CN112290592B - Capacity optimization planning method and system for wind-solar-storage combined power generation system and readable storage medium - Google Patents

Capacity optimization planning method and system for wind-solar-storage combined power generation system and readable storage medium Download PDF

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CN112290592B
CN112290592B CN202011168703.7A CN202011168703A CN112290592B CN 112290592 B CN112290592 B CN 112290592B CN 202011168703 A CN202011168703 A CN 202011168703A CN 112290592 B CN112290592 B CN 112290592B
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wind
capacity
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power generation
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CN112290592A (en
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伍也凡
肖振锋
刘浩田
陈仲伟
王逸超
李沛哲
冷阳
李达伟
谢欣涛
侯益灵
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan 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
    • 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
    • H02J15/00—Systems for storing electric 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of 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
    • 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
    • H02J2300/40—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
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/16—Mechanical energy storage, e.g. flywheels or pressurised fluids
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses a capacity optimization planning method, a capacity optimization planning system and a readable storage medium of a wind-solar-storage combined power generation system, wherein the method comprises the following steps: s1: establishing a gravity energy storage model depending on a mountain body; s2: constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage based on the gravity energy storage model, wherein the minimum total cost of the wind-solar-energy storage combined power generation system is taken as a target function; s3: and solving the capacity optimization planning model in the step S2 to obtain an optimal capacity planning result in a gravitational potential energy form, wherein the optimal capacity planning result is the capacity of the wind power plant, the capacity of the photovoltaic power plant and the capacity of gravity energy storage. The capacity optimization planning method for the wind-solar-energy storage combined power generation system is used for constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage, provides a brand new means and angle for thinking how to carry out capacity planning on the wind-solar-energy storage combined power generation system, and is more matched with the environment mostly in mountainous regions in northwest.

Description

Capacity optimization planning method and system for wind-solar-storage combined power generation system and readable storage medium
Technical Field
The invention belongs to the technical field of energy Internet, and particularly relates to a capacity optimization planning method and system of a wind-solar-storage combined power generation system and a readable storage medium.
Background
The renewable energy sources of wind energy and solar energy in China are rich and mostly concentrated in the northwest region, the installed capacity of the renewable energy sources in the northwest region is continuously increased, but the absorption is difficult, the peak regulation capability of a power grid is insufficient, and the phenomena of wind abandoning and light abandoning in the northwest region occur at times. The wind-solar combined power generation system based on renewable energy is configured with energy storage with certain capacity, which is one of important means for meeting the large-scale access of renewable energy, not only can effectively improve the wind-solar power generation absorption capacity, but also can improve the reliability and the economy of the power generation system. The capacity of the wind-solar-energy-storage combined power generation system has great influence on the operation reliability and economy, and when the capacity of the system is configured excessively, the load requirement in a certain time period may not be met; if the configuration is too large, the reliability can be improved, but the initial construction cost is greatly increased. Therefore, the capacity of the wind power plant, the capacity of the photovoltaic power station and the energy storage capacity are scientifically and reasonably configured, and the method is one of the key technical problems of the planning of the wind-solar-energy-storage combined power generation system.
At present, the research on capacity optimization planning of wind-solar-energy storage combined power generation systems at home and abroad mostly focuses on energy storage forms and consideration of storage battery energy storage combined power generation systems, and northwest areas are mostly mountainous areas with higher altitude, so that the application of the combined power generation systems in the northwest areas is limited due to factors such as construction scale, site environment and the like.
Disclosure of Invention
The invention aims to provide a capacity optimization planning method of a wind-solar-energy-storage combined power generation system, which is used for constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power plant and gravity energy storage for a research object by considering the gravitational potential energy, and further solving an optimal capacity planning result in a form of considering the gravitational potential energy, wherein the planning method is particularly suitable for northwest areas with more mountainous areas.
The invention provides a capacity optimization planning method of a wind-solar-energy-storage combined power generation system, which comprises the following steps:
s1: establishing a gravity energy storage model depending on a mountain body;
s2: constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage based on the gravity energy storage model, wherein the minimum total cost of the wind-solar-energy storage combined power generation system is taken as a target function;
s3: and solving the capacity optimization planning model in the step S2 to obtain an optimal capacity planning result in a gravitational potential energy form.
Further preferably, the method further comprises:
a: establishing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and storage battery energy storage and/or establishing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and compressed air energy storage;
b: solving a capacity optimization planning model to obtain an optimal capacity planning result under the storage battery energy storage form and/or an optimal capacity planning result under the compressed air energy storage form;
c: and evaluating and comparing the capacity planning models considering different energy storage forms by utilizing a rank and ratio evaluation method of entropy weight method weighting to obtain the quality results under different energy storage forms.
Preferably, if optimal capacity planning results in a gravitational potential energy form, a storage battery energy storage form and a compressed air energy storage form are obtained, the execution process of step C is as follows:
calculating each evaluation index value in different energy storage forms based on the optimal capacity planning result in each type of energy storage form, and determining a weight corresponding to each evaluation index by using an entropy weight method, wherein the evaluation index at least comprises any two or three of wind-light complementary characteristics, power supply loss rate and contribution rate of a wind-light storage combined power generation system;
then, based on the weight corresponding to each index, calculating the weighted rank sum ratio corresponding to different energy storage forms by using a rank sum ratio method;
finally, ranking the advantages and disadvantages of different energy storage forms based on the weighted rank sum ratio value to obtain an evaluation result;
the larger the weighted rank sum ratio is, the more excellent the wind-solar-energy-storage combined power generation system corresponding to the energy storage form is.
Further preferably, the calculation formulas of the wind-solar complementary characteristic, the power supply loss rate and the contribution rate of the wind-solar-energy storage combined power generation system are as follows:
Figure GDA0003250774320000021
Figure GDA0003250774320000022
Figure GDA0003250774320000023
Figure GDA0003250774320000024
in the formula, D is the wind-light complementary characteristic, f is the power supply loss rate, R is the contribution rate of the wind-light storage combined power generation system,
Figure GDA0003250774320000025
is the average power of the load, Pwt(t) is the power of the wind power generation at time t, Ppv(t) power of photovoltaic power generation at time t, PL(t) power of the load at time t, PgsAnd (t) is gravity energy storage power at the time t, and E (t) is power provided for a load by a system at the time t.
Further preferably, the energy W stored in the theoretical gravity energy storage in the gravity energy storage model in step S1 satisfies the following condition:
W＝mg·h
in the formula, h is the height of a mountain;
in the gravity energy storage process, the motor consumes electric energy to pull the heavy object from the bottom of the mountain to the top of the mountain, and the power P of the motorm_upComprises the following steps:
Pm_up＝Fm_up·vup＝(mg·sinθ+Fμ)·vup＝(mg·sinθ+μ·mg·cosθ)·vup
in the formula, Fm_upFor motor traction during ascent, vupIs the speed of the weight in the process of uniform ascending, m is the weight amount, g is the gravity acceleration, theta is the angle of the hillside, FμIs the friction between the weight and the rail, mu is the friction coefficient of the rail;
gravity energy storage releases heavy objects from the mountain top track to the mountain bottom and is divided into an acceleration stage and a grid connection stage, the gravitational potential energy is reduced in the grid connection stage, an engine is driven to work, electric energy is generated to supply loads or is transmitted to a power grid, and the power P of a generator in the gliding processg_downComprises the following steps:
Pg_down＝Fg_down·vdownwherein v isdownIs the speed of the weight in the process of sliding down at constant speed Fg_downIs the generator traction force in the gliding process and meets the following requirements:
mg·sinθ＝Fμ+Fm_down+Fg_down
in the formula, Fm_downIs the motor traction during the glide.
Further preferably, the objective function is as follows:
minCtotal＝min(CIN+COM+CBE-CSE-CEP)
wherein, CtotalThe total cost of the system; cINIs the initial cost of the system; cOMFor operating maintenance costs; cBEThe cost of purchasing electricity to the power grid; cSEFor selling electricity revenue to the grid; cEPFor the benefit of environmental protection, satisfy respectively:
Figure GDA0003250774320000031
Figure GDA0003250774320000032
wherein N iswtInstalling quantity for the wind power plant; cwtThe unit price of the fan is; wpvIs the photovoltaic power station capacity; cpvIs the price per unit capacity of the photovoltaic power station; wgsIs the gravity energy storage capacity; cgsIs the price per unit capacity of gravity energy storage; f. ofDRIs a depreciation factor; d is depreciation rate; y is the age;
Figure GDA0003250774320000033
wherein, Δ twt、Δtpv、ΔtgsThe running time of the wind power plant, the photovoltaic power station and the gravity energy storage in one day is respectively;
Figure GDA0003250774320000034
Figure GDA0003250774320000035
are respectively a unitThe operation and maintenance costs of a wind power plant, a photovoltaic power station and gravity energy storage within time;
CBE＝CP(t)·(EBE(t)+Em_down(t))
wherein, CP(t) is the grid electricity price at the moment t; eBE(t) the electric quantity purchased by the system to the power grid at the moment t; em_down(t) is the amount of electricity consumed by the motor during traction during gravity energy storage discharge;
CSE＝CP(t)·ESE(t)+CS(t)·E(t)
wherein, CS(t) the price of electricity sold by the system at the time t; eSE(t) the electric quantity sold to the power grid by the system at the moment t; and E (t) the electric quantity provided by the system to the load at the moment t.
Figure GDA0003250774320000041
Wherein, wwt、wpvThe daily generated energy of wind power and photovoltaic power respectively; n is the number of types of pollutants;
Figure GDA0003250774320000042
the environmental value cost of the i-th pollutants of thermal power generation, wind power generation and photovoltaic power generation respectively.
Further preferably, the constraint conditions of the objective function in the capacity optimization planning model containing the wind power plant, the photovoltaic power plant and the gravity energy storage include:
the lower limit of the capacity of the wind-solar-energy-storage combined power generation system is 0, and the upper limit of the capacity is 10 times of the maximum load in one day;
the gravity energy storage capacity should satisfy: w is not less than 0gs≤mg·hgs_max，hgs_maxThe maximum height of the mountain, m is the weight, and g is the gravity acceleration;
power P exchanged between wind-light-storage combined power generation system and power gridg(t) satisfies: pg_min≤Pg(t)≤Pg_max，Pg_minAnd Pg_maxMinimum power and maximum power allowed to be exchanged between the system and the power grid respectively。
Further preferably, the capacity optimization planning model of the wind-solar-energy storage combined power generation system in the capacity optimization planning model containing the wind power plant, the photovoltaic power station and the gravity energy storage comprises the following steps:
when the grid-connected wind-solar-energy storage combined power generation system runs, when wind power and photovoltaic power generation are sufficient, load requirements are supplied, gravity is used for storing energy until rated capacity is reached, if the capacity is remained, the energy is fed into a power grid, and the method comprises the following steps:
PSE(t)＝Pwt(t)+Ppv(t)-Pgs(t)-PL(t)
wherein, PSE(t) power fed into the grid, Pwt(t) is the power of wind power generation at time t; ppv(t) is the power of photovoltaic power generation at time t; pL(t) power of the load at time t, Pgs(t) is the gravity energy storage power at the moment t;
when wind power and photovoltaic power generation are insufficient, gravity energy storage discharges, if the load requirement is not met, electricity is purchased from a power grid, namely
PBE(t)＝PL(t)-(Pwt(t)+Ppv(t)+Pgs(t))
Wherein, PBE(t) power supplied to the grid.
In a second aspect, the invention provides a planning system comprising a processor and a memory, the memory storing a computer program, the processor calling the computer program to perform the steps of the capacity optimization planning method for the wind, photovoltaic and energy storage combined power generation system.
In a third aspect, the invention provides a readable storage medium storing a computer program for execution by a processor to perform the steps of the capacity optimization planning method for the wind, photovoltaic and energy storage combined power generation system.
Advantageous effects
The capacity optimization planning method provided by the invention considers the gravitational potential energy to construct a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravitational energy storage for a research object, and further solves an optimal capacity planning result in a mode of considering the gravitational potential energy, provides a brand new means and a new angle for thinking how to carry out capacity planning on the wind-solar-energy storage combined power generation system, and particularly, compared with a combined power generation system considering the energy storage of a storage battery, the method provided by the invention is more matched and matched with the environment mostly in mountainous regions in northwest.
Drawings
FIG. 1: the invention relates to a system structure diagram of a grid-connected wind-solar-storage combined power generation system;
FIG. 2: the invention relates to an energy conversion diagram of gravity energy storage;
FIG. 3: the invention relates to a capacity optimization planning and calculating flow chart of a wind-solar-storage combined power generation system;
FIG. 4: the invention evaluates a basic process diagram;
FIG. 5: the embodiment of the invention discloses a one-day wind speed prediction data graph;
FIG. 6: the embodiment of the invention discloses a data graph for predicting the illumination intensity in one day;
FIG. 7: the embodiment of the invention considers the output curve graph after capacity planning in gravity energy storage;
FIG. 8: the embodiment of the invention considers the output curve graph after capacity planning when the storage battery stores energy;
FIG. 9: the embodiment of the invention considers the output curve graph after capacity planning when the compressed air stores energy.
Detailed Description
The capacity optimization planning method of the wind-solar-energy-storage combined power generation system provided by the invention constructs a capacity optimization planning model containing a wind power plant, a photovoltaic power plant and gravity energy storage, and further solves an optimal capacity planning result in a mode of considering gravitational potential energy. Further, in order to understand the influence of different energy storage modes more deeply, the invention also compares the gravitational potential energy with the wind-solar-energy-storage combined power generation system considering the storage battery energy storage and the compressed air energy storage, and evaluates different energy storage modes. The present invention will be further described with reference to the following examples.
The capacity optimization planning method of the wind-solar-energy-storage combined power generation system provided by the embodiment of the invention comprises the following steps:
step 1: considering the energy storage process and the energy release process of gravity energy storage, establishing a gravity energy storage model depending on a mountain body;
step 2: constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage, constructing a capacity optimization planning model containing the wind power plant, the photovoltaic power station and storage battery energy storage, and constructing a capacity optimization planning model containing the wind power plant, the photovoltaic power station and compressed air energy storage based on the gravity energy storage model;
the total cost of the wind-solar-storage combined power generation system is minimum as an objective function;
and step 3: solving the model by using a cat swarm algorithm to obtain optimal capacity planning results respectively corresponding to different energy storage modes such as gravity energy storage, storage battery energy storage, compressed air energy storage and the like;
and 4, step 4: and evaluating and comparing the capacity planning models considering different energy storage forms by utilizing a rank and ratio evaluation method of entropy weight method weighting to obtain the quality results under different energy storage forms.
In the specific implementation process, the gravity energy storage model in the step 1 comprises the following contents: a process of storing energy and a process of releasing energy;
step 1.1: and (4) storing energy. The amount of energy stored and released by gravity energy storage is related to the height, gradient, weight quality and the like of a mountain. When energy is stored, the motor acts to consume electric energy, the weight is pulled to the top of the mountain from the rail at the bottom of the mountain by the pulley block, gravitational potential energy is increased in the process, and the weight is finally stored at the top of the mountain. The motor keeps the weight at a constant speed in the process of pulling the weight from the bottom of the hill to the top of the hill, and the weight is stressed in a balanced manner.
Fm_up＝mg·sinθ+Fμ
Fμ＝μ·mg·cosθ
Wherein, Fm_upIs the motor traction during ascent; fμThe friction force between the weight and the track; m is weight mass; g is the acceleration of gravity; theta is the angle of the hillside; μ is the coefficient of friction of the rail.
Pm_up＝Fm_up·vup＝(mg·sinθ+Fμ)·vup
Wherein, Pm_upIs the motor power during the ramp-up; v. ofupThe speed of the heavy object in the process of uniformly ascending is shown.
Step 1.2: and (4) releasing energy. The energy releasing process is relatively complex relative to the energy storing process, the gravity energy storage releases a heavy object from a mountain top rail to the mountain bottom by using the pulley block, the acceleration stage and the grid connection stage are divided, the gravitational potential energy is reduced in the process, the generator is driven to work, and the generated electric energy is supplied to a load or is transmitted to a power grid.
In the acceleration stage, the gliding speed of the heavy object is gradually accelerated from zero, the output of the generator is unstable in the acceleration process, and grid-connected power generation is not performed to avoid overlarge fluctuation of the power grid. In the grid connection stage, the weight is accelerated to a certain constant speed (for example, set to be 10m/s), the weight keeps the constant speed and slides down at a constant speed, the power generation power of the generator is balanced, and in order to keep the weight sliding down at the constant speed and ensure the safety of the protection device, the motor pulls the weight, and the stress of the weight is balanced.
mg·sinθ＝Fμ+Fm_down+Fg_down
Wherein, Fm_downThe traction force of the motor in the gliding process; fg_downIs the traction force of the generator in the gliding process.
Pg_down＝Fg_down·vdown
Wherein, Pg_downThe power of the generator in the gliding process; v. ofdownThe speed of the weight in the process of sliding down at constant speed.
Therefore, in the process of storing energy, the motor stores gravitational potential energy and consumes electric energy in order to pull a heavy object to the top of a mountain; in the process of releasing energy, gravitational potential energy is reduced to drive the generator to work, electric energy is generated to be supplied to a load or is transmitted to a power grid, and meanwhile, in order to ensure constant speed, the motor consumes certain electric energy.
Given that the slope, mass of the weight, etc. are known, the height of the mountain is the only factor that affects the gravity energy storage capacity to study the optimal capacity for gravity energy storage. Then theoretically the energy stored by gravity energy storage is: w is mg.h, wherein W is the energy stored by gravity energy storage; h is the height of the mountain. In the embodiment, the charge and discharge efficiency of the gravity energy storage is set to be 90% in consideration of friction loss in the energy conversion process.
In the step 2, three models, namely a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage, a capacity optimization planning model containing a wind power plant, a photovoltaic power station and storage battery energy storage and a capacity optimization planning model containing a wind power plant, a photovoltaic power station and compressed air energy storage are established, the model considering gravity energy storage is taken as an example in the embodiment, and the other two models are established in a similar manner.
The capacity optimization planning model mainly comprises the following three parts: objective functions, constraints and planning strategies.
Point 1: the objective function of the capacity optimization planning model of the wind-solar-energy storage combined power generation system is the minimum total cost, and the considered cost comprises the following steps: the initial cost of the system, the operation and maintenance cost, the electricity purchasing cost, the electricity selling income and the environmental protection income. The mathematical expression is:
minCtotal＝min(CIN+COM+CBE-CSE-CEP)
wherein, CtotalThe total cost of the system; cINIs the initial cost of the system; cOMFor operating maintenance costs; cBEThe cost of purchasing electricity to the power grid; cSEFor selling electricity revenue to the grid; cEPIs a benefit for environmental protection.
Converting the initial cost of the system to the daily initial cost, wherein the daily initial cost is as follows:
Figure GDA0003250774320000071
Figure GDA0003250774320000072
wherein N iswtInstalling quantity for the wind power plant; cwtThe unit price of the fan is; wpvIs the photovoltaic power station capacity; cpvIs the price per unit capacity of the photovoltaic power station; wgsIs the gravity energy storage capacity; cgsIs the price per unit capacity of gravity energy storage; f. ofDRIs a depreciation factor; d is depreciation rate; and y is the service life.
The operation and maintenance cost of the system is as follows:
Figure GDA0003250774320000081
wherein, Δ twt、Δtpv、ΔtgsThe running time of the wind power plant, the photovoltaic power station and the gravity energy storage in one day is respectively;
Figure GDA0003250774320000082
Figure GDA0003250774320000083
the operation and maintenance costs of a wind power plant, a photovoltaic power station and gravity energy storage in unit time are respectively.
The electricity purchasing cost of the system to the power grid is as follows:
CBE＝CP(t)·(EBE(t)+Em_down(t))
wherein, CP(t) is the grid electricity price at the moment t; eBE(t) the electric quantity purchased by the system to the power grid at the moment t; em_downAnd (t) is the electric quantity consumed by the traction of the motor in the gravity energy storage and discharge process.
The electricity selling income of the system to the power grid is as follows:
CSE＝CP(t)·ESE(t)+CS(t)·E(t)
wherein, CS(t) the price of electricity sold by the system at the time t; eSE(t) the electric quantity sold to the power grid by the system at the moment t; and E (t) the electric quantity provided by the system to the load at the moment t.
Generally, the pollutants of the traditional thermal power generation system are mainly SO2、NOx、CO2Dust, etc. and wind-force and photovoltaic power generation can reduce the emission of these pollutants effectively, have protected the environment to a certain extent, so the environmental protection income of system does:
Figure GDA0003250774320000084
wherein, wwt、wpvThe daily generated energy of wind power and photovoltaic power respectively; n is the number of types of pollutants;
Figure GDA0003250774320000085
the environmental value cost of the i-th pollutants of thermal power generation, wind power generation and photovoltaic power generation respectively.
And (2) point: the constraint conditions of the capacity optimization planning model considering the gravitational potential energy comprise:
1. the lower limit of the capacity of the wind-solar-energy-storage combined power generation system is 0, the upper limit is 10 times of the maximum load in one day, namely 2380MW in the embodiment, and the following requirements are met:
0≤Nwt·pwt+Wpv+Wgs≤2380
wherein p iswtPower of a single fan, NwtAnd installing quantity for the wind power plant.
2. The capacity of gravity energy storage depends on the height of a mountain body, and the following requirements are met:
0≤Wgs≤mg·hgs_max
wherein h isgs_maxThe maximum height of the mountain.
3. The power exchanged between the wind-light-storage combined power generation system and the power grid needs to meet the following requirements:
Pg_min≤Pg(t)≤Pg_max
wherein, Pg(t) is the exchange power, Pg_minAnd Pg_maxThe minimum power and the maximum power allowed to be exchanged by the system and the power grid respectively are determined according to supply and demand agreements between the system and the power grid.
In addition, if the storage battery energy storage or the compressed air energy storage is considered, the model constraint conditions further comprise: energy storage state of charge constraint:
for preventing the storage battery from storing energy and compressed air from being excessively charged and discharged to reduce the service life of the storage battery, the state of charge is restrained, namely
Figure GDA0003250774320000091
Figure GDA0003250774320000092
Therein, SOCBE(t) is the state of charge of the storage battery at time t;
Figure GDA0003250774320000093
respectively storing the upper limit and the lower limit of the charge state of the storage battery; epsilonCAES(t) the charge state of the compressed air energy storage at the moment t;
Figure GDA0003250774320000094
respectively the upper and lower limits of the compressed air energy storage charge state.
It should be understood that the constraints of the battery charge or compressed air charge should also include the aforementioned points 1 and 3.
And 3, point: the capacity optimization planning strategy corresponding to the capacity optimization planning model clock is as follows:
when the grid-connected wind-solar-energy storage combined power generation system runs, when wind power and photovoltaic power generation are sufficient, load requirements are supplied, gravity is used for storing energy until rated capacity is reached, if the capacity is remained, the energy is fed into a power grid, and the method comprises the following steps:
PSE(t)＝Pwt(t)+Ppv(t)-Pgs(t)-PL(t)
wherein, PSE(t) power fed into the grid, Pwt(t) is the power of wind power generation at time t; ppv(t) is the power of photovoltaic power generation at time t; pL(t) power of the load at time t, PgsAnd (t) is the gravity energy storage power at the moment t.
When wind power and photovoltaic power generation are insufficient, gravity energy storage discharges, if the load requirement is not met, electricity is purchased from a power grid, namely
PBE(t)＝PL(t)-(Pwt(t)+Ppv(t)+Pgs(t))
Wherein, PBE(t) power supplied to the grid.
In step 3, a mature cat swarm algorithm solution model is selected in this embodiment, which includes the following steps:
step 3.1: referring to the model in step 2, actual parameters are needed in the solving process, wherein the latitude n is usedlatitudeLongitude n, longitudelongitudeAverage light intensity nlight_intAverage wind speed nwind_speedObtaining wind speed prediction data and solar illumination intensity prediction data; using the original load P at each momentload(t) obtaining a load curve; rated wind speed v of faneCut-in wind velocity vinCut-out wind speed voutSingle capacity p of fanwt(ii) a Working temperature C of battery plateworkReference temperature CckPower temperature coefficient σ, illumination intensity n under standard test conditionslight_bz(ii) a Weight m, gradient theta, gravity acceleration g of gravity-stored weight, and speed v of weight in gliding processdown(ii) a The optimization planning service life of the wind-solar-energy-storage combined power generation system is assumed to be y, and the depreciation rate is assumed to be d; the installation costs of the wind power plant, the photovoltaic power station, the gravity energy storage, the storage battery energy storage and the compressed air energy storage are respectively Cwt、Cpv、Cgs、Cbess、Ccaes(ii) a The operation and maintenance costs of the wind power plant, the photovoltaic power station, the gravity energy storage, the storage battery energy storage and the compressed air energy storage are respectively
Figure GDA0003250774320000101
Figure GDA0003250774320000102
The environmental value cost of the i-th pollutants under thermal power generation, wind power generation and photovoltaic power generation is
Figure GDA0003250774320000103
The electricity price of the power grid is CP(t); the price of electricity sold by the system is CS(t)；
For example, the parameters described above in this embodiment are specifically set as:
with latitude 38 DEG 44 ', longitude 106 DEG 0', average illumination intensity 4.32kW/m2Obtaining wind speed prediction data and solar illumination intensity prediction data by the average wind speed of 4.87 m/s; using the original load P at each momentload(t) obtaining a load curve; the rated wind speed of the fan is 11m/s, the cut-in wind speed is 3m/s, the cut-out wind speed is 20m/s, and the single capacity of the fan is 2.5 MW; the working temperature of the battery plate is 25 ℃, the reference temperature is 25 ℃, the power temperature coefficient is 1, and the illumination intensity under the standard test condition is 1kW/m2(ii) a The weight of the weight for gravity energy storage is 5 ten thousand tons, the gradient is 30 degrees, and the gravity acceleration is 9.8m/s2The speed of the weight in the gliding process is 10 m/s; the optimization planning service life of the wind-solar-energy-storage combined power generation system is assumed to be 20 years, and the depreciation rate is 5%; the installation costs of a wind power plant, a photovoltaic power station, gravity energy storage, storage battery energy storage and compressed air energy storage are 350.0 ten thousand yuan/MW, 619.5 ten thousand yuan/MW, 300.0 ten thousand yuan/MW and 505.0 ten thousand yuan/MW respectively; the operation and maintenance costs of a wind power plant, a photovoltaic power station, gravity energy storage, storage battery energy storage and compressed air energy storage are 3000 yuan/h, 2300 yuan/h, 1500 yuan/h, 2000 yuan/h and 1200 yuan/h respectively; SO under thermal power generation, wind power generation and photovoltaic power generation2The cost of the environmental value is 41.47 yuan/(MW & h), 0 and 0; NO under thermal power generation, wind power generation and photovoltaic power generationxThe cost of the environmental value is 23.04 yuan/(MW & h), 0 and 0; CO generated under thermal power generation, wind power generation and photovoltaic power generation2The cost of the environmental value is 27.42 yuan/(MW & h), 0 and 0; thermal power generation, wind power generation and photovoltaic power generationThe environmental value cost of the dust is 0.32 yuan/(MW & h), 0 and 0; the peak time period (10: 00-15: 00, 18: 00-21: 00) is 1110 yuan/(MW & h), the flat time period (07: 00-10: 00, 15: 00-18: 00, 21: 00-23: 00) is 680 yuan/(MW & h), and the valley time period (23: 00-07: 00) is 350 yuan/(MW & h); the system electricity selling price in the peak period (10: 00-15: 00, 18: 00-21: 00) is 1210 yuan/(MW & h), the system electricity selling price in the flat period (07: 00-10: 00, 15: 00-18: 00, 21: 00-23: 00) is 730 yuan/(MW & h), and the system electricity selling price in the valley period (23: 00-07: 00) is 370 yuan/(MW & h);
step 3.2: initializing group parameters, a memory pool SMP, a change domain SRD, a change number CDC and a grouping rate SR; for example, in this embodiment, the memory pool 30, the variation field 0.9, the variation number 50, and the packet rate 0.4 are set.
Step 3.3: will the objective function CtotalAs a fitness function of the cat swarm algorithm, taking the constraint condition as the constraint condition of the cat swarm algorithm;
step 3.4: the optimal solution meeting the requirements is obtained by utilizing a cat swarm algorithm, the output optimal solution is used as an optimal capacity planning result, and the optimal capacity planning result under the form of considering the gravitational potential energy is used for calculating the installed number N of the wind power plantwt(for representing wind farm capacity), photovoltaic plant capacity WpvAnd gravity energy storage capacity Wgs。
Step 3.5: considering the energy storage of the storage battery, repeating the steps 3.2-3.4, and taking the output optimal solution as the optimal capacity planning result in the step 3;
step 3.6: considering compressed air energy storage, repeating the step 3.2-3.4, and taking the output optimal solution as the optimal capacity planning result in the step 3;
since the cat swarm algorithm is an existing mature algorithm and the implementation process thereof is shown in the flowchart of fig. 3, the specific implementation process thereof is not specifically described in the present invention.
And 4, step 4: the process of evaluating and comparing the capacity planning models considering different energy storage forms to obtain the good and bad results in different energy storage forms by utilizing the rank and ratio evaluation method of entropy weight method weighting is as follows:
step 3.7: calculating each evaluation index value in different energy storage forms based on the optimal capacity planning result in each energy storage form, namely calculating corresponding wind-solar complementary characteristics, power supply loss rate and wind-solar-energy storage combined power generation system contribution rate in each energy storage form; and then determining the weight of the wind-light complementary characteristic, the power supply loss rate and the contribution rate of the wind-light storage combined power generation system by using an entropy weight method.
Step 3.8: and calculating the weighted rank sum ratio corresponding to different energy storage forms by using a rank sum ratio method based on the weight corresponding to each index.
Step 3.9: and ranking the advantages and disadvantages of different energy storage forms based on the weighted rank sum ratio value to obtain an evaluation result.
Specifically, the weighted rank sum ratio formula is as follows:
Figure GDA0003250774320000111
wherein R isSRiRepresenting the weighted rank sum ratio under the ith type of energy storage form; m is the number of capacity planning schemes, i.e. equal to 3 in this embodiment; w is ajThe weight corresponding to the jth evaluation index is n, which is the number of evaluation indexes, i.e. in this embodiment, equal to 3; rijThe evaluation index is the element value corresponding to the ith type of energy storage form and the jth evaluation index in the rank matrix R. The construction process of the rank matrix is as follows: n evaluation indexes of m capacity planning schemes form a matrix S, then indexes in the matrix S are subjected to uniformization processing, and indexes are ranked according to the positive and negative directions of the indexes to obtain a rank matrix R.
In some possible embodiments, the present invention also provides a planning system comprising:
the gravity energy storage model building module is used for building a gravity energy storage model depending on a mountain body;
the capacity optimization planning model building module is used for building capacity optimization planning models, such as a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage, a capacity optimization planning model containing a wind power plant, a photovoltaic power station and storage battery energy storage, and a capacity optimization planning model containing a wind power plant, a photovoltaic power station and compressed air energy storage;
and the optimal capacity planning structure calculation module is used for solving the capacity optimization planning model to obtain an optimal capacity planning result in the energy storage form.
Further, if compare with battery energy storage, compressed air energy storage, then the system still includes: and the evaluation module is used for evaluating and comparing the capacity planning models considering different energy storage forms by utilizing a rank and ratio evaluation method of entropy weight method weighting to obtain the quality results under different energy storage forms.
It should be understood that, the specific implementation process of the above unit module refers to the method content, and the present invention is not described herein in detail, and the division of the above functional module unit is only a division of a logic function, and there may be another division manner in the actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
In some possible embodiments, the invention also provides a system comprising a processor and a memory, the memory storing a computer program, the processor invoking the computer program to perform the steps of the capacity optimization planning method of the wind, photovoltaic and storage combined power generation system.
In some possible embodiments, the invention provides a readable storage medium storing a computer program for execution by a processor to perform the steps of the capacity optimization planning method for the wind, photovoltaic and energy storage combined power generation system.
It should be understood that in the embodiments of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any of the foregoing embodiments, for example, a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the controller. Further, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used for storing the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.

Claims (10)

1. A capacity optimization planning method of a wind-solar-storage combined power generation system is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a gravity energy storage model depending on a mountain body;
s2: constructing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and gravity energy storage based on the gravity energy storage model, wherein the minimum total cost of the wind-solar-energy storage combined power generation system is taken as a target function;
the total cost of the wind-solar-storage combined power generation system comprises: initial cost of system CINAnd running maintenance cost COMElectricity purchase cost C to the gridBESelling electricity income C to the power gridSEAnd environmental protection income CEP；
Wherein:
Figure FDA0003258910100000011
Figure FDA0003258910100000012
CBE＝CP(t)·(EBE(t)+Em_down(t))
wherein N iswtInstalling quantity for the wind power plant; cwtThe unit price of the fan is; wpvIs the photovoltaic power station capacity; cpvIs the price per unit capacity of the photovoltaic power station; wgsIs the gravity energy storage capacity; cgsIs the price per unit capacity of gravity energy storage; f. ofDRIs a depreciation factor; Δ twt、Δtpv、ΔtgsThe running time of the wind power plant, the photovoltaic power station and the gravity energy storage in one day is respectively;
Figure FDA0003258910100000013
the operation and maintenance costs of a wind power plant, a photovoltaic power station and gravity energy storage in unit time are respectively set; cP(t) is the grid electricity price at the moment t; eBE(t) the electric quantity purchased by the system to the power grid at the moment t; em_down(t) is the amount of electricity consumed by the motor during traction during gravity energy storage discharge;
s3: and solving the capacity optimization planning model in the step S2 to obtain an optimal capacity planning result in a gravitational potential energy form, wherein the optimal capacity planning result is the capacity of the wind power plant, the capacity of the photovoltaic power plant and the capacity of gravity energy storage.
2. The method of claim 1, wherein: further comprising:
a: establishing a capacity optimization planning model containing a wind power plant, a photovoltaic power station and storage battery energy storage and/or establishing a capacity optimization planning model containing the wind power plant, the photovoltaic power station and compressed air energy storage;
b: solving a capacity optimization planning model to obtain an optimal capacity planning result under the storage battery energy storage form and/or an optimal capacity planning result under the compressed air energy storage form;
c: and evaluating and comparing the capacity planning models considering different energy storage forms by utilizing a rank and ratio evaluation method of entropy weight method weighting to obtain the quality results under different energy storage forms.
3. The method of claim 2, wherein: if optimal capacity planning results in a mode of considering gravitational potential energy, a mode of considering energy storage of the storage battery and a mode of considering energy storage of compressed air are obtained respectively, the execution process of the step C is as follows:
calculating each evaluation index value in different energy storage forms based on the optimal capacity planning result in each type of energy storage form, and determining a weight corresponding to each evaluation index by using an entropy weight method, wherein the evaluation index at least comprises any two or three of wind-light complementary characteristics, power supply loss rate and contribution rate of a wind-light storage combined power generation system;
then, based on the weight corresponding to each index, calculating the weighted rank sum ratio corresponding to different energy storage forms by using a rank sum ratio method;
finally, ranking the advantages and disadvantages of different energy storage forms based on the weighted rank and the ratio to obtain an evaluation result;
the larger the weighted rank sum ratio is, the more excellent the wind-solar-energy-storage combined power generation system corresponding to the energy storage form is.
4. The method of claim 3, wherein: the calculation formulas of the wind-solar complementary characteristic, the power supply loss rate and the wind-solar-storage combined power generation system contribution rate are as follows:
Figure FDA0003258910100000021
Figure FDA0003258910100000022
Figure FDA0003258910100000023
Figure FDA0003258910100000024
in the formula, D is the wind-light complementary characteristic, f is the power supply loss rate, R is the contribution rate of the wind-light storage combined power generation system,
Figure FDA0003258910100000025
is the average power of the load, Pwt(t) is the power of the wind power generation at time t, Ppv(t) power of photovoltaic power generation at time t, PL(t) power of the load at time t, PgsAnd (t) is gravity energy storage power at the time t, and E (t) is power provided for a load by a system at the time t.
5. The method of claim 1, wherein: the energy W stored in the theoretical gravity energy storage in the gravity energy storage model in the step S1 satisfies the following condition:
W＝mg·h
in the formula, h is the height of a mountain;
in the gravity energy storage process, the motor consumes electric energy to pull the heavy object from the bottom of the mountain to the top of the mountain, and the power P of the motorm_upComprises the following steps:
Pm_up＝Fm_up·vup＝(mg·sinθ+Fμ)·vup＝(mg·sinθ+μ·mg·cosθ)·vup
in the formula, Fm_upFor motor traction during ascent, vupIs a heavy objectSpeed in the process of uniform speed rising, m is weight amount, g is gravity acceleration, theta is angle of hillside, FμIs the friction between the weight and the rail, mu is the friction coefficient of the rail;
gravity energy storage releases heavy objects from the mountain top track to the mountain bottom and is divided into an acceleration stage and a grid connection stage, the gravitational potential energy is reduced in the grid connection stage, an engine is driven to work, electric energy is generated to supply loads or is transmitted to a power grid, and the power P of a generator in the gliding processg_downComprises the following steps:
Pg_down＝Fg_down·vdown
wherein v isdownIs the speed of the weight in the process of sliding down at constant speed Fg_downIs the generator traction force in the gliding process and meets the following requirements:
mg·sinθ＝Fμ+Fm_down+Fg_down
in the formula, Fm_downIs the motor traction during the glide.
6. The method of claim 1, wherein: the partial parameter formula in the objective function is as follows:
Figure FDA0003258910100000031
wherein d is depreciation rate, and y is service life;
CSE＝CP(t)·ESE(t)+CS(t)·E(t)
wherein, CS(t) the price of electricity sold by the system at the time t; eSE(t) the electric quantity sold to the power grid by the system at the moment t; e (t) the electric quantity provided by the system to the load at the moment t;
Figure FDA0003258910100000032
wherein, wwt、wpvThe daily generated energy of wind power and photovoltaic power respectively; n isThe number of types of contaminants;
Figure FDA0003258910100000033
the environmental value cost of the i-th pollutants of thermal power generation, wind power generation and photovoltaic power generation respectively.
7. The method of claim 6, wherein: the constraint conditions of the objective function in the capacity optimization planning model containing the wind power plant, the photovoltaic power station and the gravity energy storage comprise:
the lower limit of the capacity of the wind-solar-energy-storage combined power generation system is 0, and the upper limit of the capacity is 10 times of the maximum load in one day;
the gravity energy storage capacity should satisfy: w is not less than 0gs≤mg·hgs_max，hgs_maxThe maximum height of the mountain, m is the weight, and g is the gravity acceleration;
power P exchanged between wind-light-storage combined power generation system and power gridg(t) satisfies: pg_min≤Pg(t)≤Pg_max，Pg_minAnd Pg_maxRespectively the minimum power and the maximum power that the system is allowed to exchange with the grid.
8. The method of claim 6, wherein: the capacity optimization planning strategy of the wind-solar-energy storage combined power generation system in the capacity optimization planning model containing the wind power plant, the photovoltaic power station and the gravity energy storage is as follows:
when the grid-connected wind-solar-energy storage combined power generation system runs, when wind power and photovoltaic power generation are sufficient, load requirements are supplied, gravity is used for storing energy until rated capacity is reached, if the capacity is remained, the energy is fed into a power grid, and the method comprises the following steps:
PSE(t)＝Pwt(t)+Ppv(t)-Pgs(t)-PL(t)
wherein, PSE(t) power fed into the grid, Pwt(t) is the power of wind power generation at time t; ppv(t) is the power of photovoltaic power generation at time t; pL(t) power of the load at time t, Pgs(t) is the gravity energy storage power at the moment t;
when wind power and photovoltaic power generation are insufficient, gravity energy storage discharges, if the load requirement is not met, electricity is purchased from a power grid, namely
PBE(t)＝PL(t)-(Pwt(t)+Ppv(t)+Pgs(t))
Wherein, PBE(t) power supplied to the grid.
9. A capacity optimization planning system of a wind-light-storage combined power generation system is characterized in that: comprising a processor and a memory, said memory storing a computer program, said processor invoking said computer program for performing the steps of the method of any one of claims 1-8.
10. A readable storage medium, characterized by: a computer program is stored, which is called by a processor to perform the steps of the method of any of claims 1-8.
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