CN113609686B - New energy confidence capacity analysis method and system - Google Patents

New energy confidence capacity analysis method and system Download PDF

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CN113609686B
CN113609686B CN202110907150.0A CN202110907150A CN113609686B CN 113609686 B CN113609686 B CN 113609686B CN 202110907150 A CN202110907150 A CN 202110907150A CN 113609686 B CN113609686 B CN 113609686B
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power generation
new energy
capacity
power
confidence
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CN113609686A (en
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朱克平
何英静
胡鹏飞
李玉京
王科丁
汤东升
李帆
沈舒仪
刘军
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Abstract

The invention discloses a new energy confidence capacity analysis method and a system, wherein the method comprises the following steps: s1, constructing a new energy power generation power model, and calculating a new energy power generation power curve with the same interval time in a certain period based on the new energy power generation power model; s2, constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model; s3, simulating and calculating the reliability level of the electric power system containing the new energy by adopting a time sequence Monte Carlo method; and S4, performing repeated iterative computation on the reliability level of the electric power system containing the new energy through a chord cut method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation. In the invention, when the new energy confidence capacity is analyzed, the conventional unit model is added, so that the reliability of the new energy confidence capacity analysis is improved, and the similarity to an actual power system is improved.

Description

New energy confidence capacity analysis method and system
Technical Field
The invention relates to the technical field of new energy power generation, in particular to a new energy confidence capacity analysis method and system.
Background
Along with the continuous expansion of the scale of wind power and photovoltaic installation, and the differences of load characteristics and wind and light resource endowments of all areas, a confidence capacity model of new energy output is established by combining the current power grid planning requirement and new energy development trend, and the method has very important significance in providing basis for realizing the electric power and electricity balance of an electric power system containing high-proportion new energy. And the research of providing confidence capacity for new energy sources in the planning stage mainly adopts a simulation method based on reliability index analysis. The simulation method refers to the change of the trusted capacity before and after the new energy power generation is connected under the condition that the power generation reliability index is unchanged.
The system reliability analysis method comprises three types of analysis methods, namely a Monte Carlo method and an intelligent algorithm, wherein the analysis methods comprise a network method and a state space method; the Monte Carlo method comprises a state sampling method, a state duration sampling method and a system state time transfer sampling method; the intelligent algorithm comprises a particle swarm algorithm, an ant colony algorithm and the like.
The reliability evaluation method of the power system comprises a deterministic method and a probabilistic method, wherein the deterministic method comprises a percentage backup method and a maximum equipment utilization method, and the methods are mainly determined according to long-term accumulated reliability data, load prediction data and experience of planners of the power system; the probability method comprises a power shortage time probability method, a frequency and duration method and an analog method.
The method for calculating the confidence capacity of the new energy source is a dichotomy chord cut-off method.
The definition of the confidence capacity of the new energy source mainly comprises four types of equivalent reliable capacity, equivalent conventional unit capacity, effective load capacity and guaranteed output under certain confidence. The equivalent reliable capacity means that after a new energy power generation field is removed and a certain capacity of a virtual conventional unit without outage rate is added, the system reliability is the same as the actual system reliability, and then the capacity of the virtual conventional unit accounting for the installed capacity of the new energy is defined as the new energy confidence capacity; the equivalent regular unit capacity is similar to the equivalent reliable capacity definition, except that the virtual regular unit in the definition can have a certain random outage rate, and the random outage rate is often based on the random outage rate of the conventional unit in the system; the effective load capacity refers to the difference value of loads which can be supplied by the system under the same reliability level before and after the new energy is accessed, and the difference value is the new energy confidence capacity; the guaranteed output under a certain confidence coefficient refers to the size of the available capacity of the power generation side under a certain confidence coefficient (for example, 95%), namely, the available power generation capacity of the system under the confidence coefficient is not smaller than the guaranteed output, and the new energy confidence capacity is defined as the increase of the guaranteed output of the system after the new energy is added.
At present, various aspects of new energy confidence capacity analysis are studied more, but systematic comprehensive analysis still needs to be further developed. The existing scheme has a certain expansion, but has a certain defect in consideration of the operation of a conventional unit due to the small amount of analyzed data, which is not beneficial to completely analyzing the confidence capacity of new energy in a certain area.
The invention patent of China with the publication number of CN112785027A discloses a method and a system for evaluating the confidence capacity of a wind-solar-energy-storage combined power generation system in 2021, 5 and 11 days, introduces the method and the system for evaluating the confidence capacity of the wind-solar-energy-storage combined power generation system, and solves the problem of evaluating and analyzing the confidence capacity of a new energy power system with an energy storage system under different scenes. The method can calculate the theoretical confidence capacity of the new energy, but has a certain improvement space for the simulation similarity of the power system in engineering practice; meanwhile, the description of the conventional unit generation power curve is not clear.
In summary, in order to better evaluate the confidence capacity of the new energy and provide a reference for planning the electric power system, not only needs to consider the operation condition of the conventional unit, but also needs to design a set of system which is convenient for subsequent operation of personnel, and add the random operation condition of the conventional unit when calculating the confidence capacity of the new energy.
Disclosure of Invention
The invention provides a new energy confidence capacity analysis method and a new energy confidence capacity analysis system for overcoming the defects of the technology.
The technical scheme adopted for overcoming the technical problems is as follows:
a new energy confidence capacity analysis method at least comprises the following steps:
s1, constructing a new energy power generation power model, and calculating a new energy power generation power curve with equal interval time in a certain period based on the new energy power generation power model, wherein the new energy power generation at least comprises photovoltaic power generation and wind power generation, the new energy power generation power model at least comprises a photovoltaic power generation power model and a wind power generation power model, and the new energy power generation power curve at least comprises a photovoltaic power generation power curve and a wind power generation power curve;
s2, constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model;
s3, simulating and calculating the reliability level of the power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve;
and S4, performing repeated iterative computation on the reliability level of the electric power system containing the new energy through a chord cut method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation.
Further, in steps S1 and S2, a certain period is year-round, and the equal interval time is hour-by-hour.
Further, the step S1 specifically includes the following steps:
calculating a new energy power generation power curve at least through the input sunlight intensity, temperature and wind speed data;
(1) The calculation of the photovoltaic power generation power is as follows:
in the formula (1), P PV For the actual output power of the photovoltaic cell, Y PV For the rated power of the photovoltaic cell under standard test conditions, f PV For the loss factor of the photovoltaic cell, R T For the actual light radiation intensity, R STC Is 1kW/m of light radiation intensity under standard test conditions 2 ，α P For the power temperature coefficient of the photovoltaic cell, T C T is the actual ambient temperature STC Is the ambient temperature under standard test conditions;
(2) The wind power generation power is calculated as follows:
in the formula (2), P t For wind power, A, B, C is a fan power characteristic curve parameter, different fans are slightly different, V ci 、V r 、V co 、P r The wind speed is the starting wind speed, the rated wind speed, the cut-out wind speed and the rated power of the fan;
(3) Wind power generation power model parameters:
in the formula (3), A, B, C is a fan power characteristic parameter in the formula (2).
Further, in step S2, an IEEE RTS-79 test system or a TH-RTS2000 test system is adopted as a conventional unit for equivalent simulation.
Further, the step S2 specifically includes the following steps:
calculating a power generation curve of a conventional unit in a basic test system by using a state duration sampling method in a Monte Carlo method, and carrying out equivalence on conventional unit data of a simulated region according to the basic test system;
the conventional unit adopts a double-state model, namely a normal operation state and a fault state, and uses the normal operation duration t 1 And maintenance time t 2 Describing, the working time and the repairing time are subjected to exponential distribution, and the sampling method comprises the following steps:
t 1 ＝-t MTTF lnγ 1 (14)
t 2 ＝-t MTTR lnγ 2 (15)
in the formulas (4) and (5), gamma 1 、γ 2 For compliance [0,1 ] generated by a program]Random numbers uniformly distributed among t MTTF For average working time, t MTTR Is the average repair time;
and (3) calculating a conventional unit power generation curve in the basic test system according to the formulas (4) and (5), and adjusting according to conventional unit data of the simulated region to obtain a conventional unit power generation curve conforming to the parameters of the simulated region.
Further, the step S3 specifically includes the following steps:
calculating the reliability level of an electric power system containing new energy through sequential Monte Carlo simulation, wherein a reliability index is selected to be less than an expected EENS, and the calculation formula of the reliability index is as follows:
in the formula (6), N Y To simulate the total years, N is the state of lack of electricity, L j Power generation side at the jth power failure state, C j The load power in the j-th power failure state is equal to or more than 1 and equal to or less than N.
Further, the step S4 specifically includes the following steps:
calculating the confidence capacity defined by the effective load capacity through the chord cut method, namely, under the condition of keeping the specified reliability level unchanged, adding additional load requirement delta L which can be met by new energy, and calculating the confidence capacity C of photovoltaic power generation and wind power generation C And capacity confidence C P Confidence capacity of photovoltaic power generation C C And capacity confidence C P Confidence capacity C with wind power generation C And capacity confidence C P The calculation formula is general and specifically comprises the following steps:
C C ＝ΔL (17)
in the formula (8), C WP Rated installed capacity of the power system containing new energy;
s4.1, assume that the peak value of the original system load is L pk0 The given error is epsilon, wherein the original system refers to a power system without new energy, the reliability index R of the original system is calculated, and C is increased when the load curve is L WP Reliability index R of new energy power system 1 Load curve of L+C WP Increasing the reliability index R of the new energy power system 2 ；
S4.2, find the passing point X (L pk0 ,R 1 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa a of the intersection point of the straight line A and the straight line f (x) =R ci1 ＝L+a-L pk0 Substituting the reliability index R calculated in (6) 3 ；
S4.3, judging |R 3 Magnitude relation of r| to epsilon: if |R 3 -R| > ε, then continue iteration, find the passing point X 1 (a,R 3 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa B of the intersection point of the straight line B and the straight line f (x) =R ci2 ＝L+b-L pk0 Substituting the reliability index R calculated in (6) 4 Then, step S4.4 is performed; otherwise, executing the step S4.5;
s4.4, judging |R 4 Magnitude relation of r| to epsilon: if |R 4 -r| > epsilon, repeating step S4.3 until the difference between the new reliability index and the reliability index of the original system is less than a given error epsilon;
s4.5, recording the corresponding peak load L at the end of iteration p New energy can be addedThe externally assumed confidence capacities defined by the payload capacities are: c (C) C ＝ΔL＝L p -L pk0 The capacity confidence is:
further, the method further comprises the step S5: and changing the simulated installed capacity of the new energy, respectively calculating confidence capacities and capacity confidence degrees under different installed capacities of the new energy, and drawing a curve of the change of the confidence capacity of the new energy along with the penetrating power level of the new energy, wherein the penetrating power refers to the proportion of the installed capacity to the total load of the power system.
The invention also provides a new energy confidence capacity analysis system, which at least comprises:
the new energy unit power generation module is used for constructing a new energy power generation model, calculating a new energy power generation power curve with equal interval time in a certain period based on the new energy power generation model, wherein the new energy power generation at least comprises photovoltaic power generation and wind power generation, the new energy power generation model at least comprises a photovoltaic power generation power model and a wind power generation power model, and the new energy power generation power curve at least comprises a photovoltaic power generation power curve and a wind power generation power curve;
the conventional unit power generation module is used for constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model;
the reliability level module is used for simulating and calculating the reliability level of the power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve; the method comprises the steps of,
the confidence capacity and capacity confidence coefficient module is used for carrying out repeated iterative computation on the reliability level of the electric power system containing the new energy source through a chord interception method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation.
The beneficial effects of the invention are as follows:
1. in the invention, when the new energy confidence capacity is analyzed, the conventional unit model is added, so that the reliability of the new energy confidence capacity analysis is improved, and the similarity to an actual power system is improved.
2. The invention combines the advantages of the statistical method into the simulation method, and improves the reliability of the new energy confidence capacity analysis result through the requirements of data precision and data quantity.
3. The invention improves the requirement on the data quantity, accords with the current situation that the accuracy of the meteorological data acquisition result in each region of China is high and the range is wide, thereby further improving the simulation degree of the new energy confidence capacity analysis result on the actual condition of the simulated region and improving the credibility and the reference value of the analysis result.
Drawings
Fig. 1 is a flowchart of a new energy confidence capacity analysis method according to embodiment 1 of the present invention.
FIG. 2 is a schematic diagram of the basic test system employed in example 1 of the present invention.
Fig. 3 is a schematic diagram of chord cut iteration in embodiment 1 of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and the specific examples, which are given by way of illustration only and are not intended to limit the scope of the invention, in order to facilitate a better understanding of the invention to those skilled in the art.
Example 1,
The embodiment discloses a new energy confidence capacity analysis method, which at least comprises the following steps as shown in fig. 1:
step S1, a new energy power generation power model is built, a new energy power generation power curve with equal interval time in a certain period is calculated based on the new energy power generation power model, wherein the new energy power generation at least comprises photovoltaic power generation and wind power generation, the new energy power generation power model at least comprises a photovoltaic power generation power model and a wind power generation power model, and the new energy power generation power curve at least comprises a photovoltaic power generation power curve and a wind power generation power curve. In this embodiment, the certain period is preferably year round, and the equal interval time is preferably hour by hour, and may be specifically selected according to actual conditions.
In this embodiment, the step S1 specifically includes the following steps:
and calculating a new energy power generation power curve according to the input data such as sunlight intensity, temperature, wind speed and the like.
(1) The calculation of the photovoltaic power generation power is as follows:
in the formula (1), P PV For the actual output power of the photovoltaic cell, Y PV For the rated power of the photovoltaic cell under standard test conditions, f PV For the loss factor of the photovoltaic cell, R T For the actual light radiation intensity, R STC Is 1kW/m of light radiation intensity under standard test conditions 2 ，α P For the power temperature coefficient of the photovoltaic cell, the temperature coefficient is generally-0.35 percent/DEG C, T C T is the actual ambient temperature STC Is 25 ℃ of the ambient temperature under standard test conditions;
(2) The wind power generation power is calculated as follows:
in the formula (2), P t For wind power, A, B, C is a fan power characteristic curve parameter, different fans are slightly different, V ci 、V r 、V co 、P r The wind speed is the starting wind speed, the rated wind speed, the cut-out wind speed and the rated power of the fan;
(3) Wind power generation power model parameters:
in the formula (3), A, B, C is a fan power characteristic parameter in the formula (2).
And S2, constructing a conventional unit power generation power model, and calculating a conventional unit power generation power curve with equal interval time in a certain period based on the conventional unit power generation power model. The same time period and the same interval time as those in step S1 are preferably the same as each other in step S2, and the same interval time is preferably each hour.
In this embodiment, step S2 specifically includes the following steps:
the state duration sampling method in the Monte Carlo method is utilized to calculate the power curve of the conventional unit in the basic test system, the conventional unit data of the simulated region is equivalent according to the basic test system, the state duration sampling method in the Monte Carlo method is utilized to accurately calculate the duration index, any probability distribution of the state duration can be flexibly simulated, the basic test system adopted in the equivalent scheme is shown in figure 2, and the IEEE RTS-79 test system is adopted.
The conventional unit adopts a double-state model, namely a normal operation state and a fault state, and uses the normal operation duration t 1 And maintenance time t 2 Describing, the working time and the repairing time are subjected to exponential distribution, and the sampling method comprises the following steps:
t 1 ＝-t MTTF lnγ 1 (22)
t 2 ＝-t MTTR lnγ 2 (23)
in the formulas (4) and (5), gamma 1 、γ 2 For compliance [0,1 ] generated by a program]Random numbers uniformly distributed among t MTTF For average working time, t MTTR Is the average repair time;
and (3) calculating a conventional unit power generation curve in the basic test system according to the formulas (4) and (5), and adjusting according to conventional unit data of the simulated region to obtain a conventional unit power generation curve conforming to the parameters of the simulated region.
And S3, simulating and calculating the reliability level of the electric power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve.
Specifically, step S3 specifically includes the following:
the reliability level of the electric power system containing new energy is calculated through sequential Monte Carlo simulation, the reliability index selects the expected EENS with insufficient electric quantity, the index is the expected electric quantity which can not be supplied in one year because the peak load per hour exceeds the available power generation capacity, and the calculation formula of the reliability index is as follows:
in the formula (6), N Y To simulate the total years, N is the state of lack of electricity, L j Power generation side at the jth power failure state, C j The load power in the j-th power failure state is equal to or more than 1 and equal to or less than N.
And S4, performing repeated iterative computation on the reliability level of the electric power system containing the new energy through a chord cut method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation, wherein the reason of adopting the chord cut method is that the convergence rate of the chord cut method is 1.68 th order, and the theoretical efficiency is higher than that of the chord cut method.
In this embodiment, step S4 specifically includes the following steps:
calculating the confidence capacity defined by the effective load capacity through the chord cut-off method, namely, under the condition of keeping the specified reliability level unchanged, adding additional load requirement delta L which can be met by new energy, and calculating the confidence capacity C of photovoltaic power generation and wind power generation C And capacity confidence C P Confidence capacity of photovoltaic power generation C C And capacity confidence C P Confidence capacity C with wind power generation C And capacity confidence C P The calculation formula is general and specifically comprises the following steps:
C C ＝ΔL (25)
in the formula (8), C WP Rated installed capacity of the power system containing new energy;
s4.1, assume that the original system load peak value isL pk0 The given error is epsilon, wherein the original system refers to a power system without new energy, the reliability index R of the original system is calculated, and C is increased when the load curve is L WP Reliability index R of new energy power system 1 Load curve of L+C WP Increasing the reliability index R of the new energy power system 2 As shown in fig. 3;
s4.2, find the passing point X (L pk0 ,R 1 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa a of the intersection point of the straight line A and the straight line f (x) =R ci1 ＝L+a-L pk0 Substituting the reliability index R calculated in (6) 3 ；
S4.3, judging |R 3 Magnitude relation of r| to epsilon: if |R 3 -R| > ε, then continue iteration, find the passing point X 1 (a,R 3 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa B of the intersection point of the straight line B and the straight line f (x) =R ci2 ＝L+b-L pk0 Substituting the reliability index R calculated in (6) 4 Then, step S4.4 is performed; otherwise, executing the step S4.5;
s4.4, judging |R 4 Magnitude relation of r| to epsilon: if |R 4 -r| > epsilon, repeating step S4.3 until the difference between the new reliability index and the reliability index of the original system is less than a given error epsilon;
s4.5, recording the corresponding peak load L at the end of iteration p The confidence capacity defined by the payload capacity that the new energy can additionally bear is: c (C) C ＝ΔL＝L p -L pk0 The capacity confidence is:
in order to visualize the result, for ease of analysis, the present embodiment further includes step S5: and changing the simulated installed capacity of the new energy, respectively calculating confidence capacities and capacity confidence degrees under different installed capacities of the new energy, and drawing a curve of the change of the confidence capacity of the new energy along with the penetrating power level of the new energy, wherein the penetrating power refers to the proportion of the installed capacity to the total load of the power system.
EXAMPLE 2,
The present embodiment provides a new energy confidence capacity analysis system corresponding to the new energy confidence capacity analysis method described in embodiment 1, at least including:
the new energy unit power generation module is used for constructing a new energy power generation model, calculating a new energy power generation power curve with equal interval time in a certain period based on the new energy power generation model, wherein the new energy power generation at least comprises photovoltaic power generation and wind power generation, the new energy power generation model at least comprises a photovoltaic power generation power model and a wind power generation power model, and the new energy power generation power curve at least comprises a photovoltaic power generation power curve and a wind power generation power curve;
the conventional unit power generation module is used for constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model;
the reliability level module is used for simulating and calculating the reliability level of the power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve; the method comprises the steps of,
the confidence capacity and capacity confidence coefficient module is used for carrying out repeated iterative computation on the reliability level of the electric power system containing the new energy source through a chord interception method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation.
The foregoing has described only the basic principles and preferred embodiments of the present invention, and many variations and modifications will be apparent to those skilled in the art in light of the above description, which variations and modifications are intended to be included within the scope of the present invention.

Claims (8)

1. The new energy confidence capacity analysis method is characterized by comprising the following steps of:
s1, constructing a new energy power generation model, and calculating a new energy power generation curve with equal interval time in a certain period based on the new energy power generation model, wherein the new energy power generation comprises photovoltaic power generation and wind power generation, the new energy power generation model comprises a photovoltaic power generation model and a wind power generation model, and the new energy power generation curve comprises a photovoltaic power generation curve and a wind power generation curve;
s2, constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model;
s3, simulating and calculating the reliability level of the power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve;
s4, performing repeated iterative computation on the reliability level of the power system containing the new energy through a chord cut method to obtain the confidence capacity and the capacity confidence coefficient of the photovoltaic power generation and the wind power generation; the step S4 specifically includes the following steps:
calculating the confidence capacity defined by the effective load capacity through the chord cut method, namely, under the condition of keeping the specified reliability level unchanged, adding additional load requirement delta L met by new energy, and calculating the confidence capacity C of photovoltaic power generation and wind power generation C And capacity confidence C P Confidence capacity of photovoltaic power generation C C And capacity confidence C P Confidence capacity C with wind power generation C And capacity confidence C P The calculation formula is general and specifically comprises the following steps:
C C ＝ΔL (1)
in the formula (8), C WP Rated installed capacity of the power system containing new energy;
s4.1, assume that the peak value of the original system load is L pk0 The given error is epsilon, wherein the original system refers to a power system without new energy, the reliability index R of the original system is calculated, and C is increased when the load curve is L WP New energy power systemReliability index R 1 Load curve of L+C WP Increasing the reliability index R of the new energy power system 2 ；
S4.2, find the passing point X (L pk0 ,R 1 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa a of the intersection point of the straight line A and the straight line f (x) =R ci1 ＝L+a-L pk0 Substituting the reliability index R calculated in (6) 3 ；
S4.3, judging |R 3 Magnitude relation of r| to epsilon: if |R 3 -R| > ε, then continue iteration, find the passing point X 1 (a,R 3 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa B of the intersection point of the straight line B and the straight line f (x) =R ci2 ＝L+b-L pk0 Substituting the reliability index R calculated in (6) 4 Then, step S4.4 is performed; otherwise, executing the step S4.5;
s4.4, judging |R 4 Magnitude relation of r| to epsilon: if |R 4 -r| > epsilon, repeating step S4.3 until the difference between the new reliability index and the reliability index of the original system is less than a given error epsilon;
s4.5, recording the corresponding peak load L at the end of iteration p The confidence capacity defined by the payload capacity that the new energy can additionally bear is: c (C) C ＝ΔL＝L p -L pk0 The capacity confidence is:
2. the method of claim 1, wherein in steps S1 and S2, a certain period is year round, and the equal interval is hour by hour.
3. The method according to claim 1, wherein step S1 comprises the following steps:
calculating a new energy power generation power curve according to the input sunlight intensity, temperature and wind speed data;
(1) The calculation of the photovoltaic power generation power is as follows:
in the formula (1), P PV For the actual output power of the photovoltaic cell, Y PV For the rated power of the photovoltaic cell under standard test conditions, f PV For the loss factor of the photovoltaic cell, R T For the actual light radiation intensity, R STC Is 1kW/m of light radiation intensity under standard test conditions 2 ，α P For the power temperature coefficient of the photovoltaic cell, T C T is the actual ambient temperature STC Is the ambient temperature under standard test conditions;
(2) The wind power generation power is calculated as follows:
in the formula (2), P t For wind power, A, B, C is a fan power characteristic curve parameter, different fans are slightly different, V ci 、V r 、V co 、P r The wind speed is the starting wind speed, the rated wind speed, the cut-out wind speed and the rated power of the fan;
(3) Wind power generation power model parameters:
in the formula (3), A, B, C is a fan power characteristic parameter in the formula (2).
4. The method according to claim 1, wherein in step S2, an IEEE RTS-79 test system or a TH-RTS2000 test system is used as a conventional unit for equivalent simulation.
5. The method according to claim 1, wherein step S2 comprises the following steps:
calculating a power generation curve of a conventional unit in a basic test system by using a state duration sampling method in a Monte Carlo method, and carrying out equivalence on conventional unit data of a simulated region according to the basic test system;
the conventional unit adopts a double-state model, namely a normal operation state and a fault state, and uses the normal operation duration t 1 And maintenance time t 2 Describing, the working time and the repairing time are subjected to exponential distribution, and the sampling method comprises the following steps:
t 1 ＝-t MTTF lnγ 1 (6)
t 2 ＝-t MTTR lnγ 2 (7)
in the formulas (4) and (5), gamma 1 、γ 2 For compliance [0,1 ] generated by a program]Random numbers uniformly distributed among t MTTF For average working time, t MTTR Is the average repair time;
and (3) calculating a conventional unit power generation curve in the basic test system according to the formulas (4) and (5), and adjusting according to conventional unit data of the simulated region to obtain a conventional unit power generation curve conforming to the parameters of the simulated region.
6. The method according to claim 1, wherein step S3 comprises the following steps:
calculating the reliability level of an electric power system containing new energy through sequential Monte Carlo simulation, wherein a reliability index is selected to be less than an expected EENS, and the calculation formula of the reliability index is as follows:
in the formula (6), N Y To simulate the total years, N is the state of lack of electricity, L j Power generation side at the jth power failure state, C j The load power in the j-th power failure state is equal to or more than 1 and equal to or less than N.
7. The method according to any one of claims 1-6, further comprising step S5: and changing the simulated installed capacity of the new energy, respectively calculating confidence capacities and capacity confidence degrees under different installed capacities of the new energy, and drawing a curve of the change of the confidence capacity of the new energy along with the penetrating power level of the new energy, wherein the penetrating power refers to the proportion of the installed capacity to the total load of the power system.
8. A new energy belief capacity analysis system, comprising:
the new energy unit power generation module is used for constructing a new energy power generation model, calculating a new energy power generation power curve with equal interval time in a certain period based on the new energy power generation model, wherein the new energy power generation comprises photovoltaic power generation and wind power generation, the new energy power generation model comprises a photovoltaic power generation power model and a wind power generation power model, and the new energy power generation power curve comprises a photovoltaic power generation power curve and a wind power generation power curve;
the conventional unit power generation module is used for constructing a conventional unit power generation model, and calculating a conventional unit power generation curve with equal interval time in a certain period based on the conventional unit power generation model;
the reliability level module is used for simulating and calculating the reliability level of the power system containing the new energy by adopting a time sequence Monte Carlo method according to the photovoltaic power generation power curve, the wind power generation power curve and the conventional unit power generation power curve; the method comprises the steps of,
the confidence capacity and capacity confidence coefficient module is used for carrying out repeated iterative computation on the reliability level of the electric power system containing the new energy source through a chord interception method to obtain the confidence capacity and capacity confidence coefficient of photovoltaic power generation and wind power generation;
specifically, the confidence capacity defined by the effective load capacity is calculated through the chord cut method, namely, the extra load requirement delta L met by new energy is added under the condition that the specified reliability level is kept unchanged, and the confidence capacity C of photovoltaic power generation and wind power generation is calculated C And capacity confidence C P Confidence capacity of photovoltaic power generation C C And capacity confidence C P Confidence capacity C with wind power generation C And capacity confidence C P The calculation formula is general and specifically comprises the following steps:
C C ＝ΔL (9)
in the formula (8), C WP Rated installed capacity of the power system containing new energy;
s4.1, assume that the peak value of the original system load is L pk0 The given error is epsilon, wherein the original system refers to a power system without new energy, the reliability index R of the original system is calculated, and C is increased when the load curve is L WP Reliability index R of new energy power system 1 Load curve of L+C WP Increasing the reliability index R of the new energy power system 2 ；
S4.2, find the passing point X (L pk0 ,R 1 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa a of the intersection point of the straight line A and the straight line f (x) =R ci1 ＝L+a-L pk0 Substituting the reliability index R calculated in (6) 3 ；
S4.3, judging |R 3 Magnitude relation of r| to epsilon: if |R 3 -R| > ε, then continue iteration, find the passing point X 1 (a,R 3 ) And Y (L) pk0 +C WP ,R 2 ) The new load curve L is calculated by the abscissa B of the intersection point of the straight line B and the straight line f (x) =R ci2 ＝L+b-L pk0 Substituting the reliability index R calculated in (6) 4 Then, step S4.4 is performed; otherwise, executing the step S4.5;
s4.4, judging |R 4 Magnitude relation of r| to epsilon: if |R 4 -r| > epsilon, repeating step S4.3 until the difference between the new reliability index and the reliability index of the original system is less than a given error epsilon;
s4.5, recording the corresponding peak load L at the end of iteration p New thenThe confidence capacity defined by the payload capacity that the energy source can additionally bear is: c (C) C ＝ΔL＝L p -L pk0 The capacity confidence is:
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