CN114744624A - Planning optimization method for biomass-solar distributed power supply - Google Patents

Planning optimization method for biomass-solar distributed power supply Download PDF

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Publication number
CN114744624A
CN114744624A CN202210653107.0A CN202210653107A CN114744624A CN 114744624 A CN114744624 A CN 114744624A CN 202210653107 A CN202210653107 A CN 202210653107A CN 114744624 A CN114744624 A CN 114744624A
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power generation
biomass
power supply
population
solar
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Inventor
方仍存
迟赫天
李斯吾
汪颖翔
廖爽
邹崇哲
肖宇龙
蒋淑兰
涂鑫
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China University of Geosciences
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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China University of Geosciences
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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
    • H02J2300/40—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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A planning optimization method for a biomass-solar distributed power supply comprises the following steps: constructing a power generation cost model and a greenhouse gas emission model; constructing a constraint model of total power generation of the biomass-solar distributed power supply; introducing a power generation cost model and a greenhouse gas emission model into an NSGA-II algorithm frame as a target function, and introducing a biomass-solar distributed power supply total power generation amount constraint model into the NSGA-II algorithm frame as a linear constraint condition; setting initial population parameters; generating a new population according to the initial population parameters and the total power generation amount constraint of the biomass-solar distributed power supply; and optimizing the planning scheme according to the new population. The NSGA-II algorithm adopted by the invention can be used for optimizing a plurality of targets, and non-dominated rapid sequencing and crowding degree operators are introduced, so that the calculation complexity is lower, the obtained solution is closer to the global optimal solution, and the problem that the solution is lost in the traditional power supply planning is solved.

Description

Planning optimization method for biomass-solar distributed power supply
Technical Field
The invention relates to the technical field of distributed power supply planning optimization, in particular to a biomass-solar distributed power supply planning optimization method.
Background
In recent years, distributed power supplies in various places in China develop rapidly. The distributed power supply is an exploration and innovation of a traditional UPS power supply technology, the problems of insufficient power supply capacity, low power supply utilization rate, difficult transformation and the like in a traditional mode building can be thoroughly solved, and the construction cost and the operation cost of a data center are reduced to the greatest extent.
There is currently a biomass-solar distributed power supply that has two forms of power generation, one being a form of biomass power generation and the other being a form of solar power generation. Planning and optimizing the biomass-solar distributed power supply can cause the problem of considering the problem if a traditional optimization method is adopted, so how to better optimize the biomass-solar distributed power supply is an urgent problem to be solved.
Disclosure of Invention
The invention aims to overcome the defects and problems of the conventional power supply planning in the prior art and provide a biomass-solar distributed power supply planning optimization method.
In order to achieve the above purpose, the technical solution of the invention is as follows: a method for planning and optimizing a biomass-solar distributed power supply, the method comprising the steps of:
s1, constructing a power generation cost model and a greenhouse gas emission model;
s2, constructing a constraint model of total power generation of the biomass-solar distributed power supply;
s3, introducing the power generation cost model and the greenhouse gas emission model into an NSGA-II algorithm framework as a target function, and introducing the biomass-solar distributed power supply total power generation amount constraint model into the NSGA-II algorithm framework as a linear constraint condition;
s4, setting initial population parameters;
s5, generating a new population according to the initial population parameters and the total power generation amount constraint of the biomass-solar distributed power supply;
and S6, optimizing a planning scheme according to the new population.
In step S1, the power generation cost model is:
Figure 257737DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 907024DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 755900DEST_PATH_IMAGE003
is as follows
Figure 629178DEST_PATH_IMAGE002
The power generation quantity in the form of electricity generation is similar,
Figure 681448DEST_PATH_IMAGE004
is a first
Figure 400005DEST_PATH_IMAGE002
The unit power generation cost corresponding to the class power generation form,
Figure 601923DEST_PATH_IMAGE005
is the total power generation cost of the biomass-solar distributed power supply.
In step S1, the greenhouse gas emission model is:
Figure 380523DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,
Figure 654509DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 176758DEST_PATH_IMAGE007
is a first
Figure 226DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 949727DEST_PATH_IMAGE008
is as follows
Figure 976589DEST_PATH_IMAGE002
The emission of greenhouse gases like the form of power generation,
Figure 302528DEST_PATH_IMAGE009
the total greenhouse gas emission of the biomass-solar distributed power supply.
In step S2, the biomass-solar distributed power supply total power generation amount constraint model includes a social power generation amount constraint model and a biomass-solar power generation amount constraint model.
In step S2, the social power generation amount constraint model is:
Figure 465656DEST_PATH_IMAGE010
in the formula (I), the compound is shown in the specification,
Figure 602371DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 116529DEST_PATH_IMAGE011
is as follows
Figure 246159DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 998214DEST_PATH_IMAGE012
the power is required for the society.
In step S2, the biomass-solar power constraint model is:
Figure 820677DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 540240DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 676823DEST_PATH_IMAGE014
is as follows
Figure 814543DEST_PATH_IMAGE002
The power generation quantity in the form of electricity generation is similar,
Figure 889465DEST_PATH_IMAGE015
is as follows
Figure 847057DEST_PATH_IMAGE002
The minimum amount of power generation in the form of electricity generation,
Figure 584069DEST_PATH_IMAGE016
is as follows
Figure 327028DEST_PATH_IMAGE002
Maximum power generation in the form of electricity generation.
In step S4, the setting of the initial population parameter includes:
setting the number of the initial population as 200;
setting the iteration number of the initial population to be 500;
setting the optimal individual coefficient of the initial population to be 0.3;
the fitness function bias for the initial population is set to 1 e-10.
Step S5 specifically includes the following steps:
s51, initializing an initial population;
s52, generating population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s53, performing rapid non-dominated sorting and crowding degree sorting on population individuals to obtain a total population;
s54, importing the total group into a competition pool to perform selection operation and obtain a dominant individual;
and S55, inputting the dominant individual into a mating pool to carry out mating and mutation operations and obtain a new population.
Step S6 specifically includes the following steps:
s61, combining the new population with the initial population to obtain a combined population;
s62, initializing a combined population;
s63, generating new population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s64, carrying out rapid non-dominant sorting and crowding degree sorting on the new population individuals to obtain a new total population;
s65, importing the new total group into a competition pool to perform selection operation and obtain a new dominant individual;
s66, inputting the new dominant individual into a mating pool for mating and mutation operation, and obtaining a new population again;
s67, drawing a new population on the Pareto graph;
and S68, selecting individuals according with the preset power generation amount ratio on the Pareto chart.
Compared with the prior art, the invention has the beneficial effects that:
in the biomass-solar distributed power supply planning optimization method, the NSGA-II algorithm can be used for optimizing a plurality of targets, non-dominated rapid sequencing and crowding degree operators are introduced, the calculation complexity is lower, the obtained solution is closer to the global optimal solution, and the problem that the solution is lost in the traditional power supply planning is solved.
Drawings
Fig. 1 is a schematic flow chart of a method for planning and optimizing a biomass-solar distributed power supply according to the present invention.
Fig. 2 is a mathematical optimization structure of a biomass-solar distributed power supply planning optimization method in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following description and embodiments in conjunction with the accompanying drawings.
Referring to fig. 1, a method for optimizing planning of a biomass-solar distributed power supply includes the following steps:
s1, constructing a power generation cost model and a greenhouse gas emission model;
the power generation cost model is as follows:
Figure 960135DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 936181DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 945725DEST_PATH_IMAGE003
is as follows
Figure 792459DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 111313DEST_PATH_IMAGE004
is as follows
Figure 43497DEST_PATH_IMAGE002
The unit power generation cost corresponding to the class power generation form,
Figure 856733DEST_PATH_IMAGE005
the total power generation cost for the biomass-solar distributed power supply;
in the embodiment of the invention, the power generation mode comprises two power generation modes of biomass and solar energy, and the total power generation cost of the biomass-solar distributed power supply can be calculated through the formula;
the greenhouse gas emission model is as follows:
Figure 89131DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,
Figure 811843DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 496902DEST_PATH_IMAGE007
is as follows
Figure 848249DEST_PATH_IMAGE002
The power generation quantity in the form of electricity generation is similar,
Figure 669574DEST_PATH_IMAGE008
is a first
Figure 64653DEST_PATH_IMAGE002
The emission of greenhouse gases like the form of power generation,
Figure 971429DEST_PATH_IMAGE017
total greenhouse gas emissions for biomass-solar distributed power;
in the embodiment of the invention, the power generation mode comprises two power generation modes of biomass and solar energy, and the total emission amount of greenhouse gases can be calculated through the formula;
s2, constructing a constraint model of total power generation of the biomass-solar distributed power supply;
the biomass-solar distributed power supply total power generation constraint model comprises a social power generation constraint model and a biomass-solar power generation constraint model;
in the embodiment of the invention, when the total power generation amount constraint of the biomass-solar distributed power supply is obtained, firstly, a social power generation amount constraint model is obtained, and then, a biomass-solar power amount constraint model is obtained, wherein the social power generation amount constraint model is used for representing the constraint to which the social power generation amount is subjected, and the biomass-solar power amount constraint model is used for representing the constraint to which the biomass-solar power amount is subjected;
the social generating capacity constraint model is as follows:
Figure 126467DEST_PATH_IMAGE018
in the formula (I), the compound is shown in the specification,
Figure 490714DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 73005DEST_PATH_IMAGE011
is as follows
Figure 732657DEST_PATH_IMAGE002
The power generation quantity in the form of electricity generation is similar,
Figure 425806DEST_PATH_IMAGE019
the electricity is demanded for the society;
in the embodiment of the invention, the power generation mode comprises two power generation modes of biomass and solar energy, and the constraint relation between the sum of social power generation and social demand electric quantity can be represented through the formula;
the biomass-solar energy electric quantity constraint model is as follows:
Figure 470991DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 224184DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 839973DEST_PATH_IMAGE014
is as follows
Figure 336813DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 729181DEST_PATH_IMAGE015
is as follows
Figure 653274DEST_PATH_IMAGE002
The minimum amount of power generation in the form of electricity generation,
Figure 287518DEST_PATH_IMAGE016
is a first
Figure 322470DEST_PATH_IMAGE002
Maximum power generation in the form of electricity generation;
in the embodiment of the invention, the power generation form comprises two power generation forms of biomass and solar energy, and the constraint relation between biomass-solar energy electric quantity and the maximum power generation amount and the minimum power generation amount of the power generation form can be represented through the formula;
s3, introducing the power generation cost model and the greenhouse gas emission model into an NSGA-II algorithm frame as a target function, and introducing the biomass-solar distributed power supply total power generation amount constraint model into the NSGA-II algorithm frame as a linear constraint condition;
in the embodiment of the invention, a power generation cost model, a greenhouse gas emission model, a social power generation amount constraint model and a biomass-solar power amount constraint model are sequentially led into an NSGA-II algorithm framework, so that the advantages of setting an optimization target and an initial condition for the algorithm, obtaining the best result (scheme) in a demand range by an instruction program, converting an actual problem into a mathematical model and writing the mathematical model into a code which can be executed by a computer are achieved;
s4, setting initial population parameters;
the setting of the initial population parameters comprises:
setting the number of the initial population as 200;
setting the iteration times of the initial population to be 500;
setting the optimal individual coefficient of the initial population to be 0.3;
setting the fitness function deviation of the initial population as 1 e-10;
in the embodiment of the invention, when the initial population parameters are set, the number of the initial population is set to be 200, the iteration frequency of the initial population is set to be 500, the optimal individual coefficient of the initial population is set to be 0.3, and the fitness function deviation of the initial population is set to be 1e-10, so that the larger the population number and the iteration frequency is, the better the obtained optimal solution is, but when the population number is too large, the program can run too slowly; on the contrary, if the number of the population and the number of iterations are smaller, the program running speed is faster but the optimal solution can be easily missed, so the number of the selected population is 200 and the number of iterations is 500 by comprehensive consideration;
selecting an optimal individual coefficient 0.3 which is suitable for the user according to the selected initial population number and the iteration times, wherein too large the optimal individual coefficient setting can cause too many optimal individuals to be distributed on the final pareto chart to influence the user to select the appropriate individual, and conversely, too small the optimal population coefficient can cause too few individuals to be distributed on the final pareto chart to easily miss the optimal individual or the individual which is suitable for the user;
if the deviation of the fitness function is set too large, the final result cannot meet the constraint condition, if the deviation of the fitness function is set too small, the algorithm program runs too slowly, and the deviation is set to be 1e-10 in a comprehensive consideration mode;
s5, generating a new population according to the initial population parameters and the total power generation amount constraint of the biomass-solar distributed power supply; the method specifically comprises the following steps:
s51, initializing an initial population;
s52, generating population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s53, carrying out rapid non-dominated sorting and crowding degree sorting on the population individuals to obtain a total population;
s54, importing the total group into a competition pool to perform selection operation and obtain a dominant individual;
s55, inputting the dominant individual into a mating pool to carry out mating and mutation operations and obtain a new population;
in the embodiment of the invention, when a new species group is generated according to the initial population parameter and the total power generation amount constraint of the biomass-solar distributed power supply, an initial population is initialized, the initial population is set in the step S4, then population individuals meeting the total power generation amount constraint of the biomass-solar distributed power supply can be generated, rapid non-dominated sorting and crowding degree sorting are carried out on the population individuals to obtain a total population, the total population is led into a competition pool to carry out selection operation to obtain advantageous individuals, and then the advantageous individuals are input into the mating pool to carry out mating and variation operation to obtain the new population;
in the embodiment of the invention, the advantages of generating the new population according to the initial population parameters and the total power generation constraint of the biomass-solar distributed power supply are the same as the advantages of the initial population parameters and the total power generation constraint of the biomass-solar distributed power supply, and good parameter setting not only can enable the result accuracy to be higher, but also can enable the program operation efficiency to be higher;
s6, optimizing a planning scheme according to the new population; the method specifically comprises the following steps:
s61, combining the new population with the initial population to obtain a combined population;
s62, initializing a combined population;
s63, generating new population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s64, carrying out rapid non-dominant sorting and crowding degree sorting on the new population individuals to obtain a new total population;
s65, importing the new total group into a competition pool to perform selection operation and obtain a new dominant individual;
s66, inputting the new dominant individual into a mating pool for mating and mutation operation and obtaining a new population again;
s67, drawing a new population on the Pareto graph;
s68, selecting individuals according with a preset power generation ratio on a Pareto chart;
in the embodiment of the invention, when a planning scheme is optimized according to the new population, the new population is combined with the initial population to obtain a combined population, and then the combined population is initialized, wherein the initialization method is the same as the initialization method in the step S5, so that new population individuals conforming to the total power generation amount constraint of the biomass-solar distributed power supply can be generated; then, fast non-dominated sorting and congestion degree sorting are carried out on the individuals of the new population to obtain a new total group, the new total group is led into a competition pool to carry out selection operation, and new dominant individuals are obtained; then inputting the new dominant individual into a mating pool for mating and mutation operation to obtain a new population; then drawing the new population on a Pareto chart, and selecting individuals according with a preset power generation amount ratio on the Pareto chart;
in the embodiment of the invention, the new population is combined with the initial population to produce the next generation new population according to the benefit of the new population optimization planning scheme, so that the advantage individuals in the parent population are retained, the parent population and the offspring population are subjected to non-dominated sorting and crowdedness sorting together, the waste of 'excellent' genes in the parent is avoided, and the finally obtained population is closer to the global optimal solution.
The following embodiment verifies the planning optimization method for the biomass-solar distributed power supply provided by the invention.
In the embodiment of the invention, taking a biomass-solar distributed power supply of a power plant in a central area as an example, the power plant generates about 70 billion kilowatt hours per year according to the rule, and the biomass power generation amount is about 15 to 23 billion kilowatt hours and the solar power generation amount is about 43 to 61 billion kilowatt hours are calculated according to the loading amount and the average annual utilization time of the two power supplies.
According to financial statements of a power plant and detection and calculation of pollutant emission, the biomass power generation cost is about 6.68 multiplied by 107 yuan/hundred million watt-hour, the solar power generation cost is about 7.00 multiplied by 107 yuan/hundred million kilowatt-hour, greenhouse gas emitted by the biomass power generation is about 2.4 multiplied by 107 kg/hundred million kilowatt-hour, and greenhouse gas emitted by the solar power generation is about 1.7 multiplied by 106 kg/hundred million kilowatt-hour.
In the embodiment of the present invention, the first and second substrates,
Figure 76668DEST_PATH_IMAGE020
，
Figure 171663DEST_PATH_IMAGE021
，
Figure 496466DEST_PATH_IMAGE022
，
Figure 335109DEST_PATH_IMAGE023
，
Figure 710857DEST_PATH_IMAGE024
，
Figure 976754DEST_PATH_IMAGE025
，
Figure 320010DEST_PATH_IMAGE026
，
Figure 696765DEST_PATH_IMAGE027
，
Figure 159976DEST_PATH_IMAGE028
and establishing a model according to the steps S1 and S2 and importing the model into the NSGA-II algorithm framework for operation.
The following table is a biomass-solar distributed power supply generated energy distribution scheme
Figure 596774DEST_PATH_IMAGE029
。
From the above, the NSGA-ii algorithm can be applied in the distributed power generation planning, the scheme has the lowest generation cost but too high greenhouse gas emission, the scheme ii has the lowest greenhouse gas emission but too high generation cost, and the scheme iii and the scheme iv are relatively compromised.
Compared with the traditional genetic algorithm, the NSGA-II algorithm can be used for optimizing multiple targets, the non-dominated rapid sorting and crowding degree operators are introduced, the calculation complexity is lower, the obtained solution is closer to the global optimal solution, and the problem that the solution is wrong in the traditional power supply planning is solved.
In short, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for planning and optimizing a biomass-solar distributed power supply, the method comprising the steps of:
s1, constructing a power generation cost model and a greenhouse gas emission model;
s2, constructing a constraint model of total power generation of the biomass-solar distributed power supply;
s3, introducing the power generation cost model and the greenhouse gas emission model into an NSGA-II algorithm framework as a target function, and introducing the biomass-solar distributed power supply total power generation amount constraint model into the NSGA-II algorithm framework as a linear constraint condition;
s4, setting initial population parameters;
s5, generating a new population according to the initial population parameters and the total power generation amount constraint of the biomass-solar distributed power supply;
and S6, optimizing a planning scheme according to the new population.
2. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: in step S1, the power generation cost model is:
Figure DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 521653DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure DEST_PATH_IMAGE003
is as follows
Figure 717142DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 369490DEST_PATH_IMAGE004
is as follows
Figure 876695DEST_PATH_IMAGE002
The unit power generation cost corresponding to the class power generation form,
Figure DEST_PATH_IMAGE005
is the total power generation cost of the biomass-solar distributed power supply.
3. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: in step S1, the greenhouse gas emission model is:
Figure 823922DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,
Figure 936235DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure DEST_PATH_IMAGE007
is as follows
Figure 255089DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 187273DEST_PATH_IMAGE008
is as follows
Figure 734929DEST_PATH_IMAGE002
The emission of greenhouse gases like the form of power generation,
Figure DEST_PATH_IMAGE009
the total greenhouse gas emission of the biomass-solar distributed power supply.
4. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: in step S2, the biomass-solar distributed power supply total power generation amount constraint model includes a social power generation amount constraint model and a biomass-solar power generation amount constraint model.
5. The biomass-solar distributed power supply planning optimization method according to claim 4, wherein: in step S2, the social power generation amount constraint model is:
Figure 967328DEST_PATH_IMAGE010
in the formula (I), the compound is shown in the specification,
Figure 958548DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure DEST_PATH_IMAGE011
is as follows
Figure 581291DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure 932638DEST_PATH_IMAGE012
the electricity quantity is required for the society.
6. The biomass-solar distributed power supply planning optimization method according to claim 4, wherein: in step S2, the biomass-solar power constraint model is:
Figure DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 268810DEST_PATH_IMAGE002
is a serial number of a power generation form,
Figure 680200DEST_PATH_IMAGE014
is as follows
Figure 321397DEST_PATH_IMAGE002
The power generation quantity in the form of similar power generation,
Figure DEST_PATH_IMAGE015
is as follows
Figure 476434DEST_PATH_IMAGE002
The minimum amount of power generation in the form of electricity generation,
Figure 165648DEST_PATH_IMAGE016
is as follows
Figure 747939DEST_PATH_IMAGE002
Maximum power generation in the form of electricity generation.
7. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: in step S4, the setting of the initial population parameter includes:
setting the number of the initial population as 200;
setting the iteration times of the initial population to be 500;
setting the optimal individual coefficient of the initial population to be 0.3;
the fitness function bias for the initial population is set to 1 e-10.
8. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: step S5 specifically includes the following steps:
s51, initializing an initial population;
s52, generating population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s53, performing rapid non-dominated sorting and crowding degree sorting on population individuals to obtain a total population;
s54, importing the total group into a competition pool to perform selection operation and obtain a dominant individual;
and S55, inputting the dominant individual into a mating pool to carry out mating and mutation operations and obtain a new population.
9. The biomass-solar distributed power supply planning optimization method of claim 1, wherein: step S6 specifically includes the following steps:
s61, combining the new population with the initial population to obtain a combined population;
s62, initializing a combined population;
s63, generating new population individuals according with the constraint of the total power generation amount of the biomass-solar distributed power supply;
s64, carrying out rapid non-dominant sorting and crowding degree sorting on the new population individuals to obtain a new total population;
s65, importing the new total group into a competition pool to perform selection operation and obtain a new dominant individual;
s66, inputting the new dominant individual into a mating pool for mating and mutation operation and obtaining a new population again;
s67, drawing a new population on the Pareto graph;
and S68, selecting individuals according with the preset power generation amount ratio on the Pareto chart.
CN202210653107.0A 2022-06-10 2022-06-10 Planning optimization method for biomass-solar distributed power supply Pending CN114744624A (en)

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