CN113394807A - Method and device for optimizing installed ratio of clean energy complementary base - Google Patents

Method and device for optimizing installed ratio of clean energy complementary base Download PDF

Info

Publication number
CN113394807A
CN113394807A CN202110696936.2A CN202110696936A CN113394807A CN 113394807 A CN113394807 A CN 113394807A CN 202110696936 A CN202110696936 A CN 202110696936A CN 113394807 A CN113394807 A CN 113394807A
Authority
CN
China
Prior art keywords
solution
optimization
complementary base
clean energy
energy complementary
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
CN202110696936.2A
Other languages
Chinese (zh)
Other versions
CN113394807B (en
Inventor
张金良
杨建�
金鑫
胡会永
姜勃
张远生
姚帅
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.)
Yellow River Engineering Consulting Co Ltd
Original Assignee
Yellow River Engineering Consulting Co Ltd
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 Yellow River Engineering Consulting Co Ltd filed Critical Yellow River Engineering Consulting Co Ltd
Priority to CN202110696936.2A priority Critical patent/CN113394807B/en
Publication of CN113394807A publication Critical patent/CN113394807A/en
Application granted granted Critical
Publication of CN113394807B publication Critical patent/CN113394807B/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
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S10/00—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power
    • H02S10/10—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power including a supplementary source of electric power, e.g. hybrid diesel-PV energy systems
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S10/00—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power
    • H02S10/10—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power including a supplementary source of electric power, e.g. hybrid diesel-PV energy systems
    • H02S10/12—Hybrid wind-PV energy systems
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to an optimization method and a device for a clean energy complementary base installed ratio, wherein the method comprises the following steps: acquiring design load of a clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate; determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables; under an optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to design load and output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to photovoltaic installed capacity, wind power installed capacity and pumped storage installed capacity; iterative optimization is carried out by adopting a flock optimization algorithm, and a final optimal solution is selected from the configuration scheme set, so that the total investment of the clean energy complementary base is minimum.

Description

Method and device for optimizing installed ratio of clean energy complementary base
Technical Field
The disclosure relates to the technical field of clean energy, in particular to a method and a device for optimizing the installed ratio of a clean energy complementary base.
Background
In a wind, light, water and storage multi-energy complementary system, the system can stably output electric energy by reasonably configuring the capacities of a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station and adjusting the output relationship among the photovoltaic power station, the wind power station, the hydropower station and the pumped storage power station. In the aspect of installed capacity configuration optimization, because the optimization problem has the nonlinear characteristic, a cluster intelligent method is mostly adopted to research the installed configuration optimization.
The cluster intelligence refers to the overall intelligent behavior generated in the process of interaction of a plurality of simple-behavior individuals. The cluster intelligent method is mainly used for solving complex problems, originates from the simulation of the 'trend to the best' behavior of biological groups, is suitable for solving complex optimization problems, and has outstanding performance in solving various combination optimization and function optimization problems. For example, the existing Particle Swarm Optimization (PSO) is considered as efficient and simple global optimization due to its simple structure, few parameters to be adjusted, and easy implementation, and has better effects in the aspects of multi-peak, multi-objective optimization, constraint optimization, etc., but the PSO still has a certain improvement space.
Disclosure of Invention
In order to overcome the problems in the related art, the disclosure provides a method and a device for optimizing the installed ratio of a clean energy complementary base, so as to optimize the installed ratio of the clean energy complementary base, and improve the sorting quality and efficiency of the optimizing process.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for optimizing installed proportion of a clean energy base, where the clean energy base includes a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, the method includes:
acquiring design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate;
determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
under the optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and performing iterative optimization by adopting a flock optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
In one embodiment, preferably, the objective function structure includes:
Fi(t)＝PUwind×Pwind(t)+PUpv×Ppv(t)+PUps×Pps(t)
fi (t) represents the total investment of a certain installed scheme of the clean energy complementary base at the moment t, and PUwind，PUpv，PUpsRespectively representing the investment amount of a unit kW of a wind power station, a photovoltaic power station and a pumped storage power station;
the vector X to be optimizedi(t) is expressed as:
Xi(t)＝(Pwind(t),Ppv(t),Pps(t))
wherein, Pwind(t),Ppv(t),Pps(t) respectively representing the wind power installation machine at the moment tCapacity, photovoltaic installed capacity and pumped storage installed capacity.
In one embodiment, preferably, the iterative optimization using a herd optimization algorithm, selecting an optimal solution from the set of configuration solutions to minimize the total investment of the complementary bases of clean energy, includes:
determining whether the iterative optimization process falls into local optimum by checking a supervisory operator;
wherein, the discriminant formula of the inspection and supervision operator is expressed as:
Figure BDA0003128855860000031
Figure BDA0003128855860000032
indicating the new position of the ith solution affected by the optimal solution at time t,
Figure BDA0003128855860000033
representing a new position of the ith solution influenced by the optimal solution at the time of t-1, wherein theta represents a preset threshold value;
when the local optimum is determined, executing a jump-out local optimum mechanism, and resetting a part of solution sets in the solution set of the configuration scheme, wherein the solution set needing to be reset is determined by adopting the following formula:
rand(0,1)＜p
here, rand (0,1) represents a random number, and p represents a set probability threshold.
In one embodiment, preferably, the method further comprises:
when the situation that the solution is not trapped into the local optimum is determined, adopting a global optimizing operator and a local optimizing operator to calculate corresponding calling vectors, and randomly dispersing all solutions;
and (4) eliminating and resetting the solution with the fitness function value inferior to the average fitness by adopting a superior-inferior operator until the solution set is converged.
In one embodiment, preferably, the global optimization operator is calculated by using the following formula:
Figure BDA0003128855860000034
wherein,
Figure BDA0003128855860000035
represents the new position of the ith solution influenced by the optimal solution moving track at the moment t,
Figure BDA0003128855860000036
represents the position of the optimal solution, and each solution except the optimal solution is gathered close to the position, deltaiRepresenting the summons vector between the ith other solution and the optimal solution, c1And c2Is a random coefficient, and is a random coefficient,
Figure BDA0003128855860000037
wherein r is1,r2Is a random number between (0,1), T represents the total number of iterations, c1Is a random number between (0, 2) and represents the calling force of the optimal solution, when c1>When 1, the influence of the optimal solution is enhanced, otherwise, the influence is weakened; c. C2As dynamic random numbers, c increases with simulation time2Is linearly decreased from 1 to 0, and alpha represents c2The initial range of the coefficients limits the range of influence of the optimal solution.
In one embodiment, preferably, the local optimization operator is calculated using the following formula:
Figure BDA0003128855860000041
wherein,
Figure BDA0003128855860000042
denotes the position, x, of the ith solution after self-movement at time ti,d(t) representing the ith solution at time tOriginal position, R and P represent [ -1, 1 [ ]]Beta represents a modulation coefficient randomly explored, and the empirical value range is (0, 5) and deltaiRepresenting the summons vector between the ith other solution and the optimal solution, R εiRepresenting the movement trajectory of the ith solution.
In one embodiment, preferably, based on the combined action of the global optimizing operator and the local optimizing operator, the update iteration formula of each solution shift position is expressed as:
Figure BDA0003128855860000043
wherein x isi,d(t +1) represents the position of the ith solution at time t +1,
Figure BDA0003128855860000044
indicating the position of the ith solution after self-movement at time t,
Figure BDA0003128855860000045
represents the new position of the ith solution at the time t after being influenced by the optimal solution moving track, b represents a coefficient linearly reduced from 1 to 0 according to the iteration number, and r3Is [0, 1 ]]A random number is added to the random number,
Figure BDA0003128855860000046
the weight assignment showing that the ith solution moves itself at time t is affected in two ways.
According to a second aspect of the embodiments of the present disclosure, there is provided a device for optimizing installed ratio of a complementary base of clean energy including a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, the device including:
the acquisition module is used for acquiring the design load of the clean energy complementary base, the output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandon rate and power supply guarantee rate;
the determining module is used for determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
the generating module is used for randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources under the optimization constraint condition, wherein each configuration scheme solution comprises a vector to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and the selection module is used for carrying out iterative optimization by adopting a flock optimization algorithm and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
According to a third aspect of the embodiments of the present disclosure, there is provided a device for optimizing installed ratio of a complementary base of clean energy including a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, the device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate;
determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
under the optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and performing iterative optimization by adopting a flock optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the invention, the clean energy base comprises a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, and the installed capacity of each station is calculated by adopting a flock optimization algorithm, so that the total investment of the clean energy complementary base is minimum, the installed proportion of the clean energy complementary base is optimized, and the sorting quality and efficiency in the optimization process are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a method for optimizing a clean energy complementary base installed ratio according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating the detailed optimization of mathematical operators of the herd optimization algorithm, according to an exemplary embodiment.
FIG. 3 is a diagram illustrating the convergence curve of an optimal solution obtained by applying 3 optimization algorithms to 6 test functions according to an exemplary embodiment.
FIG. 4 is a histogram illustrating an installed mix plan and corresponding investment resulting from utilizing two optimization algorithms according to an exemplary embodiment.
FIG. 5 is a graph illustrating the impact of power curtailment and assurance on an investment for a larger investment situation in accordance with an exemplary embodiment.
FIG. 6 is a graph illustrating the impact of power curtailment and assurance on an investment for a small investment, according to an exemplary embodiment.
FIG. 7 is a schematic diagram illustrating a variation of the most economical installation configuration under the constraint of power curtailment according to an exemplary embodiment.
Fig. 8 is a block diagram illustrating an apparatus for optimizing clean energy complementary base installed ratio according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
During the grazing or foraging activities of the flocks of sheep, the flocks of sheep show two states, wherein one state is that in the process of eating grass by the flocks of sheep, the movement of each sheep in the flocks of sheep shows certain randomness to form a relatively scattered tissue structure, and an individual can search tender grass around the individual to eat; the other state is that during the movement of the flocks, the flocks move rapidly as a whole due to the influence of the driving of the herds, food searching or other dangerous factors, wherein the strongest is called a leader sheep, the movement of the herds leads the movement of the whole flocks, other sheep perform blind mass behaviors, and the whole flocks show rapid response capability and extremely high mass movement capability.
The invention is inspired by Sheep Flock behaviors, abstracts key factors of a Sheep Flock grazing process, simulates a new cluster intelligent algorithm, and pays attention to improvement of Optimization quality of the algorithm, and considers factors including numerical values of optimal solutions, time consumption of Optimization, avoidance of local Optimization and the like, and provides a Sheep Flock Optimization algorithm (SFO).
Fig. 1 is a flow chart illustrating a method for optimizing installed ratio of a clean energy complementary base, including a photovoltaic power plant, a wind power plant, a hydroelectric power plant, and a pumped-storage power plant, as shown in fig. 1, according to an exemplary embodiment, the method including:
step S101, obtaining design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandon rate and power supply guarantee rate;
step S102, determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
step S103, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources under the optimization constraint condition, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and step S104, iterative optimization is carried out by adopting a flock optimization algorithm, and a final optimal solution is selected from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
In one embodiment, preferably, the objective function structure includes:
Fi(t)＝PUwind×Pwind(t)+PUpv×Ppv(t)+PUps×Pps(t)
fi (t) represents the total investment of a certain installed scheme of the clean energy complementary base at the moment t, and PUwind，PUpv，PUpsRespectively representing the investment amount of a unit kW of a wind power station, a photovoltaic power station and a pumped storage power station;
the vector X to be optimizedi(t) is expressed as:
Xi(t)＝(Pwind(t),Ppv(t),Pps(t))
wherein, Pwind(t),Ppv(t),PpsAnd (t) respectively representing the wind power installed capacity, the photovoltaic installed capacity and the pumped storage installed capacity at the moment t.
As shown in fig. 2, in one embodiment, preferably, the step S104 includes:
step S201, determining whether the iterative optimization process falls into local optimization through checking a supervision operator;
wherein, the discriminant formula of the inspection and supervision operator is expressed as:
Figure BDA0003128855860000081
Figure BDA0003128855860000082
indicating the new position of the ith solution affected by the optimal solution at time t,
Figure BDA0003128855860000083
representing a new position of the ith solution influenced by the optimal solution at the time of t-1, wherein theta represents a preset threshold value;
step S202, when the local optimum is determined, executing a jump-out local optimum mechanism, and resetting a part of solution sets in the solution set of the configuration scheme, wherein the solution set which needs to be reset is determined by adopting the following formula:
rand(0,1)＜p
here, rand (0,1) represents a random number, and p represents a set probability threshold.
Step S203, when the situation that the local optimum is not involved is determined, a global optimizing operator and a local optimizing operator are adopted to calculate corresponding calling vectors, and solutions are randomly dispersed;
and S204, eliminating and resetting the solution with the fitness function value inferior to the average fitness by adopting a superior/inferior operator, and repeating the steps until the solution set is converged.
In one embodiment, preferably, the global optimization operator is calculated by using the following formula:
Figure BDA0003128855860000091
wherein,
Figure BDA0003128855860000092
represents the new position of the ith solution influenced by the optimal solution moving track at the moment t,
Figure BDA0003128855860000093
represents the position of the optimal solution, and each solution except the optimal solution is gathered close to the position, deltaiRepresenting the summons vector between the ith other solution and the optimal solution, c1And c2Is a random coefficient, and is a random coefficient,
Figure BDA0003128855860000094
wherein r is1,r2Is a random number between (0,1), T represents the total number of iterations, c1Is a random number between (0, 2) and represents the calling force of the optimal solution, when c1>When 1, the influence of the optimal solution is enhanced, otherwise, the influence is weakened; c. C2As dynamic random numbers, c increases with simulation time2Is linearly decreased from 1 to 0, and alpha represents c2The initial range of the coefficients limits the range of influence of the optimal solution.
In one embodiment, preferably, the local optimization operator is calculated using the following formula:
Figure BDA0003128855860000095
wherein,
Figure BDA0003128855860000096
denotes the position, x, of the ith solution after self-movement at time ti,d(t) represents the original position of the ith solution at time t, and R and P represent [ -1, 1]Beta represents a modulation coefficient randomly explored, and the empirical value range is (0, 5) and deltaiRepresenting the summons vector between the ith other solution and the optimal solution, R εiRepresenting the movement trajectory of the ith solution.
In one embodiment, preferably, based on the combined action of the global optimizing operator and the local optimizing operator, the update iteration formula of each solution shift position is expressed as:
Figure BDA0003128855860000097
wherein x isi,d(t +1) represents the position of the ith solution at time t +1,
Figure BDA0003128855860000098
indicating the position of the ith solution after self-movement at time t,
Figure BDA0003128855860000101
represents the new position of the ith solution at the time t after being influenced by the optimal solution moving track, b represents a coefficient linearly reduced from 1 to 0 according to the iteration number, and r3Is [0, 1 ]]A random number is added to the random number,
Figure BDA0003128855860000102
the weight assignment showing that the ith solution moves itself at time t is affected in two ways.
In the invention, 10 typical benchmark test functions shown in table 1 are respectively optimized and calculated by applying a traditional Particle Swarm Optimization (PSO), an artificial sheep swarm optimization (ASA) and the algorithm provided by the invention, and the results are compared.
TABLE 1
Figure BDA0003128855860000103
Figure BDA0003128855860000111
For the above exemplary benchmark test function, the average convergence value of the minimum value problem processed by using 3 optimization algorithms and the standard deviation comparison of multiple convergence results (30 operations) are shown in table 2. From the quality of the optimizing result, the optimal solutions found by the No. 3, the No. 4 and the No. 8 functions are very close, and the performances of the 3 algorithms are equivalent; for the No. 7 and No. 10 functions, the latter two sheep swarm algorithms are better in performance than the particle swarm algorithm; for other 5 functions, the algorithm is prominent, and a better solution is found compared with other two algorithms. Compared with a particle swarm algorithm and an artificial lamb swarm algorithm, the stability of the standard difference analysis algorithm based on the repeated optimization calculation result is slightly higher than that of the standard difference of the algorithm for the No. 1 function and the No. 7 function, and the standard difference of the algorithm is better than that of the other two algorithms for all other functions, so that the stability is better.
TABLE 2
Figure BDA0003128855860000112
Figure BDA0003128855860000121
Fig. 3 shows the optimal solution convergence curve obtained by applying 3 optimization algorithms to 6 test functions, which is easy to see that the algorithm has fast convergence speed and excellent optimization efficiency.
In conclusion, compared with the particle swarm algorithm and the artificial lamb swarm algorithm, the SFO algorithm provided in the research not only absorbs the characteristic of rapid convergence of the artificial lamb swarm algorithm, but also overcomes the defects of early convergence, local optimum and the like, and has the advantages of higher convergence speed and better calculation stability. Therefore, this algorithm can be applied to the actual installed capacity configuration optimization.
The method comprises the steps of carrying out optimization research on installation configuration by taking a yellow river wind, light, water and pumping storage multiple renewable clean energy base as a typical case, establishing an optimization model to analyze capacity configuration, carrying out optimization on the capacity configuration by taking photovoltaic, wind power and pumping storage installation (the dimension is 3) as optimization variables and taking an objective function as the minimum total investment (single-target optimization) of the base, carrying out trial calculation on the objective function for multiple times by respectively applying a particle swarm algorithm and the algorithm, and comparing installation proportioning structures obtained by the two optimization methods and corresponding investment calculation results.
In optimization calculation, the variables to be optimized are photovoltaic capacity, wind power capacity and installed capacity of pumping water and storing energy, and P is used for each variablewind，Ppv，PpsThe vector to be optimized is represented, so:
Xi(t)＝(Pwind(t),Ppv(t),Pps(t))
the dimension dim is 3, the optimal solution vector needs to be calculated in each iterative optimization, namely the position coordinate of the leading sheep, and the optimal solution vector is used
Figure BDA0003128855860000131
Representing, and influence of flock trajectories using summons vectors
Figure BDA0003128855860000132
And (4) showing.
The objective function fi (t) of the algorithm is the total investment of the base, and is calculated by the following formula (8):
Fi(t)＝PUwind×Pwind(t)+PUpv×Ppv(t)+PUps×Pps(t) (8)
in the formula Pwind，Ppv，PpsRepresenting the investment in kW for each of the three energy sources.
In the calculation process, two constraint conditions, namely the power rejection rate and the power supply guarantee rate, are respectively set to be 18% and 90%, and the optimization results shown in table 3 and fig. 4 are calculated. The installed proportion structure and the corresponding investment calculation result obtained by the two optimization methods are compared in the table 3, and the difference between the two results is visually shown in a form of a histogram in the fig. 4. By taking the water and electricity installation as a reference, the proportion result of the photovoltaic, wind power, pumped storage and final installation of the water and electricity obtained by the algorithm is 4.6: 1.4: 1.7: 1.
TABLE 3
Figure BDA0003128855860000133
The results show that the total investment of the base obtained by the algorithm is smaller, the project economic benefit is higher, and the installed capacity ratio of various energy sources is more scientific and finer.
The capacity allocation of the multi-energy complementary clean energy base is analyzed by adopting the algorithm, the optimization variable is the installed capacity of three energy sources of photovoltaic energy, wind power energy and pumped storage energy, the objective function is the minimum initial investment of the base, and the constraint condition is two indexes of the power curtailment rate and the power supply guarantee rate.
Setting the power abandon rate constraint threshold between 13% and 20%, changing by taking 0.5% as a step length, setting the power supply guarantee rate constraint threshold between 90% and 94%, changing by taking 0.5% as a step length, and repeatedly performing optimization tests on the optimization algorithm model by adopting the same optimization calculation parameters in the change interval to obtain the most economical initial investment under different constraint conditions. When the power abandonment rate is maximum and the guarantee rate is minimum, the obtained initial investment amount is minimum, the minimum investment amount is used as a reference value, the ratio of the optimized investment amount to the reference value under other constraints is calculated, the initial investment amount can be gradually increased along with the gradual increase of the guarantee rate or the gradual decrease of the power abandonment rate, and the qualitative conclusion is consistent with the actual situation.
And further comparing the influence of the two constraint conditions on the initial investment optimization result through quantitative analysis. The results of the calculation of the ratio of the optimum investment amount to the reference value obtained under different constraints are shown in table 4. And (4) analyzing the investment change condition that the power abandoning rate is gradually reduced and the guarantee rate is gradually increased from the constraint condition (the power abandoning rate is 19.5 percent and the guarantee rate is 90 percent) corresponding to the minimum initial investment reference value. As can be seen from the data in the table, when the guarantee rate is fixed at 90%, the power abandon rate is reduced by 4 times until the level of 18%, the total investment of the base is only improved by about 1% relative to the reference value, and the increase is not obvious; and when the fixed electricity abandonment rate and the guarantee rate are slightly increased, the investment is obviously increased.
Obviously, when the power abandonment rate is small or the guarantee rate is large, the initial investment of the base construction is relatively large, and when the power abandonment rate is large or the guarantee rate is small, the initial investment of the base construction is relatively small. For more accurate comparative analysis of the impact of the two constraints on the investment, the following two cases of larger and smaller investment are discussed respectively:
TABLE 4
Figure BDA0003128855860000141
For the case of large investment, the power abandonment rate can be fixed to be the minimum value, the guarantee rate is increased (the step length is 0.5%), and a change curve of the optimal initial investment along with the increase of the guarantee rate is drawn, as shown by a short line in fig. 5; it is also possible to fix the guaranteed rate to the maximum value, decrease the power rejection rate (step size is-0.5%), and plot a curve of the change of the optimum initial investment with the decrease of the power rejection rate, as shown by the long line in fig. 5. The results of comparing the slope trends of the two lines show that the optimal initial investment is more sensitive to the guarantee rate.
Similarly, for the case of small investment, the power abandon rate can be fixed to be the maximum value, the guarantee rate is increased (the step length is 0.5%), and a change curve of the optimal initial investment along with the increase of the guarantee rate is drawn, as shown by the short line in fig. 6; it is also possible to fix the guaranteed rate to the minimum value, decrease the power rejection rate (step size is-0.5%), and plot a curve of the change of the optimum initial investment with the decrease of the power rejection rate, as shown by the long line in fig. 6. The results of comparing the slope trends of the two lines also show that the optimal initial investment is more sensitive to the guarantee rate.
The cumulative bar chart of fig. 7 shows the relationship between the percentage of energy installed and the power rejection for 3 types of energy installed configurations corresponding to the most economical installed configuration under the condition that the guarantee rate is fixed at 90% and the power rejection is changed. When the power curtailment is reduced from 20% to 13%, in the optimal scheme, the occupation ratio of the photovoltaic installation machine is in a decreasing trend, the occupation ratios of the wind power and the pumped storage installation machine are in an increasing trend, and the occupation ratios of the wind power and the pumped storage installation machine gradually tend to be balanced. When the electricity abandoning rate is fixed and the guarantee rate changes, the proportion of the 3 types of energy installation ratios has no obvious change rule. From another perspective, the qualitative explanation shows that the power supply guarantee rate is higher than the power abandon rate, the influence weight on the optimal initial investment is larger, and the great decisive effect is played.
The qualitative and quantitative analysis results show that the influence of the relative power abandonment rate of the guarantee rate on the optimal initial investment is more obvious, and the optimal initial investment is more sensitive to the change of the constraint condition of the guarantee rate. Based on the above considerations and analysis of results, the present study determined that constraints with a power curtailment of 18% and a guarantee of 90% were chosen for optimal calculations for the initial investment.
Fig. 8 is a block diagram illustrating an apparatus for optimizing clean energy complementary base installed ratio according to an exemplary embodiment.
As shown in fig. 8, according to a second aspect of the embodiments of the present disclosure, there is provided a device for optimizing installed ratio of a complementary base of clean energy including a photovoltaic power station, a wind power station, a hydroelectric power station and a pumped storage power station, the device including:
an obtaining module 81, configured to obtain a design load of the clean energy complementary base, output typical sequence data of various energy sources, and an optimization constraint condition, where the constraint condition includes a power abandon rate and a power supply guarantee rate;
the determining module 82 is configured to determine an objective function structure corresponding to the clean energy complementary base, where the objective function structure takes the total investment of the clean energy complementary base as an objective function, and takes the installed photovoltaic capacity, the installed wind power capacity, and the pumped storage as optimization variables;
the generating module 83 is configured to randomly generate a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources under the optimization constraint condition, where each configuration scheme solution includes a vector to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity, and the pumped storage installed capacity;
and the selecting module 84 is used for performing iterative optimization by adopting a herd optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
According to a third aspect of the embodiments of the present disclosure, there is provided a device for optimizing installed ratio of a complementary base of clean energy including a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, the device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate;
determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
under the optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and performing iterative optimization by adopting a flock optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
It is further understood that the use of "a plurality" in this disclosure means two or more, as other terms are analogous. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be further understood that the terms "first," "second," and the like are used to describe various information and that such information should not be limited by these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the terms "first," "second," and the like are fully interchangeable. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It is further to be understood that while operations are depicted in the drawings in a particular order, this is not to be understood as requiring that such operations be performed in the particular order shown or in serial order, or that all illustrated operations be performed, to achieve desirable results. In certain environments, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (10)

1. A method for optimizing installed ratio of a clean energy complementary base is characterized in that the clean energy complementary base comprises a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, and the method comprises the following steps:
acquiring design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate;
determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
under the optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and performing iterative optimization by adopting a flock optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
2. The method of claim 1, wherein the objective function structure comprises:
Fi(t)＝PUwind×Pwind(t)+PUpv×Ppv(t)+PUps×Pps(t)
fi (t) represents the total investment of the installation scheme of the clean energy complementary base at the moment t, and PUwind，PUpv，PUpsRespectively representing the investment amount of a unit kW of a wind power station, a photovoltaic power station and a pumped storage power station;
the vector X to be optimizedi(t) is expressed as:
Xi(t)＝(Pwind(t)，Ppv(t)，Pps(t))
wherein, Pwind(t)，Ppv(t)，PpsAnd (t) respectively representing the wind power installed capacity, the photovoltaic installed capacity and the pumped storage installed capacity at the moment t.
3. The method of claim 1, wherein iteratively optimizing using a herd optimization algorithm to select an optimal solution from the set of configuration solutions to minimize the total investment in the complementary base of clean energy, comprises:
determining whether the iterative optimization process falls into local optimum by checking a supervisory operator;
wherein, the discriminant formula of the inspection and supervision operator is expressed as:
Figure FDA0003128855850000021
Figure FDA0003128855850000022
indicating the new position of the ith solution affected by the optimal solution at time t,
Figure FDA0003128855850000023
representing a new position of the ith solution influenced by the optimal solution at the time of t-1, wherein theta represents a preset threshold value;
when the local optimum is determined, executing a jump-out local optimum mechanism, and resetting a part of solution sets in the solution set of the configuration scheme, wherein the solution set needing to be reset is determined by adopting the following formula:
rand(0，1)＜p
here, rand (0,1) represents a random number, and p represents a set probability threshold.
4. The method of claim 3, further comprising:
when the situation that the solution is not trapped into the local optimum is determined, adopting a global optimizing operator and a local optimizing operator to calculate corresponding calling vectors, and randomly dispersing all solutions;
and (4) eliminating and resetting the solution with the fitness function value inferior to the average fitness by adopting a superior-inferior operator until the solution set is converged.
5. The method of claim 4, wherein the global optimization operator is calculated using the following formula:
Figure FDA0003128855850000024
wherein,
Figure FDA0003128855850000025
represents the new position of the ith solution influenced by the optimal solution moving track at the moment t,
Figure FDA0003128855850000026
represents the position of the optimal solution, and each solution except the optimal solution is gathered close to the position, deltaiRepresenting the summons vector between the ith other solution and the optimal solution, c1And c2Is a random coefficient, and is a random coefficient,
Figure FDA0003128855850000027
wherein r is1，r2Is a random number between (0,1), T represents the total number of iterations, c1Is a random number between (0, 2) and represents the calling force of the optimal solution, when c1When the value is more than 1, the influence of the optimal solution is enhanced, otherwise, the influence is weakened; c. C2As dynamic random numbers, c increases with simulation time2Is linearly decreased from 1 to 0, and alpha represents c2The initial range of the coefficients limits the range of influence of the optimal solution.
6. The method of claim 4, wherein the local optimization operator is calculated using the following formula:
Figure FDA0003128855850000031
wherein,
Figure FDA0003128855850000032
denotes the position, x, of the ith solution after self-movement at time ti，d(t) represents the original position of the ith solution at time t, and R and P represent [ -1, 1]Beta represents a modulation coefficient randomly explored, and the empirical value range is (0, 5) and deltaiRepresenting the summons vector between the ith other solution and the optimal solution, R εiRepresenting the movement trajectory of the ith solution.
7. The method of claim 6, wherein the update iteration formula of each solution shift position is expressed as:
Figure FDA0003128855850000033
wherein x isi，d(t +1) represents the position of the ith solution at time t +1,
Figure FDA0003128855850000034
indicating the position of the ith solution after self-movement at time t,
Figure FDA0003128855850000035
represents the new position of the ith solution at the time t after being influenced by the optimal solution moving track, b represents a coefficient linearly reduced from 1 to 0 according to the iteration number, and r3Is [0, 1 ]]A random number is added to the random number,
Figure FDA0003128855850000036
the weight assignment showing that the ith solution moves itself at time t is affected in two ways.
8. A device for optimizing installed ratio of a clean energy complementary base is characterized in that the clean energy complementary base comprises a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, and the device comprises:
the acquisition module is used for acquiring the design load of the clean energy complementary base, the output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandon rate and power supply guarantee rate;
the determining module is used for determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
the generating module is used for randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources under the optimization constraint condition, wherein each configuration scheme solution comprises a vector to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and the selection module is used for carrying out iterative optimization by adopting a flock optimization algorithm and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
9. A device for optimizing installed ratio of a clean energy complementary base is characterized in that the clean energy complementary base comprises a photovoltaic power station, a wind power station, a hydropower station and a pumped storage power station, and the device comprises:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring design load of the clean energy complementary base, output typical sequence data of various energy sources and optimization constraint conditions, wherein the constraint conditions comprise power abandonment rate and power supply guarantee rate;
determining an objective function structure corresponding to the clean energy complementary base, wherein the objective function structure takes the total investment of the clean energy complementary base as an objective function and takes the installed photovoltaic capacity, the installed wind power capacity and the pumped storage as optimization variables;
under the optimization constraint condition, randomly generating a configuration scheme solution set of initial optimization variables according to the design load and the output typical sequence data of various energy sources, wherein each configuration scheme solution comprises vectors to be optimized corresponding to the photovoltaic installed capacity, the wind power installed capacity and the pumped storage installed capacity;
and performing iterative optimization by adopting a flock optimization algorithm, and selecting a final optimal solution from the configuration scheme set so as to minimize the total investment of the clean energy complementary base.
10. A computer-readable storage medium having stored thereon computer instructions, which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 7.
CN202110696936.2A 2021-06-23 2021-06-23 Method and device for optimizing installed ratio of clean energy complementary base Active CN113394807B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110696936.2A CN113394807B (en) 2021-06-23 2021-06-23 Method and device for optimizing installed ratio of clean energy complementary base

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110696936.2A CN113394807B (en) 2021-06-23 2021-06-23 Method and device for optimizing installed ratio of clean energy complementary base

Publications (2)

Publication Number Publication Date
CN113394807A true CN113394807A (en) 2021-09-14
CN113394807B CN113394807B (en) 2023-03-31

Family

ID=77623711

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110696936.2A Active CN113394807B (en) 2021-06-23 2021-06-23 Method and device for optimizing installed ratio of clean energy complementary base

Country Status (1)

Country Link
CN (1) CN113394807B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118504788A (en) * 2024-07-17 2024-08-16 中冶京诚数字科技(北京)有限公司 Determination method, device, equipment and medium for pellet ingredients

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160064934A1 (en) * 2013-03-27 2016-03-03 Electric Power Research Institute Of State Grid Zhejiang Electric Power Company Optimization method for independent micro-grid system
CN108805354A (en) * 2018-06-08 2018-11-13 国网山东省电力公司枣庄供电公司 A kind of power distribution network photovoltaic installation method and system for planning based on honourable time series
CN110797906A (en) * 2018-08-02 2020-02-14 电力规划总院有限公司 Method and device for determining maximum installed capacity of wind power base and storage medium
CN111585305A (en) * 2020-06-12 2020-08-25 国网天津市电力公司 Method suitable for multi-energy complementary linkage economic evaluation
CN112085276A (en) * 2020-09-09 2020-12-15 天津大学 High-permeability wind-solar power generation-containing power supply system capacity matching optimization method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160064934A1 (en) * 2013-03-27 2016-03-03 Electric Power Research Institute Of State Grid Zhejiang Electric Power Company Optimization method for independent micro-grid system
CN108805354A (en) * 2018-06-08 2018-11-13 国网山东省电力公司枣庄供电公司 A kind of power distribution network photovoltaic installation method and system for planning based on honourable time series
CN110797906A (en) * 2018-08-02 2020-02-14 电力规划总院有限公司 Method and device for determining maximum installed capacity of wind power base and storage medium
CN111585305A (en) * 2020-06-12 2020-08-25 国网天津市电力公司 Method suitable for multi-energy complementary linkage economic evaluation
CN112085276A (en) * 2020-09-09 2020-12-15 天津大学 High-permeability wind-solar power generation-containing power supply system capacity matching optimization method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
张金良 等: "考虑可再生能源电力消纳的跨区域电源规划研究", 《山东电力技术》 *
汪赞斌: "抽蓄储能-风-光微电网建模与优化控制研究", 《中国优秀硕士学位论文全文数据库》 *
王园媛 等: "基于蚁群算法的多类型分布式电源优化配置", 《水电能源科学》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118504788A (en) * 2024-07-17 2024-08-16 中冶京诚数字科技(北京)有限公司 Determination method, device, equipment and medium for pellet ingredients

Also Published As

Publication number Publication date
CN113394807B (en) 2023-03-31

Similar Documents

Publication Publication Date Title
Meng et al. Multi-objective hydropower station operation using an improved cuckoo search algorithm
Azizivahed et al. Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems
CN108805434B (en) Improved NSGA-III-based multi-objective optimization scheduling method for cascade hydropower station
Suresh et al. Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem
Li et al. Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm
Nguyen et al. An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems
CN104009494B (en) A kind of environmental economy power generation dispatching method
Zhu et al. A coordinated optimization framework for long-term complementary operation of a large-scale hydro-photovoltaic hybrid system: Nonlinear modeling, multi-objective optimization and robust decision-making
CN111427750B (en) GPU power consumption estimation method, system and medium of computer platform
Yong An improved artificial fish swarm algorithm for optimal operation of cascade reservoirs
CN110380444B (en) Capacity planning method for distributed wind power orderly access to power grid under multiple scenes based on variable structure Copula
CN106779177A (en) Multiresolution wavelet neutral net electricity demand forecasting method based on particle group optimizing
Zou et al. Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer
CN106408031A (en) Super parameter optimization method of least squares support vector machine
Agrawal et al. A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems
CN111460001A (en) Theoretical line loss rate evaluation method and system for power distribution network
Mohamed et al. MSA for optimal reconfiguration and capacitor allocation in radial/ring distribution networks
CN113394807B (en) Method and device for optimizing installed ratio of clean energy complementary base
Lu et al. Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm
Kamal K et al. Optimal scheduling of cascaded hydrothermal systems using a new improved particle swarm optimization technique
Kyomugisha et al. Performance of various voltage stability indices in a stochastic multiobjective optimal power flow using mayfly algorithm
Amarasinghe et al. Adequacy evaluation of composite power systems using an evolutionary swarm algorithm
CN116388291A (en) Large power grid new energy consumption capability calculation method, system, device and medium
CN108770001A (en) Wireless chargeable sensor network optimization method based on close female algorithm
Yasin et al. Optimal least squares support vector machines parameter selection in predicting the output of distributed generation

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