CN112434874A - Line capacity optimization method and system for renewable energy consumption - Google Patents

Line capacity optimization method and system for renewable energy consumption Download PDF

Info

Publication number
CN112434874A
CN112434874A CN202011403514.3A CN202011403514A CN112434874A CN 112434874 A CN112434874 A CN 112434874A CN 202011403514 A CN202011403514 A CN 202011403514A CN 112434874 A CN112434874 A CN 112434874A
Authority
CN
China
Prior art keywords
transmission line
representing
dynamic
capacity
overload
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011403514.3A
Other languages
Chinese (zh)
Other versions
CN112434874B (en
Inventor
丁肇豪
余开媛
贺元康
刘瑞丰
王进
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwest Branch Of State Grid Power Grid Co
North China Electric Power University
Original Assignee
Northwest Branch Of State Grid Power Grid Co
North China Electric Power University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwest Branch Of State Grid Power Grid Co, North China Electric Power University filed Critical Northwest Branch Of State Grid Power Grid Co
Priority to CN202011403514.3A priority Critical patent/CN112434874B/en
Publication of CN112434874A publication Critical patent/CN112434874A/en
Application granted granted Critical
Publication of CN112434874B publication Critical patent/CN112434874B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—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
    • 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
    • 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
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a method and a system for optimizing line capacity consumed by renewable energy. The method comprises the following steps: constructing a transmission line overload risk assessment model and a transmission line dynamic ampere capacity optimization model according to basic data of a transmission line corresponding to the power market; determining the constraint conditions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model; and calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk. The invention describes the uncertainty of the dynamic ampere capacity in the form of an uncertainty set, takes the overload risk of the transmission line as an evaluation index, optimizes the admissible area of the dynamic ampere capacity and can realize the maximum capacity under the minimum risk.

Description

Line capacity optimization method and system for renewable energy consumption
Technical Field
The invention relates to the field of transmission line ampere capacity, in particular to a method and a system for optimizing line capacity consumed by renewable energy.
Background
Along with the large-scale access of wind, light and other renewable energy power generation to a power grid, the uncertainty of the operation of a transmission line in a power system in China at the present stage is increasing day by day. The safe operation of the overhead transmission line is inseparable connected with the ambient temperature, and the ampere capacity of the transmission line adopted by the power grid during dispatching is the maximum allowable transmission current of the conductor calculated according to the extremely conservative and time-invariant static environment parameters and the maximum working temperature. Specifically, the environmental parameters specified by the regulations of the static ampacity in China are as follows: the temperature is 40 ℃, the wind speed is 0.5m/s, and the illumination intensity is 1000MW/m2. In general, these parameters have a very low probability of occurrence, so the values calculated from the above-mentioned environmental parameters are very conservative. If the ampere capacity of the transmission line is too low, the capacity of the transmission line of the power grid is insufficient, and then the phenomenon of 'nest electricity' on the wind power side or the phenomenon of 'nest electricity' on the peak shaving unit side on the conventional power supply side is caused. From an intuitive perspective, in order to improve the ampacity of the existing transmission line, a flexible alternating current transmission system can be installed or the existing transmission line can be expanded, so that the pressure of the overlow ampacity of the transmission line on the transmission of the power network is relieved to a certain extent, however, both the two methods need implementation cost and construction time.
When a power system operates, the dynamic ampere capacity of a transmission line is considered, and the structure optimization of a power transmission network is introduced, so that the power transmission capacity of the existing transmission line and power equipment can be fully exerted under the condition of not increasing the implementation of new equipment, the transmission capacity of the power transmission network is improved, and the reliability of a power grid is improved. Most of the existing documents are dedicated to research on using real-time or dynamic environment parameters in the scheduling process, and the current-carrying capacity is properly improved according to the real-time environment parameters so as to achieve a certain purpose. The accurate calculation of the dynamic ampere capacity is very important, and the influence of different microclimates on the dynamic ampere capacity of the transmission line is considered in the prior document, or an online fixed value model is adopted to realize that the dynamic ampere capacity is obtained by the current carrying of the transmission line and the temperature of a lead. However, due to uncertainty of factors such as weather, prediction errors of dynamic ampacity cannot be avoided, and if the prediction error of the dynamic ampacity of the transmission line is too large, transmission line overload can be caused, and even cascading failure of a power system can be caused seriously. Therefore, it is of great engineering importance to consider the uncertainty of the dynamic ampacity when assessing the risk of overload of the power system.
In fact, scholars have conducted risk assessments for assessing different problems present in the power system. And in part of researches, an improved equal-dispersion sampling Monte Carlo method is adopted to quantify the operation risk of the alternating current-direct current hybrid system under the background of large-scale wind power integration. There is also a literature research on a NSGA multi-objective optimization method, which adopts a cascade overload index to optimize a system safety index related to overload on line, and evaluates the optimal solution of a transmission line to obtain the magnitude of a risk value when overload occurs, however, dynamic ampacity of the line is not considered.
It should be noted that the conventional model evaluates the steady operation state of the power system, and there are also a few studies discussing the reasons for the overload of the lines in the system. The uncertainty of the dynamic ampere capacity prediction value is considered, so that the transmission line overload risk assessment has higher engineering application value.
Disclosure of Invention
The invention aims to provide a method and a system for optimizing line capacity of renewable energy consumption, which consider the uncertainty of dynamic ampere capacity of a transmission line and realize capacity maximization when the risk is minimum.
In order to achieve the purpose, the invention provides the following scheme:
a method for line capacity optimization for renewable energy consumption, comprising:
acquiring basic data of a transmission line corresponding to an electric power market;
constructing a transmission line overload risk assessment model and a transmission line dynamic ampere capacity optimization model according to the basic data;
determining the constraint conditions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model;
and calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
Optionally, the expression of the transmission line overload risk assessment model is as follows:
Figure BDA0002813180120000021
wherein Risk is transmission line overload Risk assessment index, FlRepresenting the static ampacity, Q, of the transmission lineltThe overload capacity of the transmission line is shown, l represents the l-th section of the transmission line, and t represents the selected time interval.
Optionally, the expression of the transmission line overload Risk assessment index Risk is as follows:
Figure BDA0002813180120000031
wherein, clRepresenting transmission line overload penalty factor, deltaltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure BDA00028131801200000310
representing predicted dynamic Ampere Capacity, Pt(δlt) Representing a dynamic ampacity prediction error probability distribution function.
Optionally, the expression of the transmission line dynamic ampacity optimization model is as follows:
Figure BDA0002813180120000032
wherein the content of the first and second substances,F ltrepresenting the lower bound of the dynamic Ampere Capacity without risk, Δ FltRepresenting the amount of overrun, v, of the dynamic ampacityltThe uncertainty of the dynamic ampere capacity is represented and is a Boolean variable, phi represents a decision variable set, l represents an l-th transmission line, and t represents a selected time interval.
Optionally, the constraint conditions of the transmission line overload risk assessment model are as follows:
Figure BDA0002813180120000033
Figure BDA0002813180120000034
wherein Q isltWhich represents the amount of overload of the transmission line,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure BDA0002813180120000035
representing predicted dynamic ampacity, l representing the l-th transmission line, t representing selected time interval, dltj、eltjEach represents a coefficient representing the jth segment in the time period t risk linearization.
Optionally, the constraints of the transmission line dynamic ampacity optimization model are as follows:
Figure BDA0002813180120000036
Figure BDA0002813180120000037
Figure BDA0002813180120000038
Figure BDA0002813180120000039
Figure BDA0002813180120000041
Figure BDA0002813180120000042
Figure BDA0002813180120000043
Figure BDA0002813180120000044
Figure BDA0002813180120000045
Figure BDA0002813180120000046
wherein the content of the first and second substances,
Figure BDA0002813180120000047
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltRepresenting the amount of overrun, p, of the dynamic ampacityltRepresenting transmission power of electric power lines, pgtIs representative of the power of the generator set,
Figure BDA0002813180120000048
representing the upper/lower bound of the genset output,
Figure BDA0002813180120000049
indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure BDA00028131801200000410
the transmission line admittance is represented as a function of,
Figure BDA00028131801200000411
and thetan2Respectively representing the phase angles theta of the inflow node and the outflow node at two ends of the power line in the time period tref，tIs a reference node phase value, vltRepresenting the dynamic ampacity uncertainty, is a Boolean variable, ΓTRepresenting transmission line time smoothing parameters, ΓLRepresenting transmission line space average parameters, d representing load, g number of generator sets, psigRepresenting a set of generator sets, n representing a node, n1Denotes an ingress node, n2Representing egress nodes, N representing the number of nodes, o1Represents the set of ingress nodes, o2Representing a set of egress nodes.
The invention also provides a line capacity optimization system for renewable energy consumption, which comprises:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the electric power market;
the model building module is used for building a transmission line overload risk evaluation model and a transmission line dynamic ampere capacity optimization model according to the basic data;
the constraint condition determining module is used for determining the constraint conditions of the transmission line overload risk evaluation model and the transmission line dynamic ampere capacity optimization model;
and the solving module is used for calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method and a system for optimizing line capacity consumed by renewable energy. The method comprises the following steps: constructing a transmission line overload risk assessment model and a transmission line dynamic ampere capacity optimization model according to basic data of a transmission line corresponding to the power market; determining the constraint conditions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model; and calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk. The invention describes the uncertainty of the dynamic ampere capacity in the form of an uncertainty set, takes the overload risk of the transmission line as an evaluation index, optimizes the admissible area of the dynamic ampere capacity and can realize the maximum capacity under the minimum risk.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for line capacity optimization for renewable energy consumption in accordance with an embodiment of the present invention;
FIG. 2 is a schematic view of a 5-node test system topology employed in the present invention;
FIG. 3 is a schematic diagram of a 24-node test system topology employed in the present invention;
fig. 4 is a schematic diagram of a test result considering the influence of a unit start-stop mode on the ampere capacity zero overload lower bound of a transmission line in the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for optimizing line capacity of renewable energy consumption, which consider the uncertainty of dynamic ampere capacity of a transmission line and realize capacity maximization when the risk is minimum
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, a method for optimizing line capacity for renewable energy consumption includes the following steps:
step 101: and acquiring basic data of the transmission line corresponding to the electric power market.
Step 102: and constructing a transmission line overload risk assessment model and a transmission line dynamic ampere capacity optimization model according to the basic data.
Step 103: and determining the constraint conditions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model.
Step 104: and calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
The specific principle of the invention is as follows:
in the operation scheduling process of the power system, aiming at the power system operation problem considering a DLR mechanism, the invention expresses the transmission line ampere capacity uncertainty caused by meteorological factors by a box set, combines the proposed transmission line overload risk index, and constructs a transmission line overload risk assessment model based on two-stage robust optimization, which comprises the following implementation steps:
(1) the method for acquiring the transmission line basic data corresponding to the electric power market comprises the following steps: the power system corresponds to power grid system topology data, load demand of each node in the power grid system and basic data of each generator set; wherein the power system corresponding power grid system topology data comprises: the mutual connection relation between the nodes and lines of the corresponding power grid system in the power system and the active power flow limit of each power transmission line are determined; the basic data of each generator set comprises: maximum/minimum generating capacity of the unit, maximum/minimum climbing rate of the unit and operation cost data of the unit;
(2) selecting a line of the power system using a dynamic ampere capacity mechanism as L, selecting a time period as T, and respectively recording the total number of the lines, the total number of nodes and the total number of the time period in the system as L, B and T;
(3) initializing the system, setting an initial value k of iteration times to be 0, and setting a convergence error to be a very small constant epsilon;
(4) and constructing a transmission line overload risk evaluation model considering a dynamic ampere capacity mechanism, evaluating the overload risk of the system line and obtaining a line ampere capacity lower bound corresponding to no overload loss. The method comprises the following specific steps:
constructing an objective function of a transmission line overload risk assessment model, wherein the expression is as follows:
Figure BDA0002813180120000071
risk is a transmission line overload Risk assessment index, and the mathematical expression of the Risk is as follows:
Figure BDA0002813180120000072
in the formula, FlRepresenting the static ampacity, Q, of the transmission lineltIndicating the overload of the transmission line, l indicating the l-th transmission line, t indicating the selected time interval, clRepresenting transmission line overload penalty factor, deltaltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure BDA0002813180120000073
representing predicted dynamic Ampere Capacity, Pr(δlt) Probability distribution function representing prediction error of dynamic ampere capacityAnd (4) counting.
The expression of the transmission line dynamic ampacity optimization model is as follows:
Figure BDA0002813180120000074
wherein the content of the first and second substances,F ltrepresenting the lower bound of the dynamic Ampere Capacity without risk, Δ FltRepresenting the amount of overrun, v, of the dynamic ampacityltRepresenting the dynamic ampacity uncertainty as a boolean variable, Φ ═ pgt，plt，θnt，ΔFltRepresents a min problem decision variable set.
The constraint conditions for considering the transmission line overload risk assessment model are determined as follows:
Figure BDA0002813180120000075
Figure BDA0002813180120000076
wherein Q isltWhich represents the amount of overload of the transmission line,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure BDA0002813180120000077
representing predicted dynamic ampacity, l representing the l-th transmission line, t representing selected time interval, dltj、eltjEach represents a coefficient representing the jth segment in the time period t risk linearization.
The constraints for considering the dynamic ampacity optimization model of the transmission line are determined as follows:
Figure BDA0002813180120000081
Figure BDA0002813180120000082
Figure BDA0002813180120000083
Figure BDA0002813180120000084
Figure BDA0002813180120000085
Figure BDA0002813180120000086
Figure BDA0002813180120000087
Figure BDA0002813180120000088
Figure BDA0002813180120000089
Figure BDA00028131801200000810
in the above formula, the auxiliary constraint in the linearized overload risk assessment index is (4), the line capacity constraint considering the dynamic ampacity mechanism is (5), the transmission line capacity constraints are (6) and (7), the generator set output constraint is (8), the generator set climbing constraint is (9) and (10), the power system node balance constraint is (11), the direct current flow equation constraint is (12), the reference node phase angle constraint is (13), the transmission line time smoothing constraint is (14), and the transmission line space average constraint is (15). Wherein the content of the first and second substances,
Figure BDA00028131801200000811
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltRepresenting the amount of overrun, p, of the dynamic ampacityltRepresenting transmission power of electric power lines, pgtIs representative of the power of the generator set,
Figure BDA00028131801200000812
representing the upper/lower bound of the genset output,
Figure BDA00028131801200000813
indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure BDA00028131801200000814
the transmission line admittance is represented as a function of,
Figure BDA00028131801200000815
and thetan2Respectively representing the phase angles theta of the inflow node and the outflow node at two ends of the power line in the time period tref，tIs a reference node phase value, vltRepresenting the dynamic ampacity uncertainty, is a Boolean variable, ΓTRepresenting transmission line time smoothing parameters, ΓLRepresenting transmission line space average parameters, d representing load, g number of generator sets, ΨgRepresenting a set of generator sets, n representing a node, n1Denotes an ingress node, n2Representing egress nodes, N representing the number of nodes, o1Represents the set of ingress nodes, o2Representing a set of egress nodes.
For ease of presentation, the transmission line overload risk assessment model is represented herein in a compact form as follows:
Figure BDA0002813180120000091
solving the model, and recording the optimal solution of c as c(k)The Risk optimal value is Risk(k)。
(5) In the step (4), the situation that the dynamic ampere capacity possibly deviates from the predicted value in the running process of the power system is considered. In the model constraint, the mathematical expression is in the form of a two-layer max-min model, which is named herein as a dynamic ampacity admissible criterion, i.e., assuming that the dynamic ampacity uncertainty is intended to maximize the dynamic ampacity overrun amount in the power system operational rescheduling phase. The max-min model is made to be a transmission line overload discrimination sub-problem.
Solving the transmission line discrimination subproblem model, and recording the optimal solution of v as v(k+1)The optimal value of the target function gamma is gamma(k+1)；
(6) If gamma is(k+1)If epsilon is less than epsilon, the step is terminated and c is output(k)(ii) a Otherwise, adding the vector and the corresponding constraint, making k equal to k +1, and returning to the step (4).
(7) And (4) finishing iteration, recording the overload risk of the transmission line caused by the DLR uncertainty and the maximum allowable deviation of the ampacity of each transmission line, and ending the method.
The invention will be described in detail below with reference to model topology and example data. It should be emphasized that the examples, which are set forth below, are intended to be illustrative only and are not intended to limit the scope or application of the invention. The example model introduced in the invention is composed of a 5-node power system network (test system I) and a 24-node power system network (test system II), as shown in FIGS. 2-3. Simulation tests were performed in MATLAB.
The system operating cost is the cost of power generation when a DLR mechanism (dynamic ampacity mechanism) is employed. After the DLR mechanism is applied, the line capacity is generally higher than the static value of the DLR mechanism, and the blocking phenomenon of a power system is expected to be relieved, so that the running cost of the system is reduced. Applying this mechanism may introduce additional overload risks due to the inherent uncertainty of DLR. The impact of the number of lines using the DLR mechanism on the operation of the system will first be analyzed using test system I. The marginal net gain represents the difference between the reduction of the system operation cost and the increase of the overload risk when the line set of the DLR mechanism changes. The test numbers are 1-6, wherein the number of lines applying the DLR mechanism is 1-6, and the specific line numbers and simulation results are shown in Table 1. Unless otherwise stated, the line ampacity reference value is 100MW hereinafter.
TABLE 1
Figure BDA0002813180120000101
As can be seen from table 1, the line overload risk increases with the number of lines using the DLR mechanism, and the system operation cost gradually decreases. Further, the value of the marginal net gain of the system does not increase monotonically with the number of lines to which the DLR mechanism is applied. In test 5, the marginal net gain brought by applying the DLR mechanism to the system is the largest, and in test 2, the marginal net gain is negative, that is, the operation condition of the system is deteriorated by applying the DLR mechanism with the increased number of lines. In summary, in practical application, the number and specific number of lines using the DLR mechanism need to be set, so as to maximize the net benefit of the DLR mechanism on system operation.
In order to test the influence of the unit combination on the overload risk of the transmission line, different unit combination schemes are set in a test system II, and four working conditions are provided: starting up the machine set; generator G1、G2Respectively closing; generator G1、G2All are closed, the front section is carried, the test numbers are respectively 7-10, and the simulation result is shown in figure 4.
The invention also provides a line capacity optimization system for renewable energy consumption, which comprises:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the electric power market;
the model building module is used for building a transmission line overload risk evaluation model and a transmission line dynamic ampere capacity optimization model according to the basic data;
the constraint condition determining module is used for determining the constraint conditions of the transmission line overload risk evaluation model and the transmission line dynamic ampere capacity optimization model;
and the solving module is used for calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (7)

1. A method for optimizing line capacity for renewable energy consumption, comprising:
acquiring basic data of a transmission line corresponding to an electric power market;
constructing a transmission line overload risk assessment model and a transmission line dynamic ampere capacity optimization model according to the basic data;
determining the constraint conditions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model;
and calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
2. The method for line capacity optimization for renewable energy consumption according to claim 1, wherein the transmission line overload risk assessment model is expressed as follows:
Figure FDA0002813180110000011
wherein Risk is transmission line overload Risk assessment index, FlRepresenting the static ampacity, Q, of the transmission lineltThe overload capacity of the transmission line is shown, l represents the l-th section of the transmission line, and t represents the selected time interval.
3. The method for optimizing line capacity for renewable energy consumption according to claim 2, wherein the expression of the transmission line overload Risk assessment index Risk is as follows:
Figure FDA0002813180110000012
wherein, ClRepresenting transmission line overload penalty factor, deltaltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure FDA0002813180110000013
representing predicted dynamic Ampere Capacity, Pr(δlt) Representing a dynamic ampacity prediction error probability distribution function.
4. The method of line capacity optimization for renewable energy consumption of claim 1, wherein the transmission line dynamic ampacity optimization model is expressed as follows:
Figure FDA0002813180110000014
wherein the content of the first and second substances,F ltrepresenting the lower bound of the dynamic Ampere Capacity without risk, Δ FltRepresenting the amount of overrun, v, of the dynamic ampacityltThe uncertainty of the dynamic ampere capacity is represented and is a Boolean variable, phi represents a decision variable set, l represents an l-th transmission line, and t represents a selected time interval.
5. The method for optimizing line capacity for renewable energy consumption according to claim 1, wherein the transmission line overload risk assessment model is constrained by the following conditions:
Figure FDA0002813180110000021
Figure FDA0002813180110000022
wherein Q isltWhich represents the amount of overload of the transmission line,F ltrepresenting the lower bound of dynamic ampacity without risk,
Figure FDA0002813180110000023
representing predicted dynamic ampacity, l representing the l-th transmission line, t representing selected time interval, dltj、eltjEach represents a coefficient representing the jth segment in the time period t risk linearization.
6. The method of line capacity optimization for renewable energy consumption according to claim 1, wherein the constraints of the transmission line dynamic ampacity optimization model are as follows:
Figure FDA0002813180110000024
Figure FDA0002813180110000025
Figure FDA0002813180110000026
Figure FDA0002813180110000027
Figure FDA0002813180110000028
Figure FDA0002813180110000029
Figure FDA00028131801100000210
Figure FDA00028131801100000211
Figure FDA00028131801100000212
Figure FDA00028131801100000213
wherein the content of the first and second substances,
Figure FDA0002813180110000031
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltRepresenting the amount of overrun, p, of the dynamic ampacityltRepresenting transmission power of electric power lines, pgtIs representative of the power of the generator set,
Figure FDA0002813180110000032
representing the upper/lower bound of the genset output,
Figure FDA0002813180110000033
indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure FDA0002813180110000034
the transmission line admittance is represented as a function of,
Figure FDA0002813180110000035
and thetan2Respectively representing the phase angles theta of the inflow node and the outflow node at two ends of the power line in the time period tref，tIs a reference node phase value, vltRepresenting the dynamic ampacity uncertainty, is a Boolean variable, ΓTRepresenting transmission line time smoothing parameters, ΓLRepresenting transmission line space average parameters, d representing load, g number of generator sets, psigRepresenting a set of generator sets, n representing a node, n1Denotes an ingress node, n2Representing egress nodes, N representing the number of nodes, o1Represents the set of ingress nodes, o2Representing a set of egress nodes.
7. A line capacity optimization system for renewable energy consumption, comprising:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the electric power market;
the model building module is used for building a transmission line overload risk evaluation model and a transmission line dynamic ampere capacity optimization model according to the basic data;
the constraint condition determining module is used for determining the constraint conditions of the transmission line overload risk evaluation model and the transmission line dynamic ampere capacity optimization model;
and the solving module is used for calculating the optimal solutions of the transmission line overload risk assessment model and the transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
CN202011403514.3A 2020-12-02 2020-12-02 Line capacity optimization method and system for renewable energy consumption Active CN112434874B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011403514.3A CN112434874B (en) 2020-12-02 2020-12-02 Line capacity optimization method and system for renewable energy consumption

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011403514.3A CN112434874B (en) 2020-12-02 2020-12-02 Line capacity optimization method and system for renewable energy consumption

Publications (2)

Publication Number Publication Date
CN112434874A true CN112434874A (en) 2021-03-02
CN112434874B CN112434874B (en) 2024-05-10

Family

ID=74692251

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011403514.3A Active CN112434874B (en) 2020-12-02 2020-12-02 Line capacity optimization method and system for renewable energy consumption

Country Status (1)

Country Link
CN (1) CN112434874B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105184490A (en) * 2015-09-09 2015-12-23 贵州电网公司电力调度控制中心 Power grid dispatching operation process risk auxiliary pre-control system
CN105956783A (en) * 2016-05-15 2016-09-21 国家电网公司 Power transmission line risk assessment method
CN106096779A (en) * 2016-06-13 2016-11-09 国家电网公司 A kind of power transmission and distribution cost analysis, risk evaluation model and risk structure optimization method
EP3096428A1 (en) * 2015-05-18 2016-11-23 General Electric Technology GmbH Dynamic line rating determination apparatus and associated method
CN106815770A (en) * 2015-11-27 2017-06-09 中国电力科学研究院 It is a kind of to consider abundance and security of system the methods of risk assessment of system
CN109818347A (en) * 2018-11-30 2019-05-28 华北电力大学 A kind of appraisal procedure of electric system wind electricity digestion capability
CN110783963A (en) * 2019-09-19 2020-02-11 广州供电局有限公司 Power system optimal scheduling method and device, computer equipment and storage medium
US20200212710A1 (en) * 2017-09-04 2020-07-02 Southeast University Method for predicting operation state of power distribution network with distributed generations based on scene analysis
CN113224751A (en) * 2021-04-30 2021-08-06 华北电力大学 Method for optimizing line capacity in high wind power ratio system

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3096428A1 (en) * 2015-05-18 2016-11-23 General Electric Technology GmbH Dynamic line rating determination apparatus and associated method
CN105184490A (en) * 2015-09-09 2015-12-23 贵州电网公司电力调度控制中心 Power grid dispatching operation process risk auxiliary pre-control system
CN106815770A (en) * 2015-11-27 2017-06-09 中国电力科学研究院 It is a kind of to consider abundance and security of system the methods of risk assessment of system
CN105956783A (en) * 2016-05-15 2016-09-21 国家电网公司 Power transmission line risk assessment method
CN106096779A (en) * 2016-06-13 2016-11-09 国家电网公司 A kind of power transmission and distribution cost analysis, risk evaluation model and risk structure optimization method
US20200212710A1 (en) * 2017-09-04 2020-07-02 Southeast University Method for predicting operation state of power distribution network with distributed generations based on scene analysis
CN109818347A (en) * 2018-11-30 2019-05-28 华北电力大学 A kind of appraisal procedure of electric system wind electricity digestion capability
CN110783963A (en) * 2019-09-19 2020-02-11 广州供电局有限公司 Power system optimal scheduling method and device, computer equipment and storage medium
CN113224751A (en) * 2021-04-30 2021-08-06 华北电力大学 Method for optimizing line capacity in high wind power ratio system

Non-Patent Citations (9)

* Cited by examiner, † Cited by third party
Title
丁肇豪等: "考虑动态输送容量机制的电力传输线过载风险评估", 《电力***自动化》, 21 December 2020 (2020-12-21) *
任丽佳;江秀臣;盛戈;曾奕;吴小辰;胡玉峰;: "输电线路允许输送容量的混沌预测", 中国电机工程学报, no. 25, 5 September 2009 (2009-09-05) *
唐建兴;唐冠军;王国松;朱灵子;凌超;马覃峰;钟亮民;: "一种输电线的动态载流量评估方法及应用", 广东电力, no. 04, 25 April 2020 (2020-04-25) *
曲正伟;王京波;张坤;王云静;郑磊;: "考虑不确定性成本的含风电场群电力***短期优化调度", 电力自动化设备, no. 04, 7 April 2016 (2016-04-07) *
李军辉;贾思棋;杜冬梅;丁亮亮;何青;刘彬;: "考虑径向温差的架空输电导线的动态增容分析", 湖南大学学报(自然科学版), no. 04, 25 April 2020 (2020-04-25) *
胡剑;王建;熊小伏;乌睿;: "计及线路动态电热特性的交直流混联电网过载控制策略", 电力***保护与控制, no. 07, 1 April 2020 (2020-04-01) *
袁贝尔;应展烽;齐保军;张旭东;冯凯;: "高压碳纤维复合芯导线输电线路热过载运行的风险评估方法", 电力***自动化, no. 01, 10 January 2018 (2018-01-10) *
郭振强;张勇;朱超;王永庆;刘云云;傅金柱;: "架空高压线增容技术研究进展", 电工电气, no. 05, 15 May 2020 (2020-05-15) *
钱海;王奇;陈翔宇;胡军;: "考虑电网多元风险的输电断面动态增容调度分配方案", 中国电力, no. 10, 5 October 2015 (2015-10-05) *

Also Published As

Publication number Publication date
CN112434874B (en) 2024-05-10

Similar Documents

Publication Publication Date Title
Bihari et al. A comprehensive review of microgrid control mechanism and impact assessment for hybrid renewable energy integration
You et al. Comparative assessment of tactics to improve primary frequency response without curtailing solar output in high photovoltaic interconnection grids
Torreglosa et al. Decentralized energy management strategy based on predictive controllers for a medium voltage direct current photovoltaic electric vehicle charging station
CN107679658B (en) Power transmission network planning method under high-proportion clean energy access
JP4306760B2 (en) Distributed power supply
CN110768263A (en) Transient voltage control method
CN113516278A (en) Active power distribution network multi-time scale active and reactive power coordinated optimization scheduling method and system
CN115017854A (en) Method for calculating maximum allowable capacity of DG (distributed generation) of power distribution network based on multidimensional evaluation index system
Cano et al. Comparative analysis of HESS (battery/supercapacitor) for power smoothing of PV/HKT, simulation and experimental analysis
CN103178522A (en) Network voltage stability and continuity load flow calculation method considering characteristics of distributed generation
Lee et al. Method for determining the droop coefficients of hybrid multi-terminal HVDC systems to suppress AC voltage fluctuations
CN112434874A (en) Line capacity optimization method and system for renewable energy consumption
Xue et al. A fully distributed ADP algorithm for real-time economic dispatch of microgrid
CN113224751A (en) Method for optimizing line capacity in high wind power ratio system
CN114971069A (en) Power system interval optimization scheduling method considering source load uncertainty and frequency safety
CN115345350A (en) Alternating current-direct current hybrid power distribution network planning method, medium and system
Sharma et al. Improved energy management strategy for prosumer buildings with renewable energy sources and battery energy storage systems
CN112510685B (en) Urban power grid risk avoidance prediction control method and device
Saidi et al. Energy management strategy for hybrid power systems based on moving average filters and power forecasting
Alamelu et al. Optimal siting and sizing of UPFC control settings in grid integrated wind energy conversion systems
Brown Evaluation of integration of pumped storage units in an isolated network
CN113283105B (en) Energy internet distributed optimal scheduling method considering voltage safety constraint
CN113139152B (en) Method and system for evaluating adaptability of instantaneous load of direct-current distribution network of offshore oil production platform
CN113381400B (en) Method and device for evaluating capacity of storing and storing new energy
CN112598278B (en) Power and electric quantity balance judgment method and device based on flexible calculation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant