and the upper limit of the active output of the gas unit are set;Q t GT 、/>the lower limit and the upper limit of reactive output of the gas unit are respectively; />The upward and downward ramp rates of the gas unit are respectively; Δt is the optimized time interval; η (eta) GT The running efficiency of the unit is achieved.
The energy storage device operating constraints are:
SOC i,0 ＝SOC i,T
wherein:respectively charging and discharging power of the energy storage equipment; />Respectively rated charge and discharge power of the energy storage equipment; SOC (State of Charge) i,t Is the state of charge of the energy storage device; i SOC、/>respectively a lower limit and an upper limit of the charge state of the energy storage device; epsilon char 、ε dc 、ε self Respectively the charging efficiency, the discharging efficiency and the self-discharging rate of the equipment; />Is the rated capacity of the energy storage device.
The wind power and photovoltaic output constraint is as follows:
wherein:and->The wind power and photovoltaic access capacity of each node of the power distribution system is achieved; p (P) t WT 、P t PV Wind power and photovoltaic output; />Wind power and photovoltaic power output of each node are respectively obtained.
In the technical scheme, further, based on the interval likelihood analysis, step S3 converts the power distribution network renewable energy bearing capacity analysis model deterministically, and the specific steps are that interval constraint likelihood is set, and an analysis result is obtained, and uncertainty constraint conditions are converted into deterministically constraint by using an interval analysis method, and the model is solved linearly:
and converting uncertainty constraint in the power distribution network renewable energy bearing capacity analysis model into the following inequality constraint based on interval probability analysis:
wherein: lambda (lambda) j ∈[0,1]Is a preset likelihood level;as a function g j (x, u) can take on value intervals under the decision variable x, and +.>Wherein (1)>And->Calculated according to the following formula:
obtainingThen, based on the interval likelihood, the constraint likelihood is obtained>And judging whether the current decision meets the constraint condition.
In summary, compared with the prior art, the method provided by the invention has the advantages that based on the wind power and photovoltaic output nonparametric probability prediction result, the confidence interval under a certain confidence level is selected, the wind power and photovoltaic output are expressed as the interval number, the uncertainty of the renewable energy output is considered, and meanwhile, the model complexity and the solving difficulty are reduced.
According to the invention, the section inequality constraint-containing renewable energy bearing capacity analysis model of the power distribution network is converted into a deterministic model by a section probability method, and a linear tool can be used for solving to obtain a final bearing capacity analysis result, so that the method has a good analysis effect and operation efficiency.
The invention is suitable for comprehensive scenes of the power distribution network with wind power, photovoltaic output and the like integrated into a distributed power supply, and solves the problems that the output fluctuation of renewable energy sources is uncertain and the capacity of the renewable energy sources is effectively utilized.
Drawings
Fig. 1 is a flowchart of a method for analyzing the bearing capacity of renewable energy sources of a power distribution network.
Detailed Description
The technical scheme of the embodiment of the invention is described below with reference to the accompanying drawings.
Example 1.
A method for analyzing the bearing capacity of renewable energy sources of a power distribution network based on nonparametric probability prediction is based on the hierarchical collaborative planning of energy sources of Lishui county and the Internet, and the embodiment mainly comprises the following steps:
1) And constructing interval expression of wind power and photovoltaic output based on wind power and photovoltaic nonparametric probability prediction results.
2) And constructing an objective function and constraint conditions of a renewable energy bearing capacity analysis model of the power distribution network.
3) And constructing a renewable energy bearing capacity analysis model of the power distribution network based on interval optimization.
4) Model deterministic conversion is performed based on interval likelihood analysis.
5) And solving the optimization problem to obtain the analysis result of the renewable energy bearing capacity of the power distribution network.
The wind power and photovoltaic distributed power supply has the characteristics of strong uncertainty due to the influence of natural resources on the output force, an uncertain distribution set of uncertain variables is difficult to obtain, and a scene with discrete opposite polar ends is selected from an actual sample to represent possible values of distributed power supply output. However, the actual probability distribution for each discrete scene is still uncertain. The feasible region to which the probability value of each discrete scene belongs is arbitrary. In order to ensure that the actual probability distribution is closer to the actual operation data and fluctuates in a reasonable range, constraint is needed, and probability limitation of each distributed power supply output scene is realized.
This embodiment sets the confidence level by the 1-norm constraint and the ++norm constraint, limiting the fluctuation range of the probability distribution.
The objective function of the renewable energy bearing capacity analysis model of the power distribution network is as follows:
in the method, in the process of the invention,and->The wind power and photovoltaic access capacity of each node of the power distribution system is represented; />And the distribution system node number set is represented, and N is the number of the system nodes.
The main constraint conditions of the power distribution network renewable energy bearing capacity analysis model include: power distribution network tide constraint, gas generator set output constraint, energy storage equipment operation constraint and wind photovoltaic output constraint.
The algorithm of the power flow constraint of the power distribution network is as follows:
wherein: p (P) jk 、Q jk Active power and reactive power flowing from node j to node k on the line; p (P) j 、Q j Active power and reactive power injected for node j; v (V) i For the voltage of node i, V 0 Is the reference voltage; r is R ij 、X ij The resistance and reactance of the line ij, respectively.
The algorithm of the output constraint of the gas generator set is as follows:
P t GT ＝η GT P t gas，GT
wherein: p (P) t GT 、、P t gas，GT The active output, the reactive output and the fuel gas consumption power of the fuel gas generator set are respectively; />The lower limit and the upper limit of the active output of the gas unit are set; />The lower limit and the upper limit of reactive output of the gas unit are respectively; />The upward and downward ramp rates of the gas unit are respectively; Δt is the optimized time interval; η (eta) GT The running efficiency of the unit is achieved.
The algorithm of the energy storage device operation constraint is as follows:
SOC i,0 ＝SOC i,T
wherein:respectively charging and discharging power of the energy storage equipment; />Respectively rated charge and discharge power of the energy storage equipment; SOC (State of Charge) i,t Is the state of charge of the energy storage device; i SOC、/>respectively a lower limit and an upper limit of the charge state of the energy storage device; epsilon char 、ε dc 、ε self Respectively the charging efficiency, the discharging efficiency and the self-discharging rate of the equipment; />Is the rated capacity of the energy storage device.
The algorithm of wind-electricity photovoltaic output constraint is as follows:
wherein:and->The wind power and photovoltaic access capacity of each node of the power distribution system is achieved; p (P) t WT 、P t PV Wind power and photovoltaic output; />Wind power and photovoltaic power output of each node are respectively obtained.
The constructed section optimization-based renewable energy bearing capacity analysis model of the power distribution network is as follows:
s.t.h i (x,u)＝0,i＝1,2,...,m
g j (x,u)≥0,j＝1,2,...,n
u∈U I ＝[U - ,U + ]
wherein:and->The wind power and photovoltaic access capacity of each node of the power distribution system is achieved; h is a i (x, u) =0 is a system equality constraint, the equality constraint number is m; g j (x, u) is equal to or greater than 0 and is a system inequality constraint, and the number of the inequality constraints is n; />A power distribution system node numbering set, wherein N is the number of system nodes; x is a system decision variable; u is an uncertainty parameter in the system, and is wind power and photovoltaic output; u (U) I U as a set of interval numbers - And U + Is the left and right end points of the interval.
Based on interval possibility analysis, the specific step of deterministic conversion of the power distribution network renewable energy bearing capacity analysis model is to set interval constraint possibility, an analysis result is obtained, an uncertainty constraint condition is converted into deterministic constraint by using an interval analysis method, and a model is solved linearly:
wherein: lambda (lambda) j ∈[0,1]Is a preset likelihood level;as a function g j (x, u) can take on value intervals under the decision variable x, and +.>Wherein (1)>And->Calculated according to the following formula:
obtainingThen, based on the interval likelihood, the constraint likelihood is obtained>And judging whether the current decision meets the constraint condition.
By the method, uncertainty constraint of the number of intervals in the model is converted into deterministic constraint. And solving the problem of analysis of the renewable energy bearing capacity of the power distribution network through a linear programming solver to obtain the final maximum renewable energy bearing capacity of the power distribution network.

Claims (10)

1. The method for analyzing the renewable energy bearing capacity of the power distribution network based on the non-parametric probability prediction is characterized by comprising the following steps of:
s1: setting a confidence level based on the nonparametric probability prediction result of wind power and photovoltaic output, and selecting a wind power and photovoltaic output interval;
s2: setting constraint conditions with the aim of maximizing renewable energy access capacity, and constructing a renewable energy bearing capacity analysis model of the power distribution network;
s3: based on interval probability analysis, deterministic conversion is carried out on a renewable energy bearing capacity analysis model of the power distribution network, so that the maximum renewable energy bearing capacity is obtained, and the operation of the power distribution network is optimized.
2. The method for analyzing the bearing capacity of the renewable energy sources of the power distribution network based on the non-parametric probability prediction according to claim 1, wherein the non-parametric probability prediction in the step S1 is:
wherein: f (F) t+h∣t For discretized wind power and photovoltaic future output conditional probability distribution, the discretized bit distribution level bit distribution is adoptedConstructing; a, a i The method is characterized in that the method is divided into a specific level, t is a prediction time, and h is an advance time;
the quantiles are defined as:
q (α) ＝inf{y∣F t+h|t (y)＞α}
wherein: y is a random variable.
3. The method for analyzing the bearing capacity of the renewable energy sources of the power distribution network based on the non-parametric probability prediction according to claim 1, wherein the confidence level is set in the step S1, and a wind power and photovoltaic output interval is selected, specifically comprising the following steps:
setting confidence level (1-epsilon)%, and selecting corresponding confidence intervalFor the range of wind power and photovoltaic output intervals, the end points of the intervals meet the following conditions:
wherein:the left and right endpoints of the interval; />Alpha is the dividing level corresponding to the interval end point;
after selecting a proper confidence interval, the wind power and photovoltaic output are expressed by interval numbers:
wherein: p (P) t WT 、P t PV Wind power and photovoltaic output;the number of the wind power and photovoltaic output intervals is respectively; /> The left end point and the right end point of the wind power output section are respectively; />The left end point and the right end point of the photovoltaic output section;α WT 、/> α PV 、/>the level of the bit corresponding to each endpoint.
4. The method for analyzing the renewable energy bearing capacity of the power distribution network based on the non-parametric probability prediction according to claim 1, wherein the model for analyzing the renewable energy bearing capacity of the power distribution network in step S2 is as follows:
s.t.h i (x,u)＝0,i＝1,2,...,m
g j (x,u)≥0,j＝1,2,...,n
u∈U I ＝[U - ,U + ]
wherein:and->For each node of a power distribution systemWind power and photovoltaic access capacity; h is a i (x, u) =0 is a system equality constraint, the equality constraint number is m; g j (x, u) is equal to or greater than 0 and is a system inequality constraint, and the number of the inequality constraints is n; n is the number set of the nodes of the power distribution system, and N is the number of the nodes of the system; x is a system decision variable; u is an uncertainty parameter in the system, and is wind power and photovoltaic output; u (U) I U as a set of interval numbers - And U + Is the left and right end points of the interval.
5. The method for analyzing the bearing capacity of the renewable energy sources of the power distribution network based on the non-parametric probability prediction according to claim 1, wherein the constraint conditions in S2 comprise: power flow constraint of a power distribution network, equipment operation constraint and wind power and photovoltaic output constraint; the equipment operation constraint comprises the output constraint of the gas generator set and the energy storage equipment operation constraint.
6. The non-parametric-probability-prediction-based power distribution network renewable energy bearing capacity analysis method according to claim 5, wherein the power distribution network tide constraint is:
wherein: p (P) jk 、Q jk Active power and reactive power flowing from node j to node k on the line; p (P) j 、Q j Active power and reactive power injected for node j; v (V) i For the voltage of node i, V 0 Is the reference voltage; r is R ij 、X ij The resistance and reactance of the line ij, respectively.
7. The non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method according to claim 5, wherein the gas generator set output constraint is:
P t GT ＝η GT P t gas,GT
wherein: p (P) t GT 、Q t GT 、P t gas,GT The active output, the reactive output and the fuel gas consumption power of the fuel gas generator set are respectively;the lower limit and the upper limit of the active output of the gas unit are set; />The lower limit and the upper limit of reactive output of the gas unit are respectively; />The upward and downward ramp rates of the gas unit are respectively; Δt is the optimized time interval; η (eta) GT The running efficiency of the unit is achieved.
8. The method for analyzing the bearing capacity of the renewable energy sources of the power distribution network based on the non-parametric probability prediction according to claim 5,
the energy storage device operating constraints are:
SOC i，0 ＝SOC i，T
wherein:respectively charging and discharging power of the energy storage equipment; />Respectively rated charge and discharge power of the energy storage equipment; SOC (State of Charge) i,t Is the state of charge of the energy storage device; i SOC、/>respectively a lower limit and an upper limit of the charge state of the energy storage device; epsilon char 、ε dc 、ε self Respectively the charging efficiency, the discharging efficiency and the self-discharging rate of the equipment; />Is the rated capacity of the energy storage device.
9. The non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method according to claim 5, wherein the wind power and photovoltaic output constraints are:
wherein:and->The wind power and photovoltaic access capacity of each node of the power distribution system is achieved; p (P) t WT 、P t PV Wind power and photovoltaic output;wind power and photovoltaic power output of each node are respectively obtained.
10. The method for analyzing the bearing capacity of the renewable energy sources of the power distribution network based on the non-parametric probability prediction according to claim 1,
step S3, based on interval possibility analysis, deterministic conversion is carried out on a renewable energy bearing capacity analysis model of the power distribution network, the specific steps are that interval constraint possibility is set, an analysis result is obtained, uncertainty constraint conditions are converted into deterministic constraint by an interval analysis method, and the model is solved linearly; wherein,,
the uncertainty constraint in the power distribution network renewable energy bearing capacity analysis model is converted into an inequality constraint based on interval probability analysis:
wherein: lambda (lambda) j ∈[0,1]For a preset degree of possibilityLeveling;as a function g j (x, u) can take on value intervals under the decision variable x, and +.>Wherein (1)>And->The method comprises the following steps of:
obtainingThen, based on the interval likelihood, the constraint likelihood is obtained>And judging whether the current decision meets the constraint condition.
CN202310140964.5A 2023-02-21 2023-02-21 Non-parametric probability prediction-based power distribution network renewable energy bearing capacity analysis method Pending CN116565832A (en)

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