CN112803491A - Wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk - Google Patents

Wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk Download PDF

Info

Publication number
CN112803491A
CN112803491A CN202011635844.5A CN202011635844A CN112803491A CN 112803491 A CN112803491 A CN 112803491A CN 202011635844 A CN202011635844 A CN 202011635844A CN 112803491 A CN112803491 A CN 112803491A
Authority
CN
China
Prior art keywords
risk
wind
light
model
electricity
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
CN202011635844.5A
Other languages
Chinese (zh)
Other versions
CN112803491B (en
Inventor
明波
成楸语
黄强
王义民
郭鹏程
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.)
Xian University of Technology
Original Assignee
Xian University of Technology
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 Xian University of Technology filed Critical Xian University of Technology
Priority to CN202011635844.5A priority Critical patent/CN112803491B/en
Publication of CN112803491A publication Critical patent/CN112803491A/en
Application granted granted Critical
Publication of CN112803491B publication Critical patent/CN112803491B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • H02J3/38—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
    • 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
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • 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)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Power Engineering (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risks, and belongs to the crossing field of renewable energy utilization and reservoir scheduling. The method comprises the following steps: constructing a multivariate stochastic simulation model by considering the correlation of the multidimensional uncertainty factors; establishing an electricity abandonment risk evaluation index, and identifying and quantifying the electricity abandonment risk of the wind, light and water multi-energy complementary system according to the obtained multivariate random simulation model; based on a risk/benefit balance theory, a double-layer nested algorithm is utilized to solve the constructed wind, light and water multi-energy complementary system short-term optimization scheduling model of the coupling electricity abandonment risk, and a wind, light and water multi-energy complementary short-term optimization scheduling plan of the coupling electricity abandonment risk with balanced risk/benefit is obtained. The invention can quantitatively evaluate the electricity abandoning risk of wind, light and water multi-energy complementary scheduling, couples the risk index into the complementary optimization scheduling model, can make a short-term scheduling plan with balanced risk/benefit, and meets the requirement of output stationarity of the power generation plan.

Description

Wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk
Technical Field
The invention belongs to the cross field of renewable energy utilization and reservoir scheduling, and relates to a wind, light and water multi-energy complementary short-term optimization scheduling method for coupling electricity abandonment risks.
Background
With the aggravation of energy crisis and the deterioration of ecological environment, the development and utilization of renewable energy sources become important strategies for ensuring the future energy safety and coping with global climate change. The wind, light and water energy sources are polymerized to form a complementary power generation system, and the method is an effective way for reducing new energy grid connection impact and improving the utilization rate of basin resources.
The wind, light and water multi-energy complementary short-term scheduling basic process is to determine the water storage and discharge strategy of the cascade reservoir according to the runoff and the wind and light resource characteristics, and to improve the utilization rate of the drainage basin resources to the maximum extent on the premise of ensuring the safety of water resources and energy. Because wind power and photoelectricity are easily influenced by meteorological factors, the output of the wind power and the photoelectricity has strong random fluctuation and is difficult to predict accurately, and the high dimension and strong nonlinearity of the cascade hydroelectric dispatching problem make the compilation of a short-term dispatching plan of a complementary system have great challenge.
The main problems of the existing wind, light and water multi-energy complementary short-term scheduling method are as follows: (1) the short-term scheduling model lacks quantitative description of electricity abandonment risks, and the generated scheduling plan cannot provide necessary risk information for a decision maker; (2) output fluctuation in a short-term dispatching plan of the complementary system is large, and the requirement of extra-high voltage direct current transmission is difficult to meet.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the wind, light and water multi-energy complementary short-term optimal scheduling method coupled with the electricity abandoning risk.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
the invention discloses a wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risks, which comprises the following steps of:
the method comprises the following steps: considering the correlation of multidimensional uncertainty factors, constructing a multivariate random simulation model, and realizing the representation of multidimensional uncertainty;
step two: establishing an electricity abandonment risk evaluation index, and identifying and quantifying the operation risk of the wind, light and water multi-energy complementary system according to the multivariate random simulation model established in the step one;
step three: and constructing a short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk based on a risk/benefit balance theory, solving the constructed short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk by using a double-layer nested algorithm, and obtaining a risk/benefit balanced wind, light and water multi-energy complementary short-term optimized dispatching plan of the coupling electricity abandoning risk.
Preferably, in step one, for the multidimensional uncertainty factor with obvious correlation, constructing a multivariate joint probability distribution function based on Copula junction function, where the multivariate joint probability distribution function is:
F(X1,X2,X3…,Xn)＝C[F(X1),F(X2),F(X3)…F(Xn)]；
in the formula: f (X)1)、F(X2)、F(X3)…F(Xn) Respectively, the marginal probability distribution of the prediction error; c is the Copula join function.
Preferably, in the step one, for mutually independent multidimensional uncertainty factors, a time series model is adopted for random simulation; when the multidimensional uncertainty factors of the random simulation have correlation, selecting one multidimensional uncertainty factor as a main variable, and using other multidimensional uncertainty factors as auxiliary variables; firstly, randomly simulating a main variable to generate a plurality of long series samples of time cuts, and then sequentially simulating other auxiliary variables based on a Bayes conditional probability formula, wherein the Bayes conditional probability formula is as follows:
Figure BDA0002876266390000021
in the formula: u. of1＝F(X1)，F(X1) Is the marginal probability distribution of the prediction error; c is a Copula join function; znBayesian conditional probabilities.
Preferably, in the first step, the multidimensional uncertainty factor includes a load prediction error, a runoff prediction error, a wind power output prediction error, and a photovoltaic power output prediction error.
Preferably, in the second step, when the power abandoning rate is used as a power abandoning risk evaluation index, the calculation formula for quantifying the operation risk of the wind, light and water multi-energy complementary system is as follows:
Figure BDA0002876266390000031
in the formula: rcAverage power abandon rate under multiple scenes; s is the total scene number; s is a scene number;
Figure BDA0002876266390000032
actual output of the solar wind, light and water and electricity provided for the random simulation model; dtThe load of the wind, light and water multi-energy complementary system is supplied to the power system.
Further preferably, the power abandoning rate is the ratio of the new energy power abandoning amount to the actual power generation amount.
Preferably, in the third step, the wind, light and water multi-energy complementary system short-term optimization scheduling model coupled with the electricity abandonment risk is a double-layer planning optimization model, the double-layer planning optimization model comprises an upper layer model and a lower layer model, the upper layer model optimizes the total output of the cascade hydropower station under the given conditions of water supply and wind and light output, and the sum of squares of remaining loads is minimized under the condition of taking into account the electricity abandonment risk; and the lower layer model optimizes a load distribution strategy among hydropower stations under a given load condition, so that the cascade hydropower stations can store energy maximally.
Further preferably, the basic structure of the two-layer planning model includes:
Figure BDA0002876266390000033
in the formula: l istLarge power grid load;
Figure BDA0002876266390000034
total output for the complementary system; λ is a risk preference coefficient; p is a power output matrix of each hydropower station in each scheduling period and is a decision variable of an upper layer model; r is a power generation flow matrix of each hydropower station in each time period and is a decision variable of a lower layer model; m is the number of hydropower stations; kmThe comprehensive output coefficient of the hydropower station is obtained; i ism,tIs the flow rate of warehousing; qm,tThe flow is the warehouse-out flow; hj,tIs the water head; f is an objective function of the upper model; f is the objective function of the lower model; g and G are both constraint functions.
Further preferably, in the double-layer nested algorithm, an outer layer algorithm is adopted for an upper layer model;
the outer layer algorithm adopts an intelligent algorithm to optimize the total output of the complementary system, so that the target function of the upper layer model is minimum.
Further preferably, in the double-layer nested algorithm, an inner-layer algorithm is adopted for a lower-layer model;
the inner layer algorithm determines the inter-hydropower-station load distribution strategy by adopting a method of combining a discrimination coefficient and a full storage rate under the condition of given hydroelectric output, so that the objective function of the lower layer model is the maximum.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risks. Therefore, the wind, light and water multi-energy complementary short-term optimization scheduling method for coupling the electricity abandonment risk has the following advantages:
1. according to the method, the representation of multi-dimensional uncertainty (load prediction error, runoff prediction error, wind power output prediction error and photoelectric output prediction error) can be realized by constructing a multivariate random simulation model.
2. The conventional wind, light and water multi-energy complementary short-term scheduling research lacks quantitative description of electricity abandonment risks, and the method can quantitatively evaluate the risks of multi-energy complementary scheduling by constructing an electricity abandonment risk evaluation model of a wind, light and water multi-energy complementary system, identifying main risk factors of complementary operation and constructing a risk evaluation index.
3. The invention couples risk indexes into a complementary optimization scheduling model, so that the obtained scheduling plan meets certain risk constraint, and the benefit can be improved while controlling the risk.
4. According to the method, a short-term scheduling plan with controllable risk and optimal benefit can be finally worked out by constructing a double-layer planning optimization model and combining a wind, light and water multi-energy complementary short-term optimization scheduling model coupled with the electricity abandoning risk.
Drawings
FIG. 1 is a flow chart of a wind, light and water multi-energy complementary short-term optimization scheduling method for coupling electricity abandonment risk in an embodiment of the present invention;
fig. 2 is a schematic diagram of processing continuous on-off constraints based on a two-dimensional coding strategy according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The wind, light and water multi-energy complementary short-term optimization scheduling method for coupling the power abandonment risk according to the present invention is described in detail below with reference to the accompanying drawings.
< example >
As shown in fig. 1, the wind, light and water energy complementation short-term optimal scheduling method for coupling the electricity abandonment risk provided by the embodiment includes the following steps:
1. one-factor optimal profile and parameter identification
And respectively carrying out distribution fitting on historical observation data of multidimensional uncertainty factors (such as load prediction errors, runoff prediction errors, wind power output prediction errors and photoelectric output prediction errors) of the complementary system to obtain corresponding theoretical distribution, comparing with empirical distribution to calculate squared Euclidean distance, and selecting the distribution with the smallest squared Euclidean distance as the optimal edge distribution. A common probability distribution function is shown in table 1:
table 1 general probability distribution function
Figure BDA0002876266390000061
2. Multi-dimensional uncertainty factor joint probability distribution function derivation
Diagnosing the correlation of different prediction error series, constructing a joint probability distribution function for a multidimensional uncertainty factor with obvious correlation based on a Copula junction function, wherein the multivariate joint probability distribution function can be expressed as:
F(X1,X2,X3…Xn)＝C[F(X1),F(X2),F(X3)…F(Xn)] (1)
in the formula: f (X)1)、F(X2)、F(X3)…F(Xn) Respectively as edge probability distribution functions of prediction errors; c is the Copula join function.
Usually, three commonly used archimedean type Copula functions are selected to construct a joint distribution model, and the function expressions are respectively:
clayton Copula function:
Figure BDA0002876266390000071
frank Copula function:
Figure BDA0002876266390000072
Gumbel-Hougaard Copula function:
C(u,v)＝exp{-[(-lnu)θ+(-lnv)θ]1/θ} (4)
in the formula: u and v represent edge distribution functions of different variables respectively, theta is a parameter of the Copula function, and theta can be obtained according to a maximum likelihood method.
Empirical Copula is defined as:
Figure BDA0002876266390000073
wherein, I[.]For an illustrative function, when Fn(xi)≤When u is the number of the carbon atoms,
Figure BDA00028762663900000710
otherwise
Figure BDA00028762663900000711
Has an empirical Copula function
Figure BDA0002876266390000074
Then, calculate
Figure BDA0002876266390000075
Squared Euclidean distance from empirical Copula
Figure BDA0002876266390000076
Figure BDA0002876266390000077
Figure BDA0002876266390000078
Where u and v represent the edge distribution functions of the different variables, respectively.
Figure BDA0002876266390000079
Respectively reflecting the conditions of fitting data of Clayton Copula, Frank Copula and Gumbel-Hougaard Copula, and comparing the squared Euclidean distance of Clayton Copula, Frank Copula and Gumbel-Hougaard Copula, if the squared Euclidean distance is the minimum, the combined distribution can better fit the measured data.
3. Multi-dimensional uncertainty joint stochastic simulation considering spatio-temporal correlations
Firstly, respectively adopting a time series model (such as ARMA) to carry out random simulation on mutually independent multidimensional uncertainty factors; when the multi-dimensional uncertainty factors of random simulation have correlation, selecting one multi-dimensional uncertainty factor as a primary variable and the other multi-dimensional uncertainty factors as secondary variables, firstly, randomly simulating the primary variable to generate a plurality of long series samples of time cut; then, based on a Bayesian conditional probability formula, other slave variables are simulated in sequence. The basic version of the bayesian conditional probability formula is as follows:
Figure BDA0002876266390000081
in the formula: u. of1＝F(X1)，F(X1) An edge probability distribution function that is a prediction error; c is a Copula join function; znBayesian conditional probabilities.
The steps of back-deriving the remaining scene values are as follows:
when the Clayton Copula function is selected, the function expression of the conditional distribution is as follows:
Figure BDA0002876266390000082
when the Frank Copula function is selected, the functional expression of the conditional distribution is as follows:
Figure BDA0002876266390000083
when Gumbel-Hougaard Copula function is selected, the function expression of the conditional distribution is as follows:
Figure BDA0002876266390000084
adopting Latin hypercube sampling method to generate random number sequence Pi，PiE (0, 1); let F (v | u) be PiFurther, the corresponding v value can be obtained by knowing the sequence value of u.
4. Electricity abandoning risk quantification of wind, light and water multi-energy complementary system
Establishing an electricity abandonment risk evaluation index, generating various forecast error situations based on a multi-dimensional uncertainty combined random simulation model, superposing the forecast error situations on a given load forecast value, a runoff forecast value, a wind power output forecast value and a photoelectric output forecast value to form various situations, and calculating the electricity abandonment risk of a given power generation plan under the multiple situations.
When the power abandoning rate (the ratio of the new energy power abandon amount to the actual generated energy) is used as the power abandoning risk evaluation index, the power abandoning risk quantification calculation formula of the wind, light and water multi-energy complementary system is as follows:
Figure BDA0002876266390000091
in the formula: rcAverage power abandon rate under multiple scenes; s is the total scene number; s is a scene number;
Figure BDA0002876266390000092
actual output of the solar wind, light and water and electricity provided for the random simulation model; dtThe load assigned to the system;
Figure BDA0002876266390000093
for comparison
Figure BDA0002876266390000094
And 0, the maximum value is taken.
5. Double-layer planning model construction for day-ahead power generation planning of coupling risk index
Based on a risk/benefit balance theory, considering a hydropower station economic operation module, and constructing a double-layer planning model for compiling a day-ahead power generation plan of a complementary system, namely a short-term optimization scheduling model of the wind, light and water multi-energy complementary system for coupling the electricity abandonment risk, wherein the double-layer planning model comprises an upper layer model and a lower layer model. The upper layer model optimizes the total output of the complementary system under the given conditions of the input water and wind and light output, and minimizes the sum of squares of the residual load under the condition of giving consideration to the risk of electricity abandonment; and the lower layer model optimizes the load distribution strategy among hydropower stations under the condition of given total output of the complementary system, so that the cascade hydropower stations can store energy maximally.
The basic structure of the double-layer planning model is as follows:
Figure BDA0002876266390000095
in the formula: l istLarge power grid load;
Figure BDA0002876266390000096
total output for the complementary system; λ is a risk preference coefficient; p is a power output matrix of each hydropower station in each scheduling period and is a decision variable of an upper layer model; r is a power generation flow matrix of each hydropower station in each time period and is a decision variable of a lower layer model; m is the number of hydropower stations; kmThe comprehensive output coefficient of the hydropower station is obtained; i ism,tIs the flow rate of warehousing; qm,tThe flow is the warehouse-out flow; hj,tIs the water head; f is an objective function of the upper model; f is the objective function of the lower model; g and G are both constraint functions.
6. Efficient dimension reduction solution of short-term scheduling model
Firstly, in order to take account of the solving efficiency and the calculation precision of the model, a double-layer nested algorithm is to be constructed to solve the double-layer planning model constructed in the step 5. The method comprises the following steps that an outer layer algorithm is adopted for an upper layer model, and the outer layer algorithm adopts an intelligent algorithm (such as a genetic algorithm) to optimize the output of a complementary system in each scheduling time period, so that a target function F of the upper layer model is minimum; adopting an inner layer algorithm aiming at the lower layer model; and under the condition of given total output, the inner layer algorithm optimizes the inter-station load distribution strategy of the hydropower station by adopting an algorithm combining the discrimination coefficient and the water storage rate, so that the target function f of the lower layer model is maximum.
In order to better meet the requirement of the stability of the total output, a two-dimensional coding strategy is adopted for processing. As shown in fig. 2, the total output of the complementary system is constant between two adjacent time nodes to avoid frequent fluctuation. And if the total time interval of the total output of the complementary system which is continuously unchanged in the whole scheduling period is p and the number of the time nodes is p-1, the total number of the optimized variables is 2 multiplied by p-1, thereby realizing the dimension reduction. The processing of force stationarity constraints based on a two-dimensional encoding strategy is shown in fig. 2. Referring to fig. 2, given different risk preference values, the developed optimization algorithm is used to solve the scheduling model, and a short-term optimized scheduling scheme with balanced risk/benefit is obtained.
In summary, the invention provides a wind, light and water multi-energy complementary short-term optimal scheduling method coupled with electricity abandoning risks, which comprises the following steps: the method comprises the following steps: considering the correlation of multidimensional uncertainty factors, constructing a multivariate random simulation model, and realizing the representation of multidimensional uncertainty; step two: establishing an electricity abandonment risk evaluation index, and identifying and quantifying the electricity abandonment risk of the wind, light and water multi-energy complementary system according to the multi-dimensional prediction error combined random simulation model established in the step one; step three: and constructing a short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk based on a risk/benefit balance theory, solving the constructed short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk by using a double-layer nested algorithm, and obtaining a risk/benefit balanced wind, light and water multi-energy complementary short-term optimized dispatching plan of the coupling electricity abandoning risk. The method provided by the invention couples the power-saving risk of wind, light and water multi-energy complementation in the short-term optimization scheduling model, improves the operation benefit of the system on the premise of controllable risk, and can provide important and operability basis for the short-term scheduling of the wind, light and water multi-energy complementation system.
The above embodiments are merely illustrative of the technical solutions of the present invention. The short-term risk scheduling method of the basin wind, light and water multi-energy complementary system coupled with the multidimensional uncertainty is not limited to the contents described in the above embodiments, but is subject to the scope defined by the claims. Any modification or supplement or equivalent replacement made by a person skilled in the art on the basis of this embodiment is within the scope of the invention as claimed in the claims.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risks is characterized by comprising the following steps:
the method comprises the following steps: constructing a multivariate random simulation model according to the correlation of the multidimensional uncertainty factors;
step two: establishing an electricity abandonment risk evaluation index, and identifying and quantifying the electricity abandonment risk of the wind, light and water multi-energy complementary system according to the multivariate random simulation model established in the step one;
step three: and constructing a short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk based on a risk/benefit balance theory, solving the constructed short-term optimized dispatching model of the wind, light and water multi-energy complementary system of the coupling electricity abandoning risk by using a double-layer nested algorithm, and obtaining a risk/benefit balanced wind, light and water multi-energy complementary short-term optimized dispatching plan of the coupling electricity abandoning risk.
2. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk as claimed in claim 1, wherein in the first step, for the multidimensional uncertainty factor with obvious correlation, a multivariate joint probability distribution function is constructed based on Copula junction function, and the multivariate joint probability distribution function is:
F(X1,X2,X3…,Xn)＝C[F(X1),F(X2),F(X3)…F(Xn)]；
in the formula: f (X)1)、F(X2)、F(X3)…F(Xn) Respectively as edge probability distribution functions of prediction errors; c is the Copula join function.
3. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the electricity abandoning risk is characterized in that in the step one, a time series model is adopted to carry out random simulation on mutually independent multi-dimensional uncertainty factors; when the multidimensional uncertainty factors of the random simulation have correlation, selecting one multidimensional uncertainty factor as a main variable, and using other multidimensional uncertainty factors as auxiliary variables; firstly, randomly simulating a main variable to generate a plurality of long series samples of time cuts, and then sequentially simulating other auxiliary variables based on a Bayes conditional probability formula, wherein the Bayes conditional probability formula is as follows:
Figure FDA0002876266380000021
in the formula: u. of1＝F(X1)，F(X1) An edge probability distribution function that is a prediction error; c is a Copula join function; znBayesian conditional probabilities.
4. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the electricity abandonment risk as claimed in claim 1, wherein in the first step, the multidimensional uncertainty factor comprises a load prediction error, a runoff prediction error, a wind power output prediction error and a photoelectric output prediction error.
5. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the electricity abandoning risk as claimed in claim 1, wherein in the second step, when the electricity abandoning rate is used as the electricity abandoning risk evaluation index, the calculation formula of the electricity abandoning risk quantification of the wind, light and water multi-energy complementary system is as follows:
Figure FDA0002876266380000022
in the formula: rcAverage power abandon rate under multiple scenes; s is the total scene number; s is a scene number;
Figure FDA0002876266380000023
actual output of the solar wind, light and water and electricity provided for the random simulation model; dtThe load of the wind, light and water multi-energy complementary system is supplied to the power system.
6. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk is characterized in that the electricity abandoning rate is the ratio of the new energy electricity abandoning amount to the actual power generation amount.
7. The wind, light and water multi-energy complementary short-term optimization scheduling method for coupling the electricity abandonment risk according to claim 1, wherein in the third step, the wind, light and water multi-energy complementary system short-term optimization scheduling model for coupling the electricity abandonment risk is a double-layer planning optimization model, the double-layer planning optimization model comprises an upper layer model and a lower layer model, the upper layer model optimizes the total output of a complementary system under the given conditions of incoming water and wind and light output, and the sum of squares of remaining loads is minimized under the condition of considering the electricity abandonment risk; and the lower layer model optimizes the load distribution strategy among hydropower stations under the condition of giving the total output of the complementary system, so that the energy storage of the cascade hydropower stations is the maximum.
8. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the power curtailment risk as recited in claim 7, wherein the basic structure of the double-layer planning optimization model comprises:
Figure FDA0002876266380000031
in the formula: l istLarge power grid load;
Figure FDA0002876266380000032
total output for the complementary system; λ is a risk preference coefficient; p is a power output matrix of each hydropower station in each scheduling period and is a decision variable of an upper layer model; r is a power generation flow matrix of each hydropower station in each time period and is a decision variable of a lower layer model; m is the number of hydropower stations; kmThe comprehensive output coefficient of the hydropower station is obtained; i ism,tIs the flow rate of warehousing; qm,tThe flow is the warehouse-out flow; hj,tIs the water head; f is an objective function of the upper layer model(ii) a f is the objective function of the lower model; g and G are both constraint functions.
9. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the electricity abandoning risk is characterized in that an outer layer algorithm is adopted for an upper layer model in the double-layer nesting algorithm;
the outer layer algorithm adopts an intelligent algorithm to optimize the total output of the complementary system, so that the target function of the upper layer model is minimum.
10. The wind, light and water multi-energy complementary short-term optimization scheduling method coupled with the electricity abandoning risk is characterized in that an inner layer algorithm is adopted for a lower layer model in the double-layer nesting algorithm;
the method comprises the steps that the inner layer algorithm determines a hydropower station inter-plant load distribution strategy by adopting a method of combining a discrimination coefficient and a full storage rate under the condition of giving the total output of a complementary system, so that the target function of a lower layer model is the maximum.
CN202011635844.5A 2020-12-31 2020-12-31 Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk Active CN112803491B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011635844.5A CN112803491B (en) 2020-12-31 2020-12-31 Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011635844.5A CN112803491B (en) 2020-12-31 2020-12-31 Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk

Publications (2)

Publication Number Publication Date
CN112803491A true CN112803491A (en) 2021-05-14
CN112803491B CN112803491B (en) 2023-07-04

Family

ID=75808696

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011635844.5A Active CN112803491B (en) 2020-12-31 2020-12-31 Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk

Country Status (1)

Country Link
CN (1) CN112803491B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115659672A (en) * 2022-11-02 2023-01-31 中国长江三峡集团有限公司 Basin water, wind and light resource combined random simulation method and device and electronic equipment
CN117196415A (en) * 2023-11-07 2023-12-08 长江三峡集团实业发展(北京)有限公司 Risk scheduling method, device, equipment and medium for water-wind-solar complementary system
CN115659672B (en) * 2022-11-02 2024-06-07 中国长江三峡集团有限公司 River basin water wind-solar resource joint random simulation method and device and electronic equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103259285A (en) * 2013-05-03 2013-08-21 国家电网公司 Method for optimizing short running of electric power system comprising large-scale wind power
CN107039977A (en) * 2017-06-03 2017-08-11 广东博慎智库能源科技发展有限公司 With the uncertain collection construction method of the power system Robust Scheduling of the minimum target of integrated cost
CN108964050A (en) * 2018-08-26 2018-12-07 燕山大学 Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response
CN111181198A (en) * 2020-01-13 2020-05-19 四川大学 Heterogeneous energy complementary power generation scheduling method based on network source mutual feedback
CN111898801A (en) * 2020-06-28 2020-11-06 国网上海能源互联网研究院有限公司 Method and system for configuring multi-energy complementary power supply system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103259285A (en) * 2013-05-03 2013-08-21 国家电网公司 Method for optimizing short running of electric power system comprising large-scale wind power
US20160169202A1 (en) * 2013-05-03 2016-06-16 State Grid Corporation Of China Short-term operation optimization method of electric power system including large-scale wind power
CN107039977A (en) * 2017-06-03 2017-08-11 广东博慎智库能源科技发展有限公司 With the uncertain collection construction method of the power system Robust Scheduling of the minimum target of integrated cost
CN108964050A (en) * 2018-08-26 2018-12-07 燕山大学 Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response
CN111181198A (en) * 2020-01-13 2020-05-19 四川大学 Heterogeneous energy complementary power generation scheduling method based on network source mutual feedback
CN111898801A (en) * 2020-06-28 2020-11-06 国网上海能源互联网研究院有限公司 Method and system for configuring multi-energy complementary power supply system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
YUAN AN等: "Coordinative optimization of hydro-photovoltaic-wind-battery complementary power stations", 《CSEE JOURNAL OF POWER AND ENERGY SYSTEMS》 *
张利升等: "多受端梯级水电站厂网多目标协调优化调度模型", 《电网技术》 *
明波: "大规模水光互补系统全生命周期协同运行研究", 《中国优秀博硕士学位论文全文数据库(博士) 工程科技Ⅱ辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115659672A (en) * 2022-11-02 2023-01-31 中国长江三峡集团有限公司 Basin water, wind and light resource combined random simulation method and device and electronic equipment
CN115659672B (en) * 2022-11-02 2024-06-07 中国长江三峡集团有限公司 River basin water wind-solar resource joint random simulation method and device and electronic equipment
CN117196415A (en) * 2023-11-07 2023-12-08 长江三峡集团实业发展(北京)有限公司 Risk scheduling method, device, equipment and medium for water-wind-solar complementary system
CN117196415B (en) * 2023-11-07 2024-01-26 长江三峡集团实业发展(北京)有限公司 Risk scheduling method, device, equipment and medium for water-wind-solar complementary system

Also Published As

Publication number Publication date
CN112803491B (en) 2023-07-04

Similar Documents

Publication Publication Date Title
Wu et al. Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective
Yan et al. Stochastic multi-scenario optimization for a hybrid combined cooling, heating and power system considering multi-criteria
CN110690702B (en) Active power distribution network optimal scheduling and operation method considering comprehensive bearing capacity
CN114638502A (en) Power distribution network line reinforcement strategy considering flexibility of demand side resources
CN108075471B (en) Multi-objective constraint optimization power grid scheduling strategy based on stochastic power output prediction
Huang et al. Smart energy management system based on reconfigurable AI chip and electrical vehicles
CN116523240A (en) Micro-grid evaluation feedback scheduling method based on MCMC method prediction
CN114142461B (en) New energy differential configuration method considering grid morphology evolution and structure development
CN111353711A (en) Comprehensive energy system evaluation method and system based on TOPSIS method
Miah et al. Energy storage controllers and optimization schemes integration to microgrid: an analytical assessment towards future perspectives
Li et al. Long duration flexibility planning challenges and solutions for power system with ultra high share of renewable energy
Manirathinam et al. Sustainable renewable energy system selection for self-sufficient households using integrated fermatean neutrosophic fuzzy stratified AHP-MARCOS approach
CN115051388A (en) Distribution robustness-based &#39;source-network-load-storage&#39; two-stage scheduling optimization method
CN112803491A (en) Wind, light and water multi-energy complementary short-term optimization scheduling method coupled with electricity abandoning risk
Du et al. A data-driven home energy scheduling strategy under the uncertainty in photovoltaic generations
CN105552941B (en) A kind of distributed generation resource peak capacity optimization method
CN116937562A (en) Power quality prediction method and device for distributed photovoltaic access and power system
CN113488990B (en) Micro-grid optimal scheduling method based on improved bat algorithm
CN116029490A (en) Optical network storage collaborative planning method considering capacity limitation of distributed resource area
CN115545429A (en) Multi-scene low-voltage renewable resource load prediction method based on CPSS framework
Kaygusuz et al. Possible contributions of smart grids to regional development of countries
CN112290543A (en) Method and system for identifying weak points of energy utilization efficiency of energy interconnection system
Okundamiya et al. Optimisation models for hybrid energy systems–a review
da Silva et al. A multi-objective approach for energy management in a microgrid scenario
CN114723278B (en) Community micro-grid scheduling method and system considering photovoltaic energy storage

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