CN110601184A - Wind power plant extended photovoltaic multi-objective optimization method considering boost main transformer capacity - Google Patents
Wind power plant extended photovoltaic multi-objective optimization method considering boost main transformer capacity Download PDFInfo
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- 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
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- 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
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- 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
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Abstract
The invention discloses a wind power plant extended photovoltaic multi-target optimization method considering the capacity of a boosting main transformer, which improves an independent wind-solar meteorological model, considers the correlation between the change of wind speed and solar radiation intensity, and converts meteorological data into power generation output data; constructing a multi-target optimization model which takes the site area and the capacity of the boosting main transformer as constraint conditions and takes the maximum utilization rate of the boosting main transformer and the minimum power variation coefficient and clean energy waste rate as objective functions; on the basis of a Monte Carlo method, long-term meteorological data of a researched area are obtained by taking hours as time scales through simulation, a Pareto optimal solution set is solved by adopting a multi-target particle swarm algorithm, and the influence of expansion of the capacity of a main booster transformer on a final scheme is considered.
Description
Technical Field
The invention relates to the field of new energy power generation, in particular to a wind power plant extended photovoltaic multi-target optimization method considering the capacity of a boosting main transformer.
Background
In recent years, wind power and photovoltaic are rapidly developed, and installed capacity and permeability in a power grid of the wind power and photovoltaic reach higher levels. Wind power and photovoltaic are used as intermittent energy sources, and the potential randomness and fluctuation of the intermittent energy sources limit the consumption capacity of a power grid on wind and photovoltaic power generation, so that the running economy of a power generation system is reduced. Meanwhile, the wind power and the photovoltaic have natural complementarity in time, the wind power output is small in the daytime, the photovoltaic output is large, and the wind power output and the photovoltaic output are opposite at night; wind power output is large in rainy days, photovoltaic output is small, and sunny days are opposite. The wind-solar hybrid is fully utilized, so that the aggregate power fluctuation of a power generation system can be stabilized to a certain extent, and the operation economy and the absorption capacity of a power grid to the power generation system are improved. At present, most of the established wind and light power generation systems are single power generation systems, especially single wind power plants. Therefore, upgrading and modifying the built wind power plant and expanding photovoltaic power generation to fully utilize wind-solar complementation have important significance for the development of renewable energy power generation.
Currently, research and design on capacity optimization of wind-solar hybrid power generation systems are mainly directed to newly planned power generation systems, and the problem of expanding an established single power generation system into a complementary power generation system is less concerned. The optimal configuration method of the wind-solar hybrid power generation system can be divided into a single-target optimization method and a multi-target optimization method. The single-target optimization method only considers one optimization target, so that contradictions among all factors of the problem cannot be reflected well, and the multi-target optimization method can reflect the problem more comprehensively and improve the accuracy of the optimization result. In addition, in most of current researches and designs, the utilization level of main electrical equipment of a power plant is not considered, so that the utilization rate of electrical equipment such as a step-up main transformer of a power generation system is low, and the engineering economy and the practicability are limited. In the optimization process of the expanded photovoltaic of the established wind power plant, how the capacities of main electrical equipment represented by a main booster transformer are matched to improve the comprehensive performance of the expanded wind-solar hybrid power generation system is a problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the problems and provides a wind power plant extended photovoltaic multi-target optimization method considering the capacity of a boosting main transformer, a wind-light probability distribution model is constructed based on the analysis of wind-light distribution characteristics of a research region, long-term wind speed and solar radiation intensity data are generated by using a Monte Carlo method, and the long-term wind speed and solar radiation intensity data are further converted into power generation output data; comprehensively considering contradictions among the utilization rate of a boosting main transformer of the power generation system, the variation coefficient of the polymerization power and the clean energy waste rate, constructing a multi-target optimization model, and solving a Pareto optimal solution set of the multi-target optimization model by adopting a multi-target particle swarm algorithm; and the influence of different boosting main transformer capacities on the optimization result is contrastively analyzed and expanded, the Pareto optimal solution set under multiple scenes is calculated, and the final engineering scheme is determined by the investor.
In order to achieve the purpose, the invention adopts the following specific scheme:
the invention discloses a wind power plant extended photovoltaic multi-objective optimization method considering the capacity of a boosting main transformer, which comprises the following steps of:
(1) analyzing wind and light distribution characteristics of a set area, initializing a wind and light probability distribution model and a power generation system output model of the set area, and simulating by using a Monte Carlo method to obtain power generation output data in a set time period of the set area;
(2) initializing the constructed multi-target optimization model, and solving a Pareto optimal solution set by adopting a multi-target particle swarm algorithm;
(3) if the capacity of the boosting main transformer is expanded, returning to the step (2), updating the constraint conditions of the multi-objective optimization model, and re-solving, otherwise, executing the step (4);
(4) and analyzing the solved Pareto optimal solution set, and determining a final engineering scheme.
Further, in the step (1), analyzing and researching the wind and light distribution characteristics of the region to obtain relevant parameters of a probability model of wind speed and solar radiation intensity of the region.
Further, in the step (1), the wind speed probability model specifically includes:
wherein v is the instantaneous value of the wind speed; k is a shape parameter; and c is a scale parameter.
Further, in the step (1), the solar radiation intensity probability model specifically includes:
wherein HhThe instantaneous value of the solar radiation intensity at the moment h; hmhThe maximum value of the solar radiation intensity at the moment h; alpha is alphahAnd betahIs the shape of the h timeA state parameter.
Further, in the step (1), the power generation system output model specifically includes:
wherein v iswAnd vrefRespectively the wind speed at the hub position and the measurement position of the fan; h iswAnd hrefRespectively measuring the position height of a hub of the fan and the wind speed; gamma is a wind speed conversion factor; pwAnd PrRespectively representing the generating power and the rated power of the wind driven generator in an ideal state; v. ofsTo cut into the wind speed; v. ofpCutting out the wind speed; v. ofrRated wind speed; pwdGenerating power for the total wind power; n is a radical ofwThe number of the wind generating sets is the number of the wind generating sets; etawThe generating efficiency of the wind generating set; ppvIs photovoltaic power generation power; a. thepThe mounting area of the photovoltaic cell array; etapThe comprehensive power generation efficiency of photovoltaic power generation is obtained; poOutputting power for the wind-solar hybrid power generation system; pomaxThe maximum output power of the system is kW.
Further, in the step (1), data of an hour time scale of a set time period of the set area is obtained by a monte carlo method simulation.
Further, in the step (2), the objective function of the multi-objective optimization model includes output power volatility, electrical equipment utilization rate, and wind and light abandoning electric quantity, and specifically includes:
wherein, CvIs the output power variation coefficient; n is the total length of the data sequence; poiThe output power of the complementary power generation system at the ith hour; u. ofpoIs the average of annual output power; rUOTPThe utilization rate of a main step-up transformer is obtained; ptThe total capacity of a main transformer of the system is boosted; rLOEPWaste rate for clean energy; pwdiThe output of the wind generating set in the ith hour is obtained; ppviThe output at the ith hour of the photovoltaic cell array is kW; and m is the number of hours that the combined wind and light output is greater than the maximum output power.
Further, in the step (2), the optimization objectives of the multi-objective optimization model are all minimization objectives, which specifically include:
f1＝min(Cv)
f2＝min(1-RUOTP)
f3＝min(RLOEP)
further, in the step (2), the constraint conditions of the multi-objective optimization model include site area constraint and boost main transformer capacity constraint, and specifically include:
0≤Ap≤Ap max
Pomax-Pt＝0
Pt-(Pto+Ptn)＝0
wherein A ispmaxThe maximum installation area for photovoltaic power generation; ptoThe capacity of the original step-up main transformer is obtained; ptnThe capacity of the main booster transformer is expanded.
Further, in the step (2), the multi-objective particle swarm algorithm is used for solving a Pareto optimal solution set of the optimization model.
Further, in the step (3), the extended boost main transformer capacity represents different planning scenarios corresponding to Pomax。
Further, in the step (4), the final engineering project should be selected from Pareto optimal solution sets of different scenes, and should be determined by the investor according to requirements of engineering investment cost, expected income, various optimization target ranges and the like.
The invention has the beneficial effects that:
the invention provides a wind power plant extended photovoltaic multi-target optimization method considering the capacity of a boosting main transformer, and compared with the prior art, the wind power plant extended photovoltaic multi-target optimization method has the following beneficial effects:
1) the method considers the problem of whether the capacity of the boosting main transformer is expanded or not in the process of expanding the photovoltaic power generation of the wind power plant, can provide a Pareto optimal solution set under a plurality of planning scenes, fully considers different planning scenes and provides a planning scheme with better economical efficiency and indexes for a sponsor;
2) the invention adopts a multi-target optimization method, can reflect the engineering requirements more comprehensively, fully considers the contradiction among three indexes of output power fluctuation, the utilization rate of electrical equipment and the wind and light abandoning electric quantity, adopts a multi-target particle swarm algorithm to obtain a Pareto optimal solution set, and determines a final engineering scheme by an investor, thereby better meeting the requirements of the investor and having higher advancement and practical engineering application value;
3) the method has no special application condition, has strong universality, is suitable for planning the extended photovoltaic power generation capacity of the wind power plants in various regions, and has popularization value and significance;
4) the method has the advantages of clear principle, simple operation and low requirements on execution environment and maintenance, and is suitable for practical engineering application.
Drawings
FIG. 1 is a schematic diagram of an execution flow of an extended photovoltaic capacity optimization configuration method of a wind power plant;
FIG. 2 is a schematic diagram of a Pareto front end when the main step-up transformer is not expanded in the embodiment;
FIG. 3 is a schematic diagram of a Pareto front end when the main transformer of the extended booster in the embodiment is used;
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
as shown in fig. 1, a wind farm extended photovoltaic multi-objective optimization method considering boost main transformer capacity includes:
(1) analyzing wind and light distribution characteristics of a research area, initializing a wind and light probability distribution model and a power generation system output model, and simulating by using a Monte Carlo method to obtain long-term power generation output data of the research area;
(2) initializing the constructed multi-target optimization model, and solving a Pareto optimal solution set by adopting a multi-target particle swarm algorithm;
(3) if the capacity of the booster main transformer is expanded, returning to the step (2), updating the constraint conditions of the constructed multi-target optimization model, and re-solving the Pareto optimal solution set by adopting a multi-target particle swarm algorithm, otherwise, executing the step (4);
(4) and analyzing the calculation result and determining a final engineering scheme.
In the step (1), analyzing and researching the wind and light distribution characteristics of the region to obtain the relevant parameters of the probability model of the wind speed and the solar radiation intensity of the region.
In the step (1), the wind speed probability model specifically comprises:
wherein v is the instantaneous value of the wind speed; k is a shape parameter; and c is a scale parameter.
In the step (1), the solar radiation intensity probability model specifically comprises:
wherein HhThe instantaneous value of the solar radiation intensity at the moment h; hmhThe maximum value of the solar radiation intensity at the moment h; alpha is alphahAnd betahIs the shape parameter at time h.
In the step (1), the output model of the power generation system is specifically as follows:
wherein v iswAnd vrefRespectively the wind speed at the hub position and the measurement position of the fan; h iswAnd hrefRespectively measuring the position height of a hub of the fan and the wind speed; gamma is a wind speed conversion factor; pwAnd PrRespectively representing the generating power and the rated power of the wind driven generator in an ideal state; v. ofsTo cut into the wind speed; v. ofpCutting out the wind speed; v. ofrRated wind speed; pwdGenerating power for the total wind power; n is a radical ofwThe number of the wind generating sets is the number of the wind generating sets; etawThe generating efficiency of the wind generating set; ppvIs photovoltaic power generation power; a. thepThe mounting area of the photovoltaic cell array; etapThe comprehensive power generation efficiency of photovoltaic power generation is obtained;Pooutputting power for the wind-solar hybrid power generation system; pomaxThe maximum output power of the system is kW.
In the step (1), data of an hour time scale of a set time period of the set region is obtained by a Monte Carlo method simulation, and the set time period can be 10-15 years.
In the step (2), the objective function of the multi-objective optimization model includes output power volatility, electrical equipment utilization rate and wind and light abandoning electric quantity, and specifically includes:
wherein, CvIs the output power variation coefficient; n is the total length of the data sequence; poiThe output power of the complementary power generation system at the ith hour; u. ofpoIs the average of annual output power; rUOTPThe utilization rate of a main step-up transformer is obtained; ptThe total capacity of a main transformer of the system is boosted; rLOEPWaste rate for clean energy; pwdiThe output of the wind generating set in the ith hour is obtained; ppviThe output at the ith hour of the photovoltaic cell array is kW; and m is the number of hours that the combined wind and light output is greater than the maximum output power.
In the step (2), the optimization objectives of the multi-objective optimization model are all minimization objectives, which specifically include:
f1＝min(Cv)
f2＝min(1-RUOTP)
f3＝min(RLOEP)
in the step (2), the constraint conditions of the multi-objective optimization model comprise site area constraint and boost main transformer capacity constraint, and specifically comprise:
0≤Ap≤Ap max
Pomax-Pt＝0
Pt-(Pto+Ptn)＝0
wherein A ispmaxThe maximum installation area for photovoltaic power generation; ptoThe capacity of the original step-up main transformer is obtained; ptnThe capacity of the main booster transformer is expanded.
In the step (2), the multi-objective particle swarm algorithm is used for solving a Pareto optimal solution set of the optimization model.
In the step (3), the capacity of the expanded boosting main transformer represents the corresponding system maximum output power P in different planning scenesomax。
In the step (4), the final engineering scheme should be selected from Pareto optimal solution sets of different scenes, and should be determined by the investor according to requirements of engineering investment cost, expected income, various optimization target ranges and the like.
Examples
According to the method, existing wind power plant expansion photovoltaic power generation capacity engineering in a certain area of Shandong province is taken as an embodiment, and Pareto front ends obtained through optimization under two scenes of not expanding a boosting main transformer and expanding a 50MVA boosting main transformer are respectively shown in fig. 2 and fig. 3. Assuming that the project investor requires that the utilization rate of the boosting main transformer reaches 35%, the waste rate of clean energy is not more than 2%, and the lower the volatility is, the better the volatility is, the final scheme is selected to be the scheme of expanding the boosting main transformer without expanding 32.2 hectares of photovoltaics, and the scheme is shown in table 1.
TABLE 1 Final engineering protocol
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (5)
1. A wind power plant extended photovoltaic multi-objective optimization method considering the capacity of a boosting main transformer is characterized by comprising the following steps of:
(1) analyzing wind and light distribution characteristics of a set area, initializing a wind and light probability distribution model and a power generation system output model of the set area, and simulating by using a Monte Carlo method to obtain power generation output data in a set time period of the set area;
(2) initializing the constructed multi-target optimization model, and solving a Pareto optimal solution set by adopting a multi-target particle swarm algorithm;
(3) if the capacity of the booster main transformer is expanded, returning to the step (2), updating the constraint conditions of the constructed multi-target optimization model, and re-solving the Pareto optimal solution set by adopting a multi-target particle swarm algorithm, otherwise, executing the step (4);
(4) and analyzing the solved Pareto optimal solution set, and determining a final engineering scheme.
2. The wind power plant extended photovoltaic multi-objective optimization method considering the capacity of the boost main transformer according to claim 1, wherein in the step (1), the wind and light distribution characteristics of a set region are analyzed to obtain relevant parameters of a probability model of wind speed and solar radiation intensity of the region;
the wind speed probability model specifically comprises the following steps:
wherein v is the instantaneous value of the wind speed; k is a shape parameter; c is a scale parameter;
the solar radiation intensity probability model specifically comprises the following steps:
wherein HhThe instantaneous value of the solar radiation intensity at the moment h; hmhThe maximum value of the solar radiation intensity at the moment h; alpha is alphahAnd betahThe shape parameter at the h moment;
the output model of the power generation system is specifically as follows:
wherein v iswAnd vrefRespectively the wind speed at the hub position and the measurement position of the fan; h iswAnd hrefRespectively measuring the position height of a hub of the fan and the wind speed; gamma is a wind speed conversion factor; pwAnd PrRespectively representing the generating power and the rated power of the wind driven generator in an ideal state; v. ofsTo cut into the wind speed; v. ofpCutting out the wind speed; v. ofrRated wind speed; pwdGenerating power for the total wind power; n is a radical ofwThe number of the wind generating sets is the number of the wind generating sets; etawThe generating efficiency of the wind generating set; ppvIs photovoltaic power generation power; a. thepThe mounting area of the photovoltaic cell array; etapThe comprehensive power generation efficiency of photovoltaic power generation is obtained; poOutputting power for the wind-solar hybrid power generation system; pomaxThe maximum output power kW of the system is obtained;
and simulating by using a Monte Carlo method to obtain the data of the hour time scale of the set time period of the set region.
3. The wind power plant extended photovoltaic multi-objective optimization method considering the capacity of the boost main transformer in the step (2), wherein the objective function of the multi-objective optimization model comprises output power volatility, electrical equipment utilization rate and wind curtailment and light curtailment electric quantity, and specifically comprises the following steps:
wherein, CvIs the output power variation coefficient; n is the total length of the data sequence; poiThe output power of the complementary power generation system at the ith hour; u. ofpoIs the average of annual output power; rUOTPThe utilization rate of a main step-up transformer is obtained; ptThe total capacity of a main transformer of the system is boosted; rLOEPWaste rate for clean energy; pwdiThe output of the wind generating set in the ith hour is obtained; ppviThe output at the ith hour of the photovoltaic cell array is kW; m is the hours when the wind-solar combined output is greater than the maximum output power;
the optimization targets of the multi-target optimization model are all minimization targets, and specifically comprise the following steps:
f1＝min(Cv)
f2＝min(1-RUOTP)
f3＝min(RLOEP)
the constraint conditions of the multi-objective optimization model comprise site area constraint and boost main transformer capacity constraint, and specifically comprise the following steps:
0≤Ap≤Apmax
Pomax-Pt＝0
Pt-(Pto+Ptn)＝0
wherein A ispmaxThe maximum installation area for photovoltaic power generation; ptoThe capacity of the original step-up main transformer is obtained; ptnThe capacity of the booster main transformer is expanded;
and solving the Pareto optimal solution set of the optimization model by utilizing a multi-objective particle swarm algorithm according to the objective function, the optimization objective and the constraint condition of the multi-objective optimization model.
4. The wind power plant extended photovoltaic multi-objective optimization method considering capacity of the boosting main transformer in claim 1, wherein in the step (3), the capacity of the extended boosting main transformer represents the maximum output power P of the corresponding system of different planning scenesomax。
5. The wind power plant extended photovoltaic multi-objective optimization method considering the main transformer capacity of the voltage boosting unit as claimed in claim 1, wherein in the step (4), the final engineering scheme should be selected from Pareto optimal solution sets of different scenes and should be determined by investors according to engineering investment cost, expected income and the requirements of various optimization objective ranges.
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