CN112803491B - Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk - Google Patents
Wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-abandoning risk Download PDFInfo
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Abstract
The invention discloses a wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-off risks, and belongs to the crossing field of renewable energy utilization and reservoir scheduling. Comprising the following steps: constructing a multivariate stochastic simulation model by considering the correlation of the multidimensional uncertainty factors; establishing an electric discarding risk evaluation index, and identifying and quantifying the electric discarding risk of the wind-solar-water multi-energy complementary system according to the obtained multivariate random simulation model; based on a risk/benefit balance theory, a constructed wind-solar-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk is solved by using a double-layer nesting algorithm, and a wind-solar-water multi-energy complementary short-term optimization scheduling plan of the coupling power-off risk with balanced risk/benefit is obtained. According to the invention, the electricity discarding risk of the wind, light and water multi-energy complementary scheduling can be quantitatively evaluated, the risk index is coupled in the complementary optimal scheduling model, a short-term scheduling plan with balanced risk/benefit can be formulated, and the power generation plan meets the requirement of output stability.
Description
Technical Field
The invention belongs to the crossing field of renewable energy utilization and reservoir dispatching, and relates to a wind-solar-water multi-energy complementary short-term optimization dispatching method for coupling power-abandoning risks.
Background
With the aggravation of energy crisis and the deterioration of ecological environment, the development and utilization of renewable energy sources are important strategies for ensuring 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, so that the complementary power generation system is an effective way for reducing new energy grid-connected impact and improving the utilization rate of river basin resources.
The basic process of wind, light and water multi-energy complementary short-term dispatching is to determine the water storage and drainage strategy of the cascade reservoir according to the runoff and the characteristics of wind and light resources, and to improve the utilization rate of the river basin resources to the greatest 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 has extremely strong random fluctuation and is difficult to accurately predict, and the high-dimensional performance and the strong nonlinearity of the cascade hydroelectric scheduling problem make the preparation of a complementary system short-term scheduling plan have great challenges.
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 the power-off risk, and the generated scheduling plan cannot provide necessary risk information for a decision maker; (2) The output fluctuation in the short-term scheduling plan of the complementary system is large, and the ultra-high voltage direct current transmission requirement is difficult to meet.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling the power-losing risk.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
the invention discloses a wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-off risks, which comprises the following steps:
step one: taking the correlation of the multidimensional uncertainty factors into consideration, constructing a multivariate stochastic simulation model, and realizing the characterization of the multidimensional uncertainty;
step two: establishing an electric abandoning 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 first step;
step three: based on a risk/benefit balance theory, a wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk is constructed, the constructed wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk is solved by using a double-layer nesting algorithm, and a wind-light-water multi-energy complementary short-term optimization scheduling plan of the coupling power-off risk with balanced risk/benefit is obtained.
Preferably, in the first step, for the multidimensional uncertainty factor with obvious correlation, a multivariate joint probability distribution function is constructed based on a Copula joining function, wherein the multivariate joint probability distribution function is:
F(X 1 ,X 2 ,X 3 …,X n )＝C[F(X 1 ),F(X 2 ),F(X 3 )…F(X n )]；
wherein: f (X) 1 )、F(X 2 )、F(X 3 )…F(X n ) Edge probability distribution of prediction errors respectively; c is a Copula joining function.
Preferably, in the first step, for mutually independent multidimensional uncertainty factors, a time sequence model is adopted for random simulation; when the randomly simulated multi-dimensional uncertainty factors have correlation, selecting one multi-dimensional uncertainty factor as a main variable, and selecting other multi-dimensional uncertainty factors as auxiliary variables; firstly, carrying out random simulation on a main variable to generate a long series of samples with a plurality of time kerfs, and then, sequentially carrying out simulation on other auxiliary variables based on a Bayesian conditional probability formula, wherein the Bayesian conditional probability formula is as follows:
wherein: u (u) 1 ＝F(X 1 )，F(X 1 ) An edge probability distribution that is a prediction error; c is a Copula joining function; z is Z n Is Bayesian conditional probability.
Preferably, in the first step, the multidimensional uncertainty factor includes a load prediction error, a radial flow prediction error, a wind power output prediction error and a photoelectric output prediction error.
Preferably, in the second step, when the electricity rejection rate is used as an electricity rejection risk evaluation index, a calculation formula of the running risk quantification of the wind-solar-water multi-energy complementary system is as follows:
wherein: r is R c Average power rejection rate under multiple scenarios; s is the total scene number; s is the scene number;
actual output of secondary solar wind, light and hydropower provided for a random simulation model; d (D) t The load given by the wind, light and water multi-energy complementary system is given to the electric power system.
Further preferably, the electricity rejection rate is a ratio of the new energy electricity rejection amount to the actual electricity generation amount.
Preferably, in the third step, the short-term optimization scheduling model of the wind-solar-water multi-energy complementary system for coupling the power-saving 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 condition of water supply and wind-solar output, and the sum of residual load squares is minimum under the condition of considering the power-saving risk; and under the given load condition, the lower model optimizes the load distribution strategy among the hydropower stations so as to maximize the energy storage of the cascade hydropower station.
Further preferably, the basic structure of the dual-layer planning model includes:
wherein: l (L) t Is a large grid load;
total output of the complementary system; lambda is a risk preference coefficient; p is the output matrix of each hydropower station in each scheduling period and is the decision variable of the upper model; r is a power generation flow matrix of each hydropower station in each period and is a decision variable of a lower model; m is the number of hydropower stations; k (K) m The comprehensive output coefficient of the hydropower station; i m,t Is the warehouse-in flow; q (Q) m,t Is the delivery flow; h j,t Is a water head; f is an objective function of the upper model; f is an objective function of the lower model; g and G are constraint functions.
Further preferably, in the double-layer nesting algorithm, an outer layer algorithm is adopted for an upper layer model;
and the outer layer algorithm adopts an intelligent algorithm to optimize the total output of the complementary system, so that the objective function of the upper layer model is minimum.
Further preferably, in the double-layer nesting algorithm, an inner-layer algorithm is adopted for a lower-layer model;
and determining a hydropower station inter-plant load distribution strategy by adopting a method of combining a discrimination coefficient and a full storage rate under the given water-electricity output condition by the inner layer algorithm, so that an objective function of a lower layer model is maximum.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a wind-light-water multi-energy complementary short-term optimization scheduling method for coupling power-curtailment risks. Therefore, the wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-off risk has the following advantages:
1. according to the invention, by constructing a multivariate stochastic simulation model, the representation of multidimensional uncertainty (load prediction error, runoff prediction error, wind power output prediction error and photoelectric output prediction error) can be realized.
2. The conventional wind-light-water multi-energy complementary short-term scheduling research lacks quantitative description of the power-saving risk, and the risk of the multi-energy complementary scheduling can be quantitatively evaluated by constructing a power-saving risk evaluation model of the wind-light-water multi-energy complementary system, identifying main risk factors of complementary operation and constructing a risk evaluation index.
3. The conventional wind-solar-water-multifunctional complementary short-term scheduling research mostly adopts a random and robust optimization method to make a scheduling plan, and necessary risk information cannot be provided for a decision maker.
4. According to the invention, a double-layer planning optimization model is constructed, and a wind-solar-water multi-energy complementary short-term optimization scheduling model for coupling power-off risks is combined, so that a short-term scheduling plan with controllable risks and optimal benefits can be finally prepared.
Drawings
FIG. 1 is a flow chart of a wind-solar-water-energy multi-energy complementary short-term optimization scheduling method for coupling power-off risks in an embodiment of the invention;
FIG. 2 is a schematic diagram of a two-dimensional encoding strategy-based continuous start-stop constraint processing method.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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-solar-water multi-energy complementary short-term optimization scheduling method for the coupling power-off risk is described in detail below with reference to the accompanying drawings.
< example >
As shown in fig. 1, the wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-off risk provided by the embodiment includes the following steps:
1. single-factor optimal probability 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 the theoretical distribution with empirical distribution to calculate square Euclidean distance, and selecting the distribution with the smallest square Euclidean distance as the optimal edge distribution. The usual probability distribution functions are shown in table 1:
table 1 common probability distribution function
2. Multi-dimensional uncertainty factor joint probability distribution function derivation
Diagnosing the correlation of different prediction error series, constructing a joint probability distribution function based on Copula joint function for multidimensional uncertainty factors with obvious correlation, wherein the multivariate joint probability distribution function can be expressed as:
F(X 1 ,X 2 ,X 3 …X n )＝C[F(X 1 ),F(X 2 ),F(X 3 )…F(X n )] (1)
wherein: f (X) 1 )、F(X 2 )、F(X 3 )…F(X n ) Respectively an edge probability distribution function of the prediction error; c is a Copula joining function.
Three common archimedes Copula functions are generally selected to construct a joint distribution model, and the function expressions are respectively:
clayton Copula function:
frank Copula function:
Gumbel-Hougaard Copula function:
C(u,v)＝exp{-[(-lnu) θ +(-lnv) θ ] 1/θ } (4)
wherein: u and v represent edge distribution functions of different variables, respectively, and θ is a parameter of a Copula function, and θ can be obtained according to a maximum likelihood method.
The empirical Copula is defined as:
wherein I is [.] As a function of the readiness, when F n (x i ) When the u is less than or equal to the u,
otherwise->
With empirical Copula function
After that, calculate +.>
Squared Euclidean distance from empirical Copula
Where u and v represent the edge distribution functions of the different variables, respectively.
The fit data for Clayton Copula, frank Copula, gumbil-Hougaard Copula are reflected separately, and the squared Euclidean distance for Clayton Copula, frank Copula, gumbil-Hougaard Copula is compared to better fit the measured data if the squared Euclidean distance is minimal.
3. Multi-dimensional uncertainty joint stochastic simulation considering spatio-temporal correlation
Firstly, respectively adopting a time sequence model (such as ARMA) to randomly simulate mutually independent multidimensional uncertainty factors; when the randomly simulated multi-dimensional uncertainty factors have correlation, selecting one of the multi-dimensional uncertainty factors as a main variable and the other multi-dimensional uncertainty factors as auxiliary variables, firstly randomly simulating the main variable to generate a long series of samples with a plurality of time kerfs; then, based on the Bayesian conditional probability formula, other slave variables are simulated in turn. The basic pattern of the bayesian conditional probability formula is as follows:
wherein: u (u) 1 ＝F(X 1 )，F(X 1 ) An edge probability distribution function for the prediction error; c is a Copula joining function; z is Z n Is Bayesian conditional probability.
The steps of back-pushing the remaining scene values are as follows:
when the Clayton Copula function is selected, the functional expression of the condition distribution is as follows:
when selecting the Frank Copula function, the functional expression of the condition distribution is as follows:
when the Gumbel-Hougaard Copula function is selected, the function expression of the conditional distribution is as follows:
generating a random number sequence P by using Latin hypercube sampling method i ，P i E (0, 1); let F (v|u) =p i Further, the corresponding v value can be obtained by knowing the sequence value of u.
4. Power-discarding risk quantification of wind, light and water multi-energy complementary system
And (3) establishing a power-rejection risk evaluation index, generating various prediction error scenes based on a multidimensional uncertainty joint random simulation model, superposing the prediction error scenes on a given load predicted value, a runoff predicted value, a wind power output predicted value and a photoelectric output predicted value to form various scenes, and calculating the power-rejection risk of a given power generation plan under the multiple scenes.
When the electric discarding rate (the ratio of the new energy electric discarding amount to the actual electric generating amount) is used as the electric discarding risk evaluation index, the calculation formula of the electric discarding risk quantification of the wind-solar-water multi-energy complementary system is as follows:
wherein: r is R c Average power rejection rate under multiple scenarios; s is the total scene number; s is the scene number;
actual output of secondary solar wind, light and hydropower provided for a random simulation model; d (D) t Load for system down-set;
for comparison->
And a size of 0, and taking the maximum value.
5. Double-layer planning model construction for daily power generation planning coupled with risk indexes
Based on a risk/benefit balance theory, considering a hydropower station economic operation module, constructing a complementary system day-ahead power generation plan and constructing a double-layer planning model, namely a wind-solar-water multi-energy complementary system short-term optimization scheduling model for coupling the power rejection risk, wherein the double-layer planning model comprises an upper-layer model and a lower-layer model. The upper model optimizes the total output of the complementary system under the given water and wind and light output conditions, and minimizes the sum of squares of residual loads under the condition of giving consideration to the power-losing risk; and under the condition of given total output of the complementary system, the lower model optimizes the load distribution strategy among the hydropower stations, so that the energy storage of the cascade hydropower stations is maximum.
The basic structure of the double-layer planning model is as follows:
wherein: l (L) t Is a large grid load;
total output of the complementary system; lambda is a risk preference coefficient; p is the output matrix of each hydropower station in each scheduling period and is the decision variable of the upper model; r is a power generation flow matrix of each hydropower station in each period and is a decision variable of a lower model; m is the number of hydropower stations; k (K) m The comprehensive output coefficient of the hydropower station; i m,t Is the warehouse-in flow; q (Q) m,t Is the delivery flow; h j,t Is a water head; f is an objective function of the upper model; f is an objective function of the lower model; g and G are constraint functions.
6. Efficient dimension reduction solution for short-term scheduling model
Firstly, in order to consider the solving efficiency and the calculating 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. Wherein, an outer layer algorithm is adopted for the upper layer model, and an intelligent algorithm (such as a genetic algorithm) is adopted for the outer layer algorithm to optimize the output of each scheduling period of the complementary system, so that the objective function F of the upper layer model is minimum; adopting an inner layer algorithm aiming at the lower layer model; and under the given total output condition, the inner layer algorithm optimizes the inter-plant load distribution strategy of the hydropower station by adopting an algorithm combining the discrimination coefficient and the water storage rate, so that the objective function f of the lower layer model is maximum.
In order to better meet the requirement of total output stability, a two-dimensional coding strategy is adopted for processing. As shown in fig. 2, the total force of the complementary system is constant between two adjacent time nodes to avoid frequent fluctuations. And the total number of time periods with continuous total output of the complementary system in the whole scheduling period is p, the number of time nodes is p-1, and the total number of optimization variables is 2 multiplied by p-1, so that dimension reduction is realized. The constraint of the stability of the output is processed based on a two-dimensional coding strategy as shown in fig. 2. Referring to fig. 2, given different risk preference values, a developed optimization algorithm is adopted to solve a scheduling model, and a short-term optimized scheduling scheme with balanced risk/benefit is obtained.
In summary, the invention provides a wind-solar-water-multi-energy complementary short-term optimization scheduling method for coupling power-off risks, which comprises the following steps: step one: taking the correlation of the multidimensional uncertainty factors into consideration, constructing a multivariate stochastic simulation model, and realizing the characterization of the multidimensional uncertainty; step two: establishing an electric discarding risk evaluation index, and identifying and quantifying the electric discarding 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 first step; step three: based on a risk/benefit balance theory, a wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk is constructed, the constructed wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk is solved by using a double-layer nesting algorithm, and a wind-light-water multi-energy complementary short-term optimization scheduling plan of the coupling power-off risk with balanced risk/benefit is obtained. The method provided by the invention couples the wind-solar-water multi-energy complementary power-discarding risk into the short-term optimization scheduling model, improves the operation benefit of the system on the premise of controllable risk, and can provide important and operable basis for short-term scheduling of the wind-solar-water multi-energy complementary system.
The above embodiments are merely illustrative of the technical solutions of the present invention. The short-term risk scheduling method of the watershed wind-light-water multi-energy complementary system with the coupling multi-dimensional uncertainty related to the invention is not limited to what is described in the embodiment, but the scope of the invention is defined by the claims. Any modifications, additions or equivalent substitutions made by those skilled in the art based on this embodiment are within the scope of the invention as claimed in the claims.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (5)
1. A wind-light-water multi-energy complementary short-term optimization scheduling method for coupling power-off risk is characterized by comprising the following steps:
step one: constructing a multivariate random simulation model according to the correlation of the multidimensional uncertainty factors;
step two: establishing an electric discarding risk evaluation index, and identifying and quantifying the electric discarding risk of the wind, light and water multi-energy complementary system according to the multivariate random simulation model established in the first step;
step three: based on a risk and benefit balance theory, constructing a wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk, solving the constructed wind-light-water multi-energy complementary system short-term optimization scheduling model of the coupling power-off risk by using a double-layer nesting algorithm, and obtaining a wind-light-water multi-energy complementary short-term optimization scheduling plan of the coupling power-off risk with balanced risk and benefit;
in the second step, when the electricity discarding rate is used as an electricity discarding risk evaluation index, the calculation formula of the electricity discarding risk quantification of the wind-solar-water multi-energy complementary system is as follows:
wherein: r is R c The average power rejection rate under multiple conditions is obtained; s is the total scene number; s is the scene number;
actual output of secondary solar wind, light and hydropower provided for a random simulation model; d (D) t The load given by the wind, light and water multi-energy complementary system is given to the electric power system;
in the third step, the short-term optimization scheduling model of the wind-solar-water multi-energy complementary system for coupling the power-saving 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 complementary system under the given condition of the power-on and wind-solar output, and the sum of the residual load squares is minimum under the condition of considering the power-saving risk; under the condition of given total output of a complementary system, the lower model optimizes a load distribution strategy among hydropower stations so as to maximize energy storage of the cascade hydropower stations;
the basic structure of the double-layer planning optimization model comprises:
wherein:L t is a large grid load; p (P) t hs Total output of the complementary system; lambda is a risk preference coefficient; p is the output matrix of each hydropower station in each scheduling period and is the decision variable of the upper model; r is a power generation flow matrix of each hydropower station in each period and is a decision variable of a lower model; m is the number of hydropower stations; k (K) m The comprehensive output coefficient of the hydropower station; i m,t Is the warehouse-in flow; q (Q) m,t Is the delivery flow; h j,t Is a water head; f is an objective function of the upper model; f is an objective function of the lower model; g and G are constraint functions;
in the double-layer nesting algorithm, an outer-layer algorithm is adopted aiming at an upper-layer model;
the outer layer algorithm adopts an intelligent algorithm to optimize the total output of the complementary system so as to minimize the objective function of the upper layer model;
in the double-layer nesting algorithm, an inner-layer algorithm is adopted aiming at a lower-layer model;
and determining a hydropower station inter-plant load distribution strategy by adopting a method of combining a discrimination coefficient and a full accumulation rate under the condition of giving total output of a complementary system by the inner layer algorithm, so that an objective function of a lower layer model is maximum.
2. The wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-curtailment risk according to claim 1, wherein in the first step, for multi-dimensional uncertainty factors with obvious correlation, a multi-variable joint probability distribution function is constructed based on a Copula joining function, and the multi-variable joint probability distribution function is as follows:
F(X 1 ,X 2 ,X 3 …,X n )＝C[F(X 1 ),F(X 2 ),F(X 3 )…F(X n )]；
wherein: f (X) 1 )、F(X 2 )、F(X 3 )…F(X n ) Respectively an edge probability distribution function of the prediction error; c is a Copula joining function.
3. The wind-solar-water multi-energy complementary short-term optimization scheduling method for coupling power-off risk according to claim 1, wherein in the first step, random simulation is performed on mutually independent multidimensional uncertainty factors by adopting a time sequence model; when the randomly simulated multi-dimensional uncertainty factors have correlation, selecting one multi-dimensional uncertainty factor as a main variable, and selecting other multi-dimensional uncertainty factors as auxiliary variables; firstly, carrying out random simulation on a main variable to generate a long series of samples with a plurality of time kerfs, and then, sequentially carrying out simulation on other auxiliary variables based on a Bayesian conditional probability formula, wherein the Bayesian conditional probability formula is as follows:
wherein: u (u) 1 ＝F(X 1 )，F(X 1 ) An edge probability distribution function for the prediction error; c is a Copula joining function; z is Z n Is Bayesian conditional probability; f (X) 2 )、F(X 3 )…F(X n ) Respectively an edge probability distribution function of the prediction error; r is R c Representing the average power rejection rate in multiple scenarios.
4. The method for scheduling wind, light and water multi-energy complementary short-term optimization of coupling power-curtailment risk according to claim 1, wherein in the first step, the multi-dimensional uncertainty factors comprise load prediction errors, runoff prediction errors, wind power output prediction errors and photoelectric output prediction errors.
5. The wind-solar-water-multipotent complementary short-term optimization scheduling method for coupling power-curtailment risk according to claim 4, wherein the power curtailment rate is the ratio of the new energy power curtailment rate to the actual power generation rate.
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