CN112039058A - Unit combination method, system, medium and device based on wind power prediction interval - Google Patents
Unit combination method, system, medium and device based on wind power prediction interval Download PDFInfo
- Publication number
- CN112039058A CN112039058A CN202010831986.2A CN202010831986A CN112039058A CN 112039058 A CN112039058 A CN 112039058A CN 202010831986 A CN202010831986 A CN 202010831986A CN 112039058 A CN112039058 A CN 112039058A
- Authority
- CN
- China
- Prior art keywords
- wind power
- scene
- interval
- unit
- turbine generator
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 78
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 27
- 238000010248 power generation Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 33
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000009827 uniform distribution Methods 0.000 claims description 3
- 239000003245 coal Substances 0.000 claims description 2
- 238000010977 unit operation Methods 0.000 claims 2
- 230000009467 reduction Effects 0.000 abstract description 16
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000005457 optimization Methods 0.000 description 23
- 238000010586 diagram Methods 0.000 description 11
- 230000009977 dual effect Effects 0.000 description 11
- 238000004590 computer program Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 238000009826 distribution Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000003860 storage Methods 0.000 description 5
- 238000005315 distribution function Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 238000003462 Bender reaction Methods 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000010205 computational analysis Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
- H02J2300/28—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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Power Engineering (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a unit combination method, a system, a medium and equipment based on a wind power prediction interval, which belong to the technical field of wind power generation and comprise the following steps: acquiring operation state data of each wind turbine generator in a wind power plant; obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result, and solving a target function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the obtained operation state data to obtain an optimized wind turbine generator combination strategy; on the basis of wind power interval prediction, a wind power plant-containing random unit combination model based on a scene method is established through a scene generation and improved rapid backward reduction technology, and optimized scheduling is performed by adopting a Lagrange relaxation algorithm; the reliability of the model is high, the solving is quick, and the robustness and the economical efficiency of the obtained unit combination mode are higher.
Description
Technical Field
The present disclosure relates to the field of wind power generation technologies, and in particular, to a method, a system, a medium, and a device for combining units based on a wind power prediction interval.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, with the continuous promotion of global energy structure revolution and the continuous development of new energy technology, wind power generation with higher technology maturity becomes one of the important measures for realizing clean and alternative energy. However, due to the uncertainty of wind energy itself, the increasing wind power integration capacity thereof makes the safe and economic operation of the traditional power system face a huge difficult problem. The popularity of the wind turbine generator is greatly increased, the phenomenon that the wind turbine generator is difficult to grid in an electric power system is gradually shown, the wind abandoning proportion is increased year by year, and a serious challenge is brought to how to furthest absorb the wind power. The optimal scheduling of the power system can reduce the total operation cost to the minimum, and the reliability of the power system after wind power integration is ensured while the wind energy economic benefit is exerted to the maximum extent. Therefore, the research on the unit combination model of the wind power randomness plays an important role in improving the reliability and the economic operation level of the power system.
For effectively dealing with wind power randomness, in the aspect of combined research of wind power-containing units, the traditional deterministic optimization model cannot meet the requirement of actual production, so that the search for an accurate and efficient nondeterministic optimization model becomes the focus of attention of scholars at home and abroad. From the algorithm perspective, the solution method of the optimal investment is divided into three categories, namely a heuristic algorithm, a mathematical optimization algorithm and an intelligent algorithm; from the modeling perspective, the uncertainty model proposed by the current research can be generally classified into several categories, such as random programming, fuzzy analysis, robust optimization, interval optimization, and the like.
The stochastic programming method mainly utilizes a probability density function or an accumulative distribution function and the like in wind power output prediction information to depict uncertain quantity, and the stochastic programming method broadly comprises a scene method and an opportunity constraint method.
The scene method is the most common method for describing uncertain variables, simulates the uncertain variables by multiple scenes, and can effectively process random phenomena in actual production. Taking wind power output as an example, one scene represents a wind power output sequence, and the scene method is to represent all possible wind power outputs as much as possible by generating a large number of scenes and further calculate an expected value to make a decision. Researchers assume that wind power obeys normal distribution, and Monte Carlo simulation is adopted to generate scenes. Some researchers adopt a Latin hypercube sampling method to generate scenes, and compared with the traditional Monte Carlo sampling, the method effectively reduces the sampling scale required by reaching the specified precision and improves the sampling efficiency. But the scenes generated by the simulation are almost impossible to contain all scenes and the computational cost of a large number of scenes is high. Therefore, many researches propose a scene reduction method to generate an approximate scene set of the original scene set, and the currently common scene reduction method mainly uses a fast forward selection method and a synchronous back substitution reduction method.
The opportunistic constraint method utilizes a probabilistic form constraint to process random variables contained in the constraint. In dealing with random variables, the decision to be made is allowed to fail the constraint to some extent, but it must be guaranteed that the probability of the constraint being met is greater than a certain confidence level, applicable to the case where the decision is made before the realization of the random variable is observed. And the confidence level can be set according to the actual experience of the decision maker or the risk preference, and is often applied to the reliability field and the risk management.
The fuzzy programming describes uncertain variables based on fuzzy sets and membership functions, and the current common models mainly comprise fuzzy expected value models, fuzzy opportunity constraint programming and fuzzy related opportunity constraints. Researchers consider the rotary standby cost expectation and the condition risk value in the objective function, establish a multi-objective optimization model of fuzzy risk decision, and determine an optimal rotary standby plan by adopting a fuzzy decision theory.
Robust optimization requires a decision maker to optimally select an optimal decision scheme according to the worst implementation situation of each decision scheme, so that the obtained result can be adapted to all scenes, but the worst scenes occur rarely, namely, the optimal decision under the worst case of uncertain parameters is realized, which leads to the situation that the obtained solution is relatively conservative under the normal condition. At present, many researches on the aspect of improving the conservatism of robust optimization are carried out, such as a distributed robust optimization model and multi-interval uncertainty, and the uncertainty set is changed by increasing the description of uncertainty characteristics. Researchers build an uncertain set by utilizing Kullback-Leibler divergence, build a distribution robust optimization model, and solve a unit combination by adopting a Benders decomposition method. The above measures effectively reduce the conservative degree of robust optimization, but the conservative degree is still hard to measure.
The interval optimization uses the interval to represent the variation range of the uncertain variable, namely only the upper and lower bound information of the uncertain quantity is needed, and the accurate fuzzy membership function or probability distribution of the uncertain variable is not needed to be determined. The interval optimization has small dependence on samples, and compared with the random planning, the interval optimization does not need to sample or simulate uncertain quantities, and the calculated quantity is greatly reduced. In recent years, interval optimization receives more and more attention, and is expanded to be applied to many fields to solve optimization decisions containing uncertain parameters. Some researchers describe the uncertain quantity in the economic dispatching model containing wind power by interval quantity to obtain the interval form of the solution of the economic dispatching.
The inventor of the present disclosure finds that, although the optimization algorithm and the optimization model have respective characteristics, they have some disadvantages, such as dimension disaster problem of the dynamic programming algorithm, troublesome selection of parameters of the simulated annealing algorithm, difficulty in solving the opportunistic constraint programming, and conservative robust optimization result.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a unit combination method, a system, a medium and equipment based on a wind power prediction interval.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a unit combination method based on a wind power prediction interval.
A unit combination method based on a wind power prediction interval comprises the following steps:
acquiring operation state data of each wind turbine generator in a wind power plant;
obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
The second aspect of the disclosure provides a unit combination system based on a wind power prediction interval.
A unit combination system based on a wind power prediction interval comprises:
a data acquisition module configured to: acquiring operation state data of each wind turbine generator in a wind power plant;
a scene acquisition module configured to: obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
a unit combination module configured to: and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
A third aspect of the present disclosure provides a medium having a program stored thereon, where the program is executed by a processor to implement the steps in the method for assembling a unit based on a wind power prediction interval according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an apparatus, including a memory, a processor, and a program stored on the memory and executable on the processor, where the processor implements the steps in the method for combining units based on the wind power prediction interval according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the system, the medium or the equipment, on the basis of wind power interval prediction, a wind power plant-containing random unit combination model based on a scene method is established through a scene generation and improved rapid backward reduction technology, optimized scheduling is performed through a Lagrange relaxation algorithm, the model is high in reliability and rapid in solving, and the obtained optimized unit combination mode is high in robustness and economy.
2. According to the method, the system, the medium or the equipment, the unit combination problem is solved by adopting a Lagrange relaxation algorithm, multiplier initialization is carried out according to a heuristic sorting method and an equal-consumption micro-increment rate principle, the decoupled single-unit problem is solved by a dynamic programming method, the Lagrange multiplier is corrected by utilizing a secondary gradient method, and the combined result obtained by solving is low in cost and high in stability.
3. According to the method, the system, the medium or the equipment, the random fluctuation of the wind power is processed by adopting a scene method, the combined model of the random unit of the power system including the wind power plant is established, a representative scene is obtained by utilizing scene generation and an improved rapid backward reduction technology, the random fluctuation of the wind power is more accurately expressed, and the stability of the combined model is improved.
4. According to the method, the system, the medium or the equipment, the Kantorovich matrix is adopted for scene reduction, so that the probability distance between the scene set after the scene set is reduced and the scene set before the scene set is reduced is minimum, a new probability value is given to the selected representative scene, and the accuracy and the efficiency of the scene reduction are greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic flow chart of a unit combination method based on a wind power prediction interval according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic diagram of a solution to the dual problem and the original problem provided in embodiment 1 of the present disclosure.
Fig. 3 is a flowchart of the lagrangian relaxation algorithm provided in embodiment 1 of the present disclosure.
Fig. 4 is a schematic diagram of start-stop results obtained by the ILR according to embodiment 1 of the present disclosure.
Fig. 5 is a schematic diagram of start-stop results obtained by LR provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a unit combination method based on a wind power prediction interval, including the following steps:
acquiring operation state data of each wind turbine generator in a wind power plant;
and obtaining an operation scene of the wind turbine generator according to the wind power interval prediction result, and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the obtained operation state data to obtain an optimized wind turbine generator combination strategy.
More specifically, the following are included:
s1: unit combination problem solved by Lagrange relaxation method
The unit combination of the power system is to reasonably arrange the operation modes of the generator sets according to the load prediction curve and the quotation of the generator sets, meet different load requirements of each time period in a scheduling period, and determine the output power of each unit on the premise of various global constraints and independent constraints, so that the total operation cost is minimum.
The unit combination is the optimal input problem between the economic allocation and the production scheduling, and the determined scheduling period can be 1 hour, also can be 24 hours, and even can be 1 week.
S1.1: fundamental principle of Lagrange relaxation algorithm
The Lagrange relaxation algorithm is an approximate measure for simplifying calculation, can simplify the problem, reduce the calculation amount of solution, improve the calculation efficiency, and improve the accuracy and the feasibility of the calculation result, thereby obtaining greater economic benefit; therefore, the present embodiment will describe a solution to the problem of nonlinear programming with integer variables using the lagrangian relaxation method.
Various constraint conditions are directly added into an objective function needing to be solved by a Lagrange relaxation algorithm, and a Lagrange multiplier lambda is introduced into the objective functiont、μtThe following augmentation functions can be constructed:
further dual problems are obtained:
from equation (2), the dual problem is a two-level optimization problem that includes the maximum and minimum problems. The method is characterized in that a dynamic programming method is adopted to solve the problem of the lower layer, the dual problem is maximized by the upper layer, iteration is carried out by utilizing a secondary gradient method, the Lagrange relaxation algorithm is a method for solving the lower bound of the optimal value of the original problem, different initial values are given to multipliers, different minimum values exist, and the maximum value of the minimum values is selected as the result of the optimization.
As can be seen from the formula (1), the objective function comprises two integer variables of 0 and 1, and a dual gap exists between the optimal solution of the dual problem and the optimal solution of the objective function. According to the weak dual principle, when the original problem is a minimum value problem, the solution of the original problem is always larger than that of the dual problem, and the relationship between the dual problem and the solution of the original problem is shown in fig. 2. Therefore, the lower bound of the optimal value of the objective function of the original problem is obtained by continuously adjusting the multipliers when the dual gap meets a certain range, so that the difficulty in solving the original problem is reduced; the flow chart of the unit combination solving based on Lagrange relaxation is shown in FIG. 3.
S2: wind power uncertainty modeling
The random unit combination model based on the scene method is a wind power uncertainty modeling established on the basis of wind power interval prediction, and the volatility of wind power is considered through the steps of scene generation, scene reduction and the like.
S2.1: scene generation
The wind power interval prediction is an extension of deterministic prediction, and not only can give an accurate wind power prediction value, but also can give a fluctuation range of the prediction value. The method is based on wind power point prediction, firstly, a probability density function of a wind power prediction error is obtained through a density estimation method, and then a confidence interval of a wind power prediction value is obtained by applying relevant knowledge of probability theory. And flexibly selecting a confidence interval under a certain confidence probability according to the requirements of economic benefit and reliability.
In this embodiment, the cumulative probability distribution function is obtained by integrating the wind power probability density function, and under a given α value, the confidence interval with a confidence level of 1- α may be represented as:
in the formula: f (xi) is an accumulative distribution function of the wind power prediction error; alpha is alpha2-α11- α, taking the symmetry interval, α1＝α/2，α2＝1-α/2；
An inverse function representing F (ξ); ppredRepresenting the predicted value of the wind power.
According to the wind power interval prediction, the upper envelope and the lower envelope of the wind power confidence interval meeting a certain confidence probability are obtained, and a certain number of random numbers on the confidence interval formed by the upper envelope and the lower envelope are generated through a uniform distribution function, so that a large number of scenes can be generated.
S2.2: scene reduction
Because the number of scenes increases exponentially with the increase of the time period, optimization calculation is more difficult, so the number of scenes needs to be reduced as much as possible, scenes with low probability values are deleted, scenes with very similar probability values are merged to reduce the number of scenes, and representative scenes are selected to be weighted and averaged.
In the scene reduction, a new probability value is finally assigned to the selected representative scene in order to minimize the probability distance between the scene set after reduction and the scene set before reduction.
The general probability distance is Kantorovich distance, and two scenes are zetai、ζjThe Kantorovich distance between is defined as follows:
in the formula: t is the total number of time segments,
wind power output conditions of scenes i and j at time t are respectively shown.
The embodiment improves on the fast backward reduction technique and proposes a Kantorovich distance matrix. If a scene set with the number of scenes being M needs to be generated, the Kantorovich distance matrix is a two-dimensional matrix, and each element in the matrix is the Kantorovich distance between two scenes. Obviously, the Kantorovich distance matrix is a directly symmetrical matrix with 0 diagonal element.
The improved scene backward reduction method comprises the following calculation steps:
s2.2.1: calculating Kantorovich distances among different scenes to generate a Kantorovich positive symmetric distance matrix;
s2.2.2: finding out each scene according to the Kantorovich directly symmetrical distance matrixMiddle closest scene, i.e., min { KD (ζ)i、ζj)}；
S2.2.3: for each pair of scenes in the scene set, min { KD (ζ) is calculatedi、ζj)}×P[ζi]Deleting the scene corresponding to the row after finding the minimum value, namely deleting a scene j, namely deleting the jth row and jth column in the Kantorovich distance matrix, and accumulating the probability value of the deleted scene to the scene closest to the deleted scene;
s2.2.4: reconstructing a Kantorovich directly symmetrical distance matrix after reducing scenes;
s2.2.5: steps S2.2.2-S2.2.4 are repeated until the number of remaining scenes meets the given requirements.
S3: random unit combination mathematical model containing wind power plant
The same start-stop plan is adopted for all scenes (except the quick start unit) in the scene set in the scheduling of the day-ahead unit combination; for the actual output level of the unit which is put into operation, the economic dispatching is independently carried out through the real-time wind power data measured in the next day, namely, the real-time regulation and control are carried out by adopting a rolling plan in the day; therefore, the present embodiment uses the weighting of each scene under the same unit combination model.
S3.1: objective function
In the formula: f is the total cost; pisIs the probability of scene s; u. ofi,jThe running state of the unit i at the moment j is shown; m is the total scene number; n is the total unit number; t is the total time period number;
the power generation cost of the unit i at the moment j when the unit is in the scene s is obtained;
the active power of the unit i at the moment j is the scene s; si，jAnd the starting cost function of the unit i at the moment t.
S3.2: constraint conditions
S3.2.1: system power balance constraints
In the formula:
is the predicted value of the system load at the moment j;
and the wind power output value at the moment j under the scene s is obtained.
S3.2.2: system positive/negative rotational standby constraints
In the formula: l% is the positive rotation reserve capacity of the system; wu% and wd% are the prediction error plus and minus spin reserve requirements.
S3.2.3: upper and lower limit restraint of unit output
In the formula (I), the compound is shown in the specification,
respectively the lower limit and the upper limit of the output power of the unit i.
S3.2.4: constraint of minimum start-stop time of the unit:
in the formula (I), the compound is shown in the specification,
the accumulated on/off time from the unit i to the moment t,
for the minimum on-time of the unit i,
the minimum off time for unit i.
S3.2.5: the starting cost is as follows:
in the formula, HSTiRepresenting the hot start cost of the unit i; CST (continuous stirred tank reactor)iIndicating the cold start cost of the unit i;
it represents the cold start time of the unit i.
S4: example analysis
Computational analysis was performed by a 10-machine system. And taking 24 hours as a scheduling period, and giving a start-stop plan of the unit when different load requirements are met on the premise of considering the influence of random fluctuation of wind power. The convergence standard of the relative dual gap is set to be 0.1, the iteration times of Lagrange multipliers in unit combination and economic dispatching are respectively set to be 30 times and 2000 times, and a wind power plant with the total installed capacity of 150MW is connected to a Bus4 through a long-distance line. The unit parameter, load and wind power prediction data are given in table 1 and table 2, respectively.
Table 1: basic information of each unit of 10-unit system
Table 2: load predicted power and wind power predicted power in each time period
S4.1: solving process
S4.1.1: initial value of multiplier
According to a heuristic sorting method, sorting by using the average coal consumption of the unit under full load to obtain a priority input sequence of 1-2-4-3-5-6-7-8-9-10; taking the time 1 as an example, under the condition that the maximum output power of the running unit meets the load requirement of 700MW, determining that the two units 1 and 2 are put into operation, wherein the economic distribution of the unit power is 455MW and 245MW respectively, and obtaining the initial value of the multiplier according to the equal consumption micro-increment rate principle.
S4.1.2: scene generation
Assuming that a probability density function of the wind power prediction error obeys normal distribution, an accumulated probability distribution function is obtained first, an upper envelope line and a lower envelope line of a confidence interval are obtained after a symmetrical probability interval is taken, and 30 random scenes between the upper limit and the lower limit of the confidence interval are generated on the premise of meeting load requirements at different moments. Namely, a 30-dimensional column vector is obtained through a uniform distribution function, then column vectors at 24 moments are sequentially generated, and finally a 30-by-24 matrix containing 30 scenes is obtained.
S4.1.3: scene reduction
On the basis of 30 scene sets, generating a Kantorovich directly symmetrical distance matrix, and finding out the scene closest to the Kantorovich in each scene; then deleting the scene after finding out the minimum value, and accumulating the probability value of the deleted scene to the scene closest to the deleted scene; and finally, reconstructing the Kantorovich distance matrix, and repeating the steps until the number of the remaining scenes is reduced to 5.
S4.2: results of the experiment
After the MATLAB2017 programming, the start-stop state and the output condition of each unit are obtained and are shown in the table 3.
Table 3: output of the machine set at each time interval
The start-up results for the unit combinations under ILR and LR conditions are shown in fig. 4 and 5, respectively. Wherein grey indicates that the unit should be in a start-up state during this period and black indicates that the unit is in a stop-down state during this period, it can be seen from a comparison of the two figures that the start-up result of the unit may change over a certain period of time.
S4.3: comparative analysis
Under the condition of not considering random wind power, a traditional Lagrange relaxation algorithm (LR) and an improved Lagrange relaxation algorithm (ILR) are respectively adopted for solving, and the result is shown in a table 4:
table 4: analysis of start-stop costs
As can be seen from the table, the optimization cost of the ILR is low, the economy is good, and the reliability and the practicability of the ILR method are further verified.
Example 2:
the embodiment 2 of the present disclosure provides a unit combination system based on a wind power prediction interval, including:
a data acquisition module configured to: acquiring operation state data of each wind turbine generator in a wind power plant;
a scene acquisition module configured to: obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
a unit combination module configured to: and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
The working method of the system is the same as the unit combination method based on the wind power prediction interval provided in embodiment 1, and is not described here again.
Example 3:
the embodiment 3 of the present disclosure provides a medium, on which a program is stored, and when the program is executed by a processor, the method implements the steps in the unit combination method based on the wind power prediction interval according to the embodiment 1 of the present disclosure, where the steps are:
acquiring operation state data of each wind turbine generator in a wind power plant;
obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
The detailed steps are the same as those of the unit combination method based on the wind power prediction interval provided in embodiment 1, and are not described herein again.
Example 4:
the embodiment 4 of the present disclosure provides an apparatus, including a memory, a processor, and a program stored on the memory and capable of running on the processor, where the processor executes the program to implement the steps in the unit combination method based on the wind power prediction interval according to embodiment 1 of the present disclosure, where the steps are:
acquiring operation state data of each wind turbine generator in a wind power plant;
obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
The detailed steps are the same as those of the unit combination method based on the wind power prediction interval provided in embodiment 1, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A unit combination method based on a wind power prediction interval is characterized by comprising the following steps:
acquiring operation state data of each wind turbine generator in a wind power plant;
obtaining an operation scene of the wind turbine generator according to a wind power interval prediction result;
and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the acquired operation state data to obtain an optimized wind turbine generator combination strategy.
2. The unit combination method based on the wind power prediction interval as claimed in claim 1, wherein according to the wind power interval prediction result, the upper and lower envelopes of the wind power confidence interval satisfying the preset confidence probability are obtained, and a certain number of random numbers on the confidence interval formed by the upper and lower envelopes are generated through a uniform distribution function, so as to obtain the initial operation scene of the wind turbine.
3. The method for combining units based on the wind power prediction interval according to claim 2, wherein the obtained preliminary operation scenario is reduced, specifically:
calculating Kantorovich distances among different scenes to generate a Kantorovich positive symmetric distance matrix;
finding out the scene closest to the Kantorovich in each scene according to the Kantorovich directly-symmetrical distance matrix;
for each pair of scenes in the scene set, calculating the product of the probability of the scene closest to the scene in each scene and the original scene, deleting the scene corresponding to the row after finding out the minimum value, and accumulating the probability value of the deleted scene to the scene closest to the deleted scene;
reconstructing a Kantorovich directly symmetrical distance matrix after reducing scenes;
and repeating the steps until the number of the remaining scenes meets the preset target.
4. The unit combination method based on the wind power prediction interval according to claim 3, wherein the Kantorovich distance is specifically: and the square of the wind power difference value of the two scenes at any moment is the square of the weighted sum of the total time intervals.
5. The method according to claim 1, wherein the objective function targets a lowest total cost, and the total cost is specifically: and adding the weighted sum of the product of the unit operation state and the power generation cost and the weighted sum of the product of the unit operation state and the starting cost under each scene.
6. The method of claim 1, wherein the objective function comprises at least a system power balance constraint, a system positive and negative rotation standby constraint, a unit output upper and lower limit constraint, a unit minimum start-stop time constraint, and a start-up cost constraint.
7. The unit combination method based on the wind power prediction interval as claimed in claim 1, wherein according to a heuristic sorting method, the average coal consumption of the units under full load is used for sorting to obtain the priority input sequence of each unit, according to the equal consumption micro-augmentation rate principle, the initial multiplier value of the Lagrangian relaxation algorithm is obtained, the decoupled single-unit problem is solved through a dynamic programming method, and the Lagrangian multiplier is corrected through a secondary gradient method.
8. A unit combination system based on wind power prediction interval is characterized by comprising:
a data acquisition module configured to: acquiring operation state data of each wind turbine generator in a wind power plant;
a unit combination module configured to: and obtaining an operation scene of the wind turbine generator according to the wind power interval prediction result, and solving an objective function model constructed based on the operation scene by adopting a Lagrange relaxation algorithm according to the obtained operation state data to obtain an optimized wind turbine generator combination strategy.
9. A medium having a program stored thereon, wherein the program when executed by a processor implements the steps in the method for crew assembly based on wind power prediction interval according to any of claims 1-7.
10. An apparatus comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for wind power prediction interval based crew assembly of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010831986.2A CN112039058A (en) | 2020-08-18 | 2020-08-18 | Unit combination method, system, medium and device based on wind power prediction interval |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010831986.2A CN112039058A (en) | 2020-08-18 | 2020-08-18 | Unit combination method, system, medium and device based on wind power prediction interval |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112039058A true CN112039058A (en) | 2020-12-04 |
Family
ID=73577517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010831986.2A Pending CN112039058A (en) | 2020-08-18 | 2020-08-18 | Unit combination method, system, medium and device based on wind power prediction interval |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112039058A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113346480A (en) * | 2021-05-18 | 2021-09-03 | 国网吉林省电力有限公司 | Opportunity constraint-based power system unit combination method |
| CN113743795A (en) * | 2021-09-08 | 2021-12-03 | 北京京东振世信息技术有限公司 | Method and device for determining task scheduling result |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107221935A (en) * | 2017-06-12 | 2017-09-29 | 国网江西省电力公司经济技术研究院 | The AC/DC Hybrid Transmission System Interval Power Flow method converted based on confidence level |
| CN107239863A (en) * | 2017-04-12 | 2017-10-10 | 广东电网有限责任公司电力调度控制中心 | The robust Unit Combination method of power system security constraints |
| CN109785183A (en) * | 2018-12-26 | 2019-05-21 | 国网山西省电力公司电力科学研究院 | A kind of consideration wind-powered electricity generation and the probabilistic Robust Scheduling method of load prediction |
| CN111327046A (en) * | 2018-12-14 | 2020-06-23 | 国网能源研究院有限公司 | Economic dispatching method for power system containing large-scale wind power scene |
| US20200212681A1 (en) * | 2019-01-02 | 2020-07-02 | Tsinghua University | Method, apparatus and storage medium for transmission network expansion planning considering extremely large amounts of operation scenarios |
-
2020
- 2020-08-18 CN CN202010831986.2A patent/CN112039058A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107239863A (en) * | 2017-04-12 | 2017-10-10 | 广东电网有限责任公司电力调度控制中心 | The robust Unit Combination method of power system security constraints |
| CN107221935A (en) * | 2017-06-12 | 2017-09-29 | 国网江西省电力公司经济技术研究院 | The AC/DC Hybrid Transmission System Interval Power Flow method converted based on confidence level |
| CN111327046A (en) * | 2018-12-14 | 2020-06-23 | 国网能源研究院有限公司 | Economic dispatching method for power system containing large-scale wind power scene |
| CN109785183A (en) * | 2018-12-26 | 2019-05-21 | 国网山西省电力公司电力科学研究院 | A kind of consideration wind-powered electricity generation and the probabilistic Robust Scheduling method of load prediction |
| US20200212681A1 (en) * | 2019-01-02 | 2020-07-02 | Tsinghua University | Method, apparatus and storage medium for transmission network expansion planning considering extremely large amounts of operation scenarios |
Non-Patent Citations (2)
| Title |
|---|
| XIAOBIN WU,ET AL.: "Scenario-based Unit Commitment Model Considering Stochastic Wind Power", 《2019 IEEE 3RD CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2)》 * |
| 何小宇等: "改进的拉格朗日松弛法求解机组组合问题", 《电力系统保护与控制》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113346480A (en) * | 2021-05-18 | 2021-09-03 | 国网吉林省电力有限公司 | Opportunity constraint-based power system unit combination method |
| CN113743795A (en) * | 2021-09-08 | 2021-12-03 | 北京京东振世信息技术有限公司 | Method and device for determining task scheduling result |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106230028B (en) | A kind of Multipurpose Optimal Method of wind-powered electricity generation-water-storage association system | |
| Mohammadi et al. | Optimal scheduling of energy hubs in the presence of uncertainty-a review | |
| CN111401664A (en) | Robust optimization scheduling method and device for comprehensive energy system | |
| Zhou et al. | Multiagent Bayesian deep reinforcement learning for microgrid energy management under communication failures | |
| CN112865190A (en) | Optimal scheduling method and system for photovoltaic and charging demand-based optical storage charging station | |
| CN112785184B (en) | Source network load coordination distribution robust long-term expansion planning method considering demand response | |
| CN112966444B (en) | Intelligent energy optimization method and device for building multi-energy system | |
| CN104318397A (en) | Risk assessment and analysis method based on power grid short-time run-time behaviors | |
| CN112039058A (en) | Unit combination method, system, medium and device based on wind power prediction interval | |
| CN111682536A (en) | Random-robust optimization operation method for virtual power plant participating in day-ahead double market | |
| CN105809349A (en) | Scheduling method considering incoming water correlation cascade hydropower stations | |
| CN111509784B (en) | Uncertainty-considered virtual power plant robust output feasible region identification method and device | |
| CN111008790A (en) | Hydropower station group power generation electric scheduling rule extraction method | |
| Chatziagorakis et al. | Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio | |
| Mirjalili et al. | A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system | |
| Wen et al. | Data-driven energy management system for flexible operation of hydrogen/ammonia-based energy hub: A deep reinforcement learning approach | |
| CN116706917A (en) | Intelligent park collaborative regulation and control method and system based on rapid alternating direction multiplier method | |
| CN109976155B (en) | Method and system for randomly optimizing and controlling interior of virtual power plant participating in gas-electricity market | |
| CN115795992A (en) | Park energy Internet online scheduling method based on virtual deduction of operation situation | |
| He et al. | Biobjective Optimization‐Based Frequency Regulation of Power Grids with High‐Participated Renewable Energy and Energy Storage Systems | |
| CN108767855A (en) | A kind of electric system random production analog method that sequential persistently mixes | |
| Cui et al. | Demand response method considering multiple types of flexible loads in industrial parks | |
| Hongwei et al. | Robust stochastic optimal dispatching of integrated electricity-gas-heat systems with improved integrated demand response | |
| CN115115276A (en) | Virtual power plant scheduling method and system considering uncertainty and privacy protection | |
| Zhai et al. | MPC-based two-stage rolling power dispatch approach for wind-integrated power system |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201204 |
|
| RJ01 | Rejection of invention patent application after publication |