CN117709098A - Wind-solar-water-storage integrated capacity optimization configuration method and system based on genetic algorithm - Google Patents

Abstract

The application provides a wind-solar-water-storage integrated capacity optimization configuration method and system based on a genetic algorithm, comprising the following steps: constructing a wind, light and water storage integrated system; simulating typical wind-light output data of a region; constructing a mathematical model of a wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system; based on typical wind-light output data, solving a Pareto set for a mathematical model of a wind-light-water storage integrated operation system by adopting a multi-target genetic algorithm; and a gray correlation analysis method is adopted to make a decision on the Pareto set so as to realize that a capacity configuration scheme with the maximum gray correlation gamma value is acquired by a strategy in unbiased, thereby meeting the optimal capacity ratio of an objective function and constraint conditions. According to the power transmission curve of the direct current channel, reasonable and effective capacity optimization configuration is realized under the conditions of economy, reliability and carbon emission, and a green power system with excellent competitiveness, environmental protection, low carbon and high utilization efficiency is constructed.

Description

Wind-solar-water-storage integrated capacity optimization configuration method and system based on genetic algorithm

Technical Field

The invention belongs to the technical field of energy storage planning of power systems, relates to a capacity configuration method, and particularly relates to a wind, light and water storage integrated capacity optimization configuration method and system based on a genetic algorithm.

Background

With the deep promotion of the double-carbon targets, the specific gravity of wind power and photovoltaic power generation in China in an electric power system is increased. But the problems of peak-valley characteristics of loads, seasonality of water and electricity, randomness and fluctuation of wind and light output, low equipment utilization rate, power supply reliability and the like of the power system are outstanding.

In order to better utilize wind and light new energy, the integrated operation of wind power, photovoltaic and water power according to a certain proportion is considered as an important means for reducing wind and light abandoning and improving resource utilization in a hydropower large-scale base mainly sent by direct current.

However, in the integrated operation system, the following problems exist in this approach:

(1) The prior art cannot completely meet the power demand of the service receiving end market, and cannot achieve source follow-up;

(2) The matching degree of the power supply and the load is not high;

(3) The wind-solar energy storage utilization rate is low, the cost is high, the environmental protection effect is poor, and the like.

Therefore, the targets such as economy, utilization rate and carbon emission are required to be fully considered, and the realization of reasonable and effective capacity optimization configuration is a key factor for the development of a wind-solar-water-storage integrated large base.

Therefore, in the prior art, because the wind-solar energy storage and direct current channel have low utilization rate, the power demand of the receiving end market cannot be better served to realize source follow-up, so that the capacity of the wind-solar energy storage integrated system is optimally configured, and the problems of low matching degree between a power supply and a load, low utilization rate, high cost, poor stability, low environmental protection effect and the like exist in the existing capacity optimal configuration process.

Disclosure of Invention

In view of the above-mentioned drawbacks of the prior art, the present application aims to provide a method and a system for optimizing and configuring wind-solar-water-storage integrated capacity based on a genetic algorithm, which are used for solving the problems in the prior art that the wind-solar-storage integrated system capacity is optimized and configured due to the fact that the wind-solar-storage integrated system is low in utilization rate and the direct-current channel, and the power demand of the receiving end market cannot be better served to follow up the source, and therefore the matching degree of a power supply and a load is low, the utilization rate is low, the cost is high, the stability is poor, the environmental protection effect is low, and the like in the existing capacity optimizing and configuring process.

To achieve the above object and other related objects, in a first aspect, the present application provides a method for optimizing and configuring a wind-solar-water-storage integrated capacity based on a genetic algorithm, including the following steps: constructing a wind, light and water storage integrated system; simulating typical wind-light output data of a region; constructing a mathematical model of the wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system; based on the typical wind-light output data, solving a Pareto set by adopting a multi-target genetic algorithm to the mathematical model of the wind-light-water storage integrated operation system; and adopting a gray correlation analysis method to make a decision on the Pareto set so as to realize that the capacity configuration scheme with the maximum gray correlation gamma value is acquired by the strategy in the unbiased state, thereby meeting the optimal capacity ratio of the objective function and the constraint condition.

In one implementation manner of the first aspect, constructing the wind-solar-water-storage integrated system includes the following steps: constructing a wind, light and water storage integrated system; the wind, light and water storage integrated system comprises: wind power plants, photovoltaic power stations, hydropower stations and collection stations; wherein, the power plant includes: a conventional unit and a reversible unit; judging the operation mode of the reversible unit based on the wind, light and water storage integrated system; comprising the following steps: when the sum of wind power, photovoltaic power and hydropower station power is larger than a direct current power transmission curve, the reversible unit operates in a pumping mode; when the sum of wind power, photovoltaic power and hydropower station power is smaller than or equal to a direct current power transmission curve, the reversible unit operates in a power generation mode.

In one implementation manner of the first aspect, the exemplary wind-solar power output data includes: photovoltaic output data, wind power output data and hydropower station output data; the calculation formula of the photovoltaic output data is as follows:

P _w (t)＝K _w (t)P _w

wherein K is _w (t) represents the 8760 output coefficient of the photovoltaic typical output of the region, t=1, 2, 3 …, 8760; p (P) _w Representing the installed capacity of the photovoltaic;

the wind power output data calculation formula is as follows:

P _pv (t)＝K _pv (t)P _pv

wherein K is _pv (t) represents the 8760 output coefficient of the wind power typical output of the region, t=1, 2, 3 …, 8760; p (P) _pv Representing the installed capacity of the wind power.

In one implementation manner of the first aspect, defining an operation manner of the wind, light and water storage integrated system includes: when the system operates, when the total power of wind, light and water power generation at a certain moment is smaller than or equal to a corresponding value in a power transmission curve of a direct current channel at the moment, the reversible unit operates in a power generation mode, and the water quantity of a reservoir is gradually reduced; when the system operates, when the total power of wind, light and water power generation at a certain moment is larger than a corresponding value in a direct current channel power transmission curve at the moment, the reversible unit operates in a pumping mode, and the water quantity of the reservoir is gradually increased; in the power generation mode, when the total power of wind-solar water power generation minus direct current power in a certain period is larger than the required water pumping power of the reversible unit, the load is not lost, and the power supply is reliable; when the total power of wind-solar water power generation in a certain period is deducted by the water pumping power of the reversible unit required by direct current power transmission to be less than or equal to the required water pumping power, the load is lost, and the power supply is unreliable; in the pumping mode, if the reservoir capacity is full and the water level is lower than the normal navigation water level, the wind and light are abandoned; and pumping water storage if the reservoir capacity is not full and the water level is greater than or equal to the normal navigation water level.

In one implementation of the first aspect, defining the objective function includes: a system cost minimum objective function, a system economic benefit maximum objective function and a system carbon emission minimum objective function; the calculation formula of the lowest cost objective function of the system is as follows:

minF1＝min{D _w +D _pv +D _ph }

Wherein D is _w Representing the total cost of the wind farm; d (D) _pv Representing the total cost of the photovoltaic power station; d (D) _ph Representing the total cost of the hydropower station; c (C) _w Representing the unit price of wind power unit capacity; c (C) _pv Representing the unit price of photovoltaic unit capacity; c (C) _cap Representing unit price of reservoir capacity; c (C) _power The unit capacity unit price of the reversible water pump turbine unit is shown; n (N) _w Representing the installed capacity of wind power in a wind power plant; n (N) _pv Representing the installed capacity of the photovoltaic; v (V) _cap Representing reservoir capacity; p (P) _power Representing the installed capacity of the reversible hydraulic turbine unit; c (C) _om,w Expressed as wind power operation maintenance cost; c (C) _om,pv Representing the photovoltaic operation maintenance cost; c (C) _om,cap Representing the operation and maintenance cost of the reservoir; c (C) _om,power Representing the operation and maintenance cost of the reversible water pump hydroelectric generating set; t (T) _a Representing the life cycle of the upper reservoir; r represents the discount rate;

the calculation formula of the maximum objective function of the economic benefit of the system is as follows:

wherein C is _e (t) represents the electric quantity supplied to the direct current channel by the 3 power supplies at the moment t; Δt represents a time interval;

the minimum objective function calculation formula of the system carbon emission is as follows:

wherein F3 represents the annual carbon emission of the system; r is R _w Carbon emission coefficient expressed as wind power over the full life cycle; r is R _pv Carbon emission coefficient expressed as photovoltaic over the full life cycle; r is R _phes Carbon emission coefficient expressed as reversible unit in full life cycle; r is R _cap Expressed as the carbon emission coefficient of the reservoir over the full life cycle.

In one implementation manner of the first aspect, the constraint condition includes: the system is limited by the power rejection rate, the power supply stability, the power supply output and the reversible unit pumping power mode, and the reservoir capacity and water quantity change; the calculation formula of the system power rejection rate limit is as follows:

P _R (t)＝P _pv (t)+P _w (t)+P _phes (t)-P _L (t)-P _p (t)

wherein P is _R (t) represents the wind-discarding and light-discarding power at time t; p (P) _pv (t)+P _w (t) represents the total power of wind and light electricity at the moment t; p (P) _L (t) represents a direct current power transmission curve at time t;

the power supply stability constraint calculation formula is as follows:

wherein, Z (t) =1 represents that the wind-solar-water power generation is smaller than a direct-current channel power transmission curve; z (t) =1 represents that the wind-solar water power generation is greater than or equal to a direct current channel power transmission curve;

the power supply output constraint formula is as follows:

0≤P _w,all ≤P _w,max ,0≤P _pv,all ≤P _pv,max

0≤P _power,all ≤P _power,max

wherein P is _w,all Representing the output power of the wind farm; p (P) _pv,all Representing the output power of the photovoltaic power plant; p (P) _w,max Representing an upper limit of output power of the wind farm; p (P) _pv,max Representing an upper output power limit of the photovoltaic power plant; p (P) _power,all The upper limit of the output power of a reversible water pump hydroelectric generating set in a hydropower station is represented;

the constraint formula of the pumping electric mode of the reversible unit is as follows:

wherein P is _p (t) is a non-negative number; p (P) _h (t) is a non-negative number;

the reservoir capacity and water quantity change constraint formula is as follows:

W _cap,min (t)≤W(t)≤W _cap,max (t)

V _cap,min (t)≤V _cap (t)≤V _cap,max (t)

Wherein W is _cap,min Representing the guaranteed output of the reservoir; w (W) _cap,max (t) represents the expected force of the reservoir; v (V) _cap,min (t) represents the reservoir capacity under the guaranteed output condition of reservoir capacity; v (V) _cap,max And (t) represents the reservoir capacity under the expected force conditions of the reservoir capacity.

In one implementation manner of the first aspect, based on the typical wind-light output data, solving the Pareto set by using a multi-objective genetic algorithm for the mathematical model of the wind-light-water-storage integrated operation system includes the following steps: initializing parameters of a multi-target genetic algorithm; the multi-objective genetic algorithm parameters include: population size, number of iterations, probability of crossover and mutation operations; non-dominant sorting and crowding degree calculation are carried out based on the initialized multi-target genetic algorithm parameters, and a primary population is generated; performing selection operation, crossover operation and mutation operation on the primary population; combining the operated population with the primary population to obtain a first population; then, non-dominant sorting and crowding degree calculation are carried out on the new population to obtain a second population; and sequentially processing the population until the current algebra is equal to the preset algebra, thereby obtaining a solving Pareto set.

In an implementation manner of the first aspect, the decision is made on the Pareto set by using a gray correlation analysis method, so as to realize that a capacity configuration scheme with the maximum gray correlation gamma value is acquired by a policy in no deflection, and the method comprises the following steps: preprocessing based on the data in the solving Pareto set; determining a reference sequence based on the preprocessed solution Pareto sets, and calculating gray correlation degree between the solution in each Pareto set and the reference sequence; averaging all the gray correlation degrees, and selecting the solution with the maximum average value as the optimal solution; and analyzing the found optimal solution, and identifying output data with the greatest influence on the result in all the typical wind-solar output data.

In a second aspect, the present application provides a wind, light and water storage integrated capacity optimization configuration system based on a genetic algorithm, including: the system forming module is used for constructing a wind, light and water storage integrated system; the data initialization module is used for simulating typical wind-light output data of the area; the mathematical model construction module is used for constructing a mathematical model of the wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system; the calculation module is used for solving a Pareto set on the mathematical model of the wind-light-water-storage integrated operation system by adopting a multi-objective genetic algorithm based on the typical wind-light output data; and the decision module is used for deciding the Pareto set by adopting a gray correlation analysis method so as to realize that the capacity configuration scheme with the maximum gray correlation gamma value is acquired by a strategy in unbiased deflection, thereby meeting the optimal capacity ratio of an objective function and constraint conditions.

As described above, the wind-solar-water-storage integrated capacity optimization configuration method and system based on the genetic algorithm have the following beneficial effects:

the application provides a genetic algorithm-based wind, light and water storage integrated capacity optimization configuration method, which solves the problem of multiple constraints and multiple targets in engineering by applying a mathematical model method. The capacity proportioning scheme of the wind-solar-energy-storage integrated system is as follows; the optimal capacity ratio of the system cost, the annual economic benefit and the annual carbon emission multi-objective function under the constraint condition can be comprehensively considered, a capacity optimization configuration model is solved through a mature genetic algorithm based on a mathematical modeling method, an accurate wind, light and water capacity configuration Pareto solution set is solved, and the optimal capacity configuration is selected by adopting a gray correlation analysis (GRA) according to the weight value of each objective function. The method is suitable for the hydropower station large-scale base mainly based on direct current delivery, under the condition that wind and light resources are good, the utilization rate of a channel is improved by additionally installing the reversible unit in the hydropower station, the matching degree of a power supply and a load is improved, and clean energy with high stability, reliability and adjustment capability is provided.

Drawings

Fig. 1 is a schematic flow chart of an embodiment of the wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm.

Fig. 2 is a schematic flow chart of S11 in the method for optimizing and configuring the capacity of the wind-solar-water-storage integration based on the genetic algorithm.

Fig. 3 shows a schematic diagram of a wind-light-water-storage integrated operation system in the wind-light-water-storage integrated capacity optimization configuration method based on a genetic algorithm.

Fig. 4 shows an operation mode diagram of a wind-light-water-storage integrated system mainly comprising direct-current output in the wind-light-water-storage integrated capacity optimization configuration method based on a genetic algorithm.

Fig. 5 shows a schematic flow chart of S14 in the wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm.

FIG. 6 shows a schematic diagram of the NSGA-II calculation procedure of the present invention.

Fig. 7 is a schematic flow chart of S15 in the method for optimizing and configuring the wind, light and water storage integrated capacity based on the genetic algorithm.

Fig. 8 is a schematic structural diagram of a wind-solar-water-storage integrated capacity optimization configuration system based on a genetic algorithm according to an embodiment of the present invention.

Description of element reference numerals

81. System building block

82. Data initialization module

83. Mathematical model construction module

84. Calculation module

85. Decision module

S11 to S15 steps

Detailed Description

Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.

It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

The following describes the wind, light and water storage integrated capacity optimization configuration method and system based on the genetic algorithm in the embodiment of the application in detail with reference to the drawings in the embodiment of the application. The method and the device are used for solving the problems that in the prior art, as the wind-solar energy storage and direct current channel are low in utilization rate, the power demand of a receiving end market cannot be better served, and the source follow-up is achieved, so that the capacity of the wind-solar energy storage integrated system is optimally configured, and the matching degree of a power source and a load is low, the utilization rate is low, the cost is high, the stability is poor, the environmental protection effect is low and the like in the conventional capacity optimal configuration process.

The method solves the problem of multiple constraints and multiple targets in engineering by using a mathematical model method. For the capacity proportioning scheme of the wind-solar energy storage integrated system, firstly, constructing a mathematical model of the wind-solar energy storage integrated operation system, defining a system operation mode, and defining an objective function and constraint conditions; secondly, based on typical wind and light output data of a region where a system is located, performing multi-objective optimization solution on a wind and light storage integrated model by using NSGA-II; and finally, deciding the obtained Pareto solution set by adopting a gray correlation analysis (GRA), and further realizing a capacity configuration scheme with the maximum gray correlation gamma value obtained by a non-deflection middle strategy, namely, the optimal capacity ratio meeting the objective function and constraint conditions. The invention is suitable for hydropower large-scale bases mainly sent by direct current, improves the utilization rate of a channel by additionally arranging a reversible unit in a hydropower station under the condition of better wind and light resources, improves the matching degree of a power supply and a load, and provides clean energy with stability, reliability and strong regulating capability.

Referring to fig. 1, a schematic flow chart of an embodiment of the wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm according to the present invention is shown. As shown in fig. 1, the embodiment provides a wind-solar-water-storage integrated capacity optimization configuration method based on a genetic algorithm.

The wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm specifically comprises the following steps:

s11, constructing a wind, light and water storage integrated system. Referring to fig. 2, a flow chart of S11 in the method for optimizing and configuring the capacity of the wind-solar-water-storage integration based on the genetic algorithm according to the present invention is shown. As shown in fig. 2, the step S11 includes the following steps:

s111, constructing a wind, light and water storage integrated system.

Referring to fig. 3, a schematic diagram of a wind-light-water-storage integrated operation system in the wind-light-water-storage integrated capacity optimization configuration method based on a genetic algorithm according to the present invention is shown.

In this embodiment, the wind-solar-water-storage integrated system includes: wind farms, photovoltaic power plants, hydroelectric plants and pooling stations. Wherein, the power plant includes: and the conventional water turbine, the conventional unit, the reversible water turbine, the reversible unit and other resources. The wind, light and water storage integrated system is mainly based on a direct current channel.

Specifically, photovoltaic power generation, wind power generation, hydropower station power generation and reversible unit power generation are collected into a collection station, and then the collected electric energy is integrated into a power grid in a form of direct current channel transmission.

S112, judging the operation mode of the reversible unit based on the wind, light and water storage integrated system.

Referring to fig. 4, a running mode diagram of the wind-light-water-storage integrated system based on direct-current output in the wind-light-water-storage integrated capacity optimizing configuration method based on the genetic algorithm is shown.

In the embodiment, when the sum of wind power, photovoltaic power and hydropower station power is larger than a direct current power transmission curve, the reversible unit operates in a pumping mode; when the sum of wind power, photovoltaic power and hydropower station power is smaller than or equal to a direct current power transmission curve, the reversible unit operates in a power generation mode to supplement load requirements. The direct current power transmission curve is drawn according to the electricity purchase and sale contract and the spot transaction rule.

And (3) according to the power demand of the receiving end, the power transmission and selling protocol and the spot transaction rule, drawing a direct current power transmission curve. The method is characterized by comprising the following steps: p (P) _L (t), wherein t=1, 2, 3 …, 8760.

S12, simulating typical wind-light output data of the area.

In this embodiment, the exemplary wind-solar power output data includes: photovoltaic output data, wind power output data, hydropower station output data and other types of data.

Specifically, the calculation formula of the photovoltaic output data is as follows:

P _w (t)＝K _w (t)P _w

wherein K is _w (t) represents the 8760 output coefficient of the photovoltaic typical output of the region, t=1, 2, 3 …, 8760; p (P) _w Representing the installed capacity of the photovoltaic. The 8760 force coefficient of a typical photovoltaic force is generally simulated from mesoscale data.

The wind power output data calculation formula is as follows:

P _pv (t)＝K _pv (t)P _pv

wherein K is _pv (t) represents 8760 output coefficients of wind power typical output in the region, t=1, 2, 3 … and 8760, and is generally obtained by data simulation of measured data of a anemometer tower; p (P) _pv Representing the installed capacity of the wind power.

S13, constructing a mathematical model of the wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system.

In the embodiment, multi-objective optimization is performed based on wind-light-water-storage integrated systems and typical wind-light output data. The objective function of the wind-solar-water-storage integrated delivery capacity optimization configuration optimization mainly comprises the following steps: safe and reliable, economical and efficient, environment-friendly and the like, thereby realizing the process of reasonably and effectively carrying out capacity optimization configuration. Then, selecting a proper parameter estimation method according to the acquired data; and determining constraint conditions and objective functions of a mathematical model of the wind, light and water storage integrated operation system.

Firstly, defining an operation mode of the wind, light and water storage integrated system.

In this embodiment, the components of the hydropower station are: conventional units and reversible units. The power generation capacity of the conventional unit of the hydropower station determines output according to the water inflow, and the output is recorded as follows: p (P) _phes (t). The reversible unit of the hydropower station determines whether the reversible unit is in a pumping or generating running state according to the total sum of wind, light and water output, the residual water-fillable capacity of the reservoir and the power transmission curve of the direct current channel, and the pumping output is recorded as the water-discharging power generation output is recorded as follows: p (P) _h (t)。

Specifically, the operation mode of the wind, light and water storage integrated system comprises the following steps: when the system operates, when the total power of wind, light and water power generation at a certain moment is smaller than or equal to a corresponding value in a power transmission curve of a direct current channel at the moment, the reversible unit operates in a power generation mode, and the water quantity of a reservoir is gradually reduced; when the system operates, when the total power of wind, light and water power generation at a certain moment is larger than a corresponding value in a direct current channel power transmission curve at the moment, the reversible unit operates in a pumping mode, and the water quantity of the reservoir is gradually increased.

In the power generation mode, when the total power of wind-solar water power generation minus direct current power in a certain period is larger than the required water pumping power of the reversible unit, the load is not lost, and the power supply is reliable; when the total power of wind-solar water power generation in a certain period is subtracted by the water pumping power of the reversible type unit, which is less than or equal to the required water pumping power of the reversible type unit, the load is lost, and the power supply is unreliable.

In the pumping mode, if the reservoir capacity is full and the water level is lower than the normal navigation water level, the wind and light are abandoned; and pumping water storage if the reservoir capacity is not full and the water level is greater than or equal to the normal navigation water level.

Then, an objective function is defined. The cost (F1) of the wind, light and water storage integrated operation system mainly based on direct current is comprised of the investment cost of each main body part, the replacement cost of the whole project period and the operation and maintenance cost.

In this embodiment, the objective function involved includes: the system cost is the lowest objective function, the system economic benefit is the largest objective function and the system carbon emission is the smallest objective function.

Specifically, the system cost minimum objective function calculation formula is:

minF1＝min{D _w +D _pv +D _ph }

wherein D is _w Representing the total cost of the wind farm; d (D) _pv Representing the total cost of the photovoltaic power station; d (D) _ph Representing the total cost of the hydropower station; c (C) _w Representing the unit price of wind power unit capacity; c (C) _pv Representing the unit price of photovoltaic unit capacity; c (C) _cap Representing unit price of reservoir capacity; c (C) _power The unit capacity unit price of the reversible water pump turbine unit is shown; n (N) _w Representing the installed capacity of wind power in a wind power plant; n (N) _pv Representing the installed capacity of the photovoltaic; v (V) _cap Representing reservoir capacity; p (P) _power Representing the installed capacity of the reversible hydraulic turbine unit; c (C) _om,w Expressed as wind power operation maintenance cost; c (C) _om,pv Representing the photovoltaic operation maintenance cost; c (C) _om,cap Representing the operation and maintenance cost of the reservoir; c (C) _om,power Representing the operation and maintenance cost of the reversible water pump hydroelectric generating set; t (T) _a Representing the life cycle of the upper reservoir; r represents the discount rate, taking 5%.

The basis of the maximum economic benefit of the system is that a wind, light and water storage integrated operation system mainly sent by direct current is taken as a whole, and the annual economic benefit (F2) comprises the power supply benefits of wind, light and water 3 power supplies to a direct current channel. The calculation formula of the maximum objective function of the economic benefit of the system is as follows:

wherein C is _e (t) represents the electric quantity supplied to the direct current channel by the 3 power supplies at the moment t; Δt represents the time interval, taking 1h.

Since the full life cycle of an asset is generally referred to as: the whole life process of manufacturing installation, production operation, operation maintenance and recovery treatment. And for wind power, photovoltaic and hydropower stations, carbon emission hardly exists in production operation links, and the carbon emission is mainly concentrated in the remaining 3 links. Coal-fired power generation is a main power supply source of a system power grid, and carbon emission exists in 4 links of the whole life cycle. According to statistics, CO ₂ The contribution of the emission amount to the carbon emission exceeds 60%. Therefore, in discussing carbon emissions, CO is mainly considered ₂ The effect of emissions on the capacity configuration of the system. Namely: for the target of minimum system carbon emissions to be achieved.

The annual energy generation amount of the generating part of the wind power plant, the photovoltaic power station and the hydroelectric conventional unit, the annual energy consumption of the pumping part of the reversible unit and the energy storage capacity of the reservoir are utilized, and the annual carbon emission (F3) of the system is calculated.

The minimum objective function calculation formula of the system carbon emission is as follows:

wherein F3 represents the annual carbon emission of the system; r is R _w Carbon emission coefficient expressed as wind power over the full life cycle; r is R _pv Carbon emission coefficient expressed as photovoltaic over the full life cycle; r is R _phes Carbon emission coefficient expressed as reversible unit in full life cycle; r is R _cap Expressed as the carbon emission coefficient of the reservoir over the full life cycle. Carbon emission coefficient R of wind power in full life cycle _w Carbon emission coefficient R of photovoltaic in full life cycle _pv Carbon emission coefficient R of reversible unit in full life cycle _phes Carbon emission coefficient R of reservoir in full life cycle _cap Are all calculated according to the related data of the annual development report 2021 of the electric power industry of China,the following are tentatively set in this embodiment: 0.1664kg/kWh, 0.1664kg/kWh, 0.1664kg/kWh, 0.416kg/kWh.

Next, constraints are defined.

In this embodiment, the constraint condition includes: the system has the advantages of power rejection rate limitation, power supply stability constraint, power supply output constraint, reversible unit pumping power mode constraint and reservoir capacity and water quantity change constraint.

Specifically, firstly, due to randomness and uncertainty of wind energy and solar energy, the power grid has limited capacity of absorbing wind and light, and the phenomenon of discarding wind and light can be caused. Therefore, a scientific and reasonable integrated system operation mode can be adopted by additionally installing the reversible unit, so that the utilization rate of renewable energy sources is effectively improved, and the wind and light abandoning is avoided.

The wind and light discarding means that one reason is limited by the fact that wind, water and light energy are forced to be discarded, the corresponding generator set is stopped or the generated energy is reduced, and the generated energy of the photovoltaic power station is larger than the maximum transmission electric quantity+load dissipation electric quantity of the electric power system. The wind abandoning is a phenomenon that part of wind power plant fans are suspended due to the characteristics of insufficient receiving capacity of a local power grid, unmatched construction period of the wind power plant, unstable wind power and the like under normal condition of the fans in the initial stage of wind power development. Regarding the wind abandoning phenomenon, relevant experts in national grid energy institute consider that the maximum wind abandoning reason is that the power grid construction speed is not in keeping with the development speed of clean energy. Ensuring the consumption is a system problem, and also requires the construction of the whole electric power market and policy matching. And the light rejection is that the generated energy of the photovoltaic power station is larger than the maximum transmission electric quantity of the power system and the load dissipation electric quantity. Reject ratio = power generation of photovoltaic power plant- (power system maximum transmission capacity + load dissipation capacity)/power generation of photovoltaic power plant. The "wind-abandoning electricity limiting electricity" refers to the phenomenon that when the wind turbine is in a normal condition, the wind turbine of the wind power plant is suspended due to insufficient receiving capacity of a local power grid, collision of wind power and thermal power and the like.

The calculation formula of the system power rejection rate limit is as follows:

P _R (t)＝P _pv (t)+P _w (t)+P _phes (t)-P _L (t)-P _p (t)

wherein P is _R (t) represents the wind-discarding light power at the moment t, wherein the water output is not discarded, and the water is fully discharged according to the water yield; p (P) _pv (t)+P _w (t) represents the total power of wind and light electricity at the moment t; p (P) _L And (t) represents a direct current power transmission curve at the time t. When R is _CR When the load loss rate is 0, the combined operation system can utilize wind and light to the maximum degree in the simulation time, and the load loss rate is defined as less than 5 percent in the application.

For the power supply stability, whether the LOLP (loss ofloadprobability, probability of a direct current power supply curve) can be satisfied is adopted as an index for evaluating the reliability of the power supply stability of the system to the load side. And the power supply stability constraint calculation formula is as follows:

wherein, Z (t) =1 represents that the wind-solar-water power generation is smaller than a direct-current channel power transmission curve; and Z (t) =1 represents a power transmission curve of the direct current channel and the wind-solar water power generation is larger than or equal to the power transmission curve of the direct current channel.

The power supply output constraint formula is as follows:

0≤P _w,all ≤P _w,max ,0≤P _pv,all ≤P _pv,max

0≤P _power,all ≤P _power,max

wherein P is _w,all Representing the output power of the wind farm; p (P) _pv,all Representing the output power of the photovoltaic power plant; p (P) _w,max Representing an upper limit of output power of the wind farm; p (P) _pv,max Representing an upper output power limit of the photovoltaic power plant; p (P) _power,all And the upper limit of the output power of the reversible water pump hydroelectric generating set in the hydropower station is indicated.

For the pumping mode and the generating mode constraint of the reversible water pump hydroelectric generating set, the reversible water pump hydroelectric generating set in the reservoir cannot be in the pumping mode and the generating mode at the same time, because for the same net output, the more the amount of pumping and generating are performed at the same time, the more resources are wasted. Therefore, the constraints of the design are as follows:

The constraint formula of the pumping electric mode of the reversible unit is as follows:

wherein P is _p (t) is a non-negative number; p (P) _h (t) is a non-negative number; therefore, both of each period must have a term of 0 to meet constraint requirements.

At any time, the water quantity of the limited reservoir cannot exceed the set capacity value, and the water quantity of the upper reservoir changes by 0 in any day.

The reservoir capacity and water quantity change constraint formula is as follows:

W _cap,min (t)≤W(t)≤W _cap,max (t)

V _cap,min (t)≤V _cap (t)≤V _cap,max (t)

wherein W is _cap,min Representing the guaranteed output of the reservoir; w (W) _cap,max (t) represents the expected force of the reservoir; v (V) _cap,min (t) represents the reservoir capacity under the guaranteed output condition of reservoir capacity; v (V) _cap,max And (t) represents the reservoir capacity under the expected force conditions of the reservoir capacity.

S14, solving a Pareto set by adopting a multi-objective genetic algorithm to the mathematical model of the wind, light and water storage integrated operation system based on the typical wind, light and output data. Referring to fig. 5, a schematic flow chart of S14 in the method for optimizing and configuring wind, light and water storage integrated capacity based on the genetic algorithm according to the present invention is shown. As shown in fig. 5, the step S14 includes the following steps:

in the method, factors such as economy, stability, environmental protection and the like are considered, and a multi-objective optimization function is adopted to solve an optimal problem; and then, evaluating the merits of each scheme by adopting a gray correlation method.

NSGA-II is one of the most popular multi-objective genetic algorithms, reduces the complexity of non-inferior sorting genetic algorithms, has the advantages of high running speed and good convergence of solution sets, and becomes a benchmark for the performance of other multi-objective optimization algorithms.

Solving the multi-objective and multi-constraint capacity optimization configuration problem, adopting NSGA-II, and setting the population quantity in the algorithm as N according to the requirement. Referring to FIG. 6, a schematic diagram of the NSGA-II calculation process of the present invention is shown.

S141, initializing parameters of the multi-objective genetic algorithm. The multi-objective genetic algorithm parameters include: population N size, number of iterations, probability of crossover and mutation, etc.

In this embodiment, first, a coding mode needs to be defined to convert the decision variable into a binary string or other form that can be processed by the genetic algorithm. For example, the decision variables are power distribution of hydropower station power generation and wind and light power generation, and each decision variable can be mapped into a binary character string in a binary coding mode. And then, calculating the fitness value of each population, namely the objective function value, based on a mathematical model of the wind-light-water-storage integrated operation system, and randomly generating a group of solutions serving as an initial population.

S142, non-dominant ranking and crowding degree calculation are carried out based on the initialized multi-objective genetic algorithm parameters, and a primary population is generated.

In multi-objective optimization, non-dominant ranking is to rank solutions in a population according to a dominant relationship, where dominant relationship means that one solution is not inferior to another solution on all objectives.

In this embodiment, solutions in the population may be layered by non-dominant ordering, progressively progressing from inferior to superior. Meanwhile, in order to preserve excellent solutions, it is necessary to calculate the degree of congestion of each solution, i.e., the distance between the solution and the adjacent solution. Solutions with less crowding are more likely to be selected as parents of the next generation.

Specifically, after each iteration is finished, non-dominant sorting is carried out on the population to form a non-dominant solution set; the degree of congestion is calculated for each non-dominant solution. Wherein, the crowding degree is an index for measuring the distribution density of the solution in the target space; the greater the degree of crowding, the more sparse the other solutions around the solution, i.e., the higher the quality of the solution.

S143, performing selection operation, crossover operation and mutation operation on the primary population.

In this embodiment, two solutions are selected from the current population to be interleaved and mutated to produce a new solution. In this process, crossover probabilities and mutation probabilities are used to control the generation of new solutions.

Specifically, based on non-dominant ranking and crowding, a good solution is selected to enter the next generation population. A certain number of solutions are randomly selected from the population, and the optimal solution is selected as the parent of the next generation according to the fitness value of the solutions. The selection operation may employ a tournament selection method or an adaptive scale-based selection method, etc. Two parents are randomly selected, a cross point is randomly selected between the parents, and the parents are subjected to cross recombination to obtain two offspring. The crossing operation may be performed by single-point crossing, multi-point crossing, uniform crossing, or the like. And (3) performing crossover and mutation operations on individuals in the population to increase the diversity of the population and prevent the algorithm from falling into a local optimal solution.

S144, merging the operated population with the primary population to obtain a first population.

In this embodiment, the offspring of the new generation are combined with the parent to form a new population. And during merging, elite strategy can be adopted, namely, excellent solutions in the parent are reserved, so that the parent can directly enter a new generation population.

S145, performing non-dominant sorting and crowding degree calculation on the new population to obtain a second population; and sequentially processing the population until the current algebra is equal to the preset algebra, thereby obtaining a solving Pareto set.

In this embodiment, non-dominant ranking and crowding calculations are performed on the new population to determine the status and value of each solution. Based on the results of the non-dominant ranking and the crowding calculation, a number of excellent solutions are selected as a new generation population. Judging whether a preset termination condition is reached or not; if the preset termination condition is not met, adding 1 to the current algebra, returning to perform the steps of selecting, crossing and mutation again, merging the populations and the like for cyclic treatment; if the preset termination condition is reached, outputting all the found Pareto solutions and the corresponding decision variable values.

Among all solutions, those that cannot be better than any other solution on other targets are found, which constitute the Pareto set. For multi-objective optimization problems, this step is typically required to find all possible Pareto solutions.

And S15, deciding the Pareto set by adopting a gray correlation analysis method to realize that the capacity configuration scheme with the maximum gray correlation gamma value is acquired by a strategy in unbiased deflection, thereby meeting the optimal capacity ratio of an objective function and constraint conditions. Referring to fig. 7, a schematic flow chart of S15 in the method for optimizing and configuring the capacity of the wind-solar-water-storage integration based on the genetic algorithm according to the present invention is shown. As shown in fig. 7, the step S15 includes the following steps:

s151, preprocessing is conducted based on the data in the Pareto set.

In this embodiment, the data in each Pareto solution set is initialized, so that the data meets the data requirement of the gray correlation analysis method.

S152, determining a reference number sequence based on the preprocessed solution Pareto sets, and calculating gray association degree of the solutions in each Pareto set and the reference number sequence.

The reference number sequence is an optimal number sequence reflecting the association relationship between the systems or factors.

In this embodiment, a solution in the best Pareto solution set is selected as a reference sequence; for each solution in the Pareto solution set, it is necessary to calculate its gray correlation with the reference number columns. The calculation formula of the gray correlation degree can be selected according to specific situations, and generally comprises the steps of data preprocessing, correlation coefficient calculation, correlation degree calculation and the like.

And S153, averaging all the gray correlation degrees, and selecting a solution with the maximum average value as an optimal solution.

In this embodiment, an unbiased compromise policy is adopted to average all gray correlation degrees, and a solution with the maximum average value is selected as an optimal solution.

And S154, analyzing the found optimal solution, and identifying output data with the largest influence on the result in all the typical wind-solar output data.

In this embodiment, if the resulting solution set is not optimal, the algorithm needs to be optimized and adjusted. For example: changing algorithm parameters, improving models or reselecting reference arrays. Then, the result is outputted. The end result is a set of capacity allocation schemes that meet all constraints and have the highest gray correlation.

Therefore, by analyzing 3 objective functions of the system cost F1, the annual economic benefit F2, and the annual carbon emission F3 as influence factors of GRA, in order to balance the influence of the 3 objective function values on the capacity allocation scheme selection, the weight ω of each influence factor is set to 0.333, and the number of the system capacity allocation schemes to be decided is N. And adopting a gray correlation analysis (GRA) method to make a decision on the obtained Pareto solution set, and further realizing a capacity configuration scheme with maximum gray correlation gamma value obtained by a policy in unbiased middle.

The wind, light and water storage integrated capacity optimization configuration method based on the genetic algorithm can comprehensively consider the optimal capacity ratio of the multi-objective function of the system cost, the annual economic benefit and the annual carbon emission under the constraint condition, solve an accurate wind, light and water capacity configuration Pareto solution set through a mature genetic algorithm based on a mathematical modeling method by a capacity optimization configuration model, and select the optimal capacity configuration by adopting a gray correlation analysis (GRA) according to the weight value of each objective function. The method is suitable for the hydropower station large-scale base mainly based on direct current delivery, under the condition that wind and light resources are good, the utilization rate of a channel is improved by additionally installing the reversible unit in the hydropower station, the matching degree of a power supply and a load is improved, and clean energy with high stability, reliability and adjustment capability is provided.

The protection scope of the wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm in the embodiment of the application is not limited to the step execution sequence listed in the embodiment, and all the schemes implemented by increasing or decreasing steps and replacing steps according to the prior art made according to the principles of the application are included in the protection scope of the application.

The embodiment of the application also provides a genetic algorithm-based wind-light-water-storage integrated capacity optimization configuration system, which can realize the genetic algorithm-based wind-light-water-storage integrated capacity optimization configuration method, but the implementation device of the genetic algorithm-based wind-light-water-storage integrated capacity optimization configuration method comprises, but is not limited to, the structure of the genetic algorithm-based wind-light-water-storage integrated capacity optimization configuration system, and all the structural deformation and replacement of the prior art according to the principles of the application are included in the protection scope of the application.

The wind-solar-water-storage integrated capacity optimization configuration system based on the genetic algorithm provided by the embodiment will be described in detail with reference to the drawings.

The embodiment provides a genetic algorithm-based wind, light and water storage integrated capacity optimization configuration system, which comprises the following components:

referring to fig. 8, a schematic structural diagram of a wind-solar-water-storage integrated capacity optimization configuration system based on a genetic algorithm according to an embodiment of the present invention is shown. As shown in fig. 8, the system for optimizing and configuring the capacity of the wind, light and water storage integration based on the genetic algorithm comprises: a system constitution module 81, a data initialization module 82, a mathematical model construction module 83, a calculation module 84, and a decision module 85.

The system constructing module 81 is used for constructing a wind, light and water storage integrated system.

In this embodiment, the wind-solar-water-storage integrated system includes: wind farms, photovoltaic power plants, hydroelectric plants and pooling stations. Wherein, the power plant includes: and the conventional water turbine, the conventional unit, the reversible water turbine, the reversible unit and other resources. The wind, light and water storage integrated system is mainly based on a direct current channel.

Specifically, photovoltaic power generation, wind power generation, hydropower station power generation and reversible unit power generation are collected into a collection station, and then the collected electric energy is integrated into a power grid in a form of direct current channel transmission.

And judging the operation mode of the reversible unit based on the wind, light and water storage integrated system.

In the embodiment, when the sum of wind power, photovoltaic power and hydropower station power is larger than a direct current power transmission curve, the reversible unit operates in a pumping mode; when the sum of wind power, photovoltaic power and hydropower station power is smaller than or equal to a direct current power transmission curve, the reversible unit operates in a power generation mode to supplement load requirements. The direct current power transmission curve is drawn according to the electricity purchase and sale contract and the spot transaction rule.

And (3) according to the power demand of the receiving end, the power transmission and selling protocol and the spot transaction rule, drawing a direct current power transmission curve.

The data initialization module 82 is connected to the system configuration module 81 and is configured to simulate typical wind and light output data of the area.

In this embodiment, the exemplary wind-solar power output data includes: photovoltaic output data, wind power output data, hydropower station output data and other types of data.

Specifically, the calculation formula of the photovoltaic output data is as follows:

P _w (t)＝K _w (t)P _w

wherein K is _w (t) represents the 8760 output coefficient of the photovoltaic typical output of the region, t=1, 2, 3 …, 8760; p (P) _w Representing the installed capacity of the photovoltaic. The 8760 force coefficient of a typical photovoltaic force is generally simulated from mesoscale data.

The wind power output data calculation formula is as follows:

P _pv (t)＝K _pv (t)P _pv

wherein K is _pv (t) represents 8760 output coefficients of wind power typical output in the region, t=1, 2, 3 … and 8760, and is generally obtained by data simulation of measured data of a anemometer tower; p (P) _pv Representing the installed capacity of the wind power.

The model construction and definition module 83 is used for constructing a mathematical model of the wind, light and water storage integrated operation system based on the wind, light and water storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind, light and water storage integrated system.

In the embodiment, multi-objective optimization is performed based on wind-light-water-storage integrated systems and typical wind-light output data. The objective function of the wind-solar-water-storage integrated delivery capacity optimization configuration optimization mainly comprises the following steps: safe and reliable, economical and efficient, environment-friendly and the like, thereby realizing the process of reasonably and effectively carrying out capacity optimization configuration. Then, selecting a proper parameter estimation method according to the acquired data; and determining constraint conditions and objective functions of a mathematical model of the wind, light and water storage integrated operation system.

Firstly, defining an operation mode of the wind, light and water storage integrated system.

In this embodiment, the components of the hydropower station are: conventional units and reversible units. The power generation capacity of the conventional unit of the hydropower station determines output according to the water inflow, and the output is recorded as follows: p (P) _phes (t). The reversible unit of the hydropower station determines whether the reversible unit is in a pumping or generating running state according to the total sum of wind, light and water output, the residual water-fillable capacity of the reservoir and the power transmission curve of the direct current channel, and the pumping output is recorded as the water-discharging power generation output is recorded as follows: p (P) _h (t)。

Specifically, the operation mode of the wind, light and water storage integrated system comprises the following steps: when the system operates, when the total power of wind, light and water power generation at a certain moment is smaller than or equal to a corresponding value in a power transmission curve of a direct current channel at the moment, the reversible unit operates in a power generation mode, and the water quantity of a reservoir is gradually reduced; when the system operates, when the total power of wind, light and water power generation at a certain moment is larger than a corresponding value in a direct current channel power transmission curve at the moment, the reversible unit operates in a pumping mode, and the water quantity of the reservoir is gradually increased.

In the power generation mode, when the total power of wind-solar water power generation minus direct current power in a certain period is larger than the required water pumping power of the reversible unit, the load is not lost, and the power supply is reliable; when the total power of wind-solar water power generation in a certain period is subtracted by the water pumping power of the reversible type unit, which is less than or equal to the required water pumping power of the reversible type unit, the load is lost, and the power supply is unreliable.

In the pumping mode, if the reservoir capacity is full and the water level is lower than the normal navigation water level, the wind and light are abandoned; and pumping water storage if the reservoir capacity is not full and the water level is greater than or equal to the normal navigation water level.

Then, an objective function is defined. The cost (F1) of the wind, light and water storage integrated operation system mainly based on direct current is comprised of the investment cost of each main body part, the replacement cost of the whole project period and the operation and maintenance cost.

In this embodiment, the objective function involved includes: the system cost is the lowest objective function, the system economic benefit is the largest objective function and the system carbon emission is the smallest objective function.

Specifically, the system cost minimum objective function calculation formula is:

minF1＝min{D _w +D _pv +D _ph }

wherein D is _w Representing the total cost of the wind farm; d (D) _pv Representing the total cost of the photovoltaic power station; d (D) _ph Representing the total cost of the hydropower station; c (C) _w Representing the unit price of wind power unit capacity; c (C) _pv Representing the unit price of photovoltaic unit capacity; c (C) _cap Representing unit price of reservoir capacity; c (C) _power The unit capacity unit price of the reversible water pump turbine unit is shown; n (N) _w Representing the installed capacity of wind power in a wind power plant; n (N) _pv Representing the installed capacity of the photovoltaic; v (V) _cap Representing reservoir capacity; p (P) _power Representing the installed capacity of the reversible hydraulic turbine unit; c (C) _om,w Represented as wind powerRunning and maintaining cost; c (C) _om,pv Representing the photovoltaic operation maintenance cost; c (C) _om,cap Representing the operation and maintenance cost of the reservoir; c (C) _om,power Representing the operation and maintenance cost of the reversible water pump hydroelectric generating set; t (T) _a Representing the life cycle of the upper reservoir; r represents the discount rate, taking 5%.

The basis of the maximum economic benefit of the system is that a wind, light and water storage integrated operation system mainly sent by direct current is taken as a whole, and the annual economic benefit (F2) comprises the power supply benefits of wind, light and water 3 power supplies to a direct current channel. The calculation formula of the maximum objective function of the economic benefit of the system is as follows:

wherein C is _e (t) represents the electric quantity supplied to the direct current channel by the 3 power supplies at the moment t; Δt represents the time interval, taking 1h.

The minimum objective function calculation formula of the system carbon emission is as follows:

wherein F3 represents the annual carbon emission of the system; r is R _w Carbon emission coefficient expressed as wind power over the full life cycle; r is R _pv Carbon emission coefficient expressed as photovoltaic over the full life cycle; r is R _phes Carbon emission coefficient expressed as reversible unit in full life cycle; r is R _cap Expressed as the carbon emission coefficient of the reservoir over the full life cycle. Carbon emission coefficient R of wind power in full life cycle _w Carbon emission coefficient R of photovoltaic in full life cycle _pv Carbon emission coefficient R of reversible unit in full life cycle _phes Carbon emission coefficient R of reservoir in full life cycle _cap All are calculated according to the related data of the annual development report 2021 of the electric power industry in China, and the following steps are respectively tentatively carried out in the embodiment: 0.1664kg/kWh, 0.1664kg/kWh, 0.1664kg/kWh, 0.416kg/kWh.

Next, constraints are defined.

In this embodiment, the constraint condition includes: the system has the advantages of power rejection rate limitation, power supply stability constraint, power supply output constraint, reversible unit pumping power mode constraint and reservoir capacity and water quantity change constraint.

Specifically, firstly, due to randomness and uncertainty of wind energy and solar energy, the power grid has limited capacity of absorbing wind and light, and the phenomenon of discarding wind and light can be caused. Therefore, a scientific and reasonable integrated system operation mode can be adopted by additionally installing the reversible unit, so that the utilization rate of renewable energy sources is effectively improved, and the wind and light abandoning is avoided.

The calculation formula of the system power rejection rate limit is as follows:

P _R (t)＝P _pv (t)+P _w (t)+P _phes (t)-P _L (t)-P _p (t)

wherein P is _R (t) represents the wind-discarding light power at the moment t, wherein the water output is not discarded, and the water is fully discharged according to the water yield; p (P) _pv (t)+P _w (t) represents the total power of wind and light electricity at the moment t; p (P) _L And (t) represents a direct current power transmission curve at the time t. When R is _CR When the load loss rate is 0, the combined operation system can utilize wind and light to the maximum degree in the simulation time, and the load loss rate is defined as less than 5 percent in the application.

For power supply stability, whether LOLP can be met is adopted as an index for evaluating the stability and reliability of power supply of the system to the load end. And the power supply stability constraint calculation formula is as follows:

Wherein, Z (t) =1 represents that the wind-solar-water power generation is smaller than a direct-current channel power transmission curve; and Z (t) =1 represents a power transmission curve of the direct current channel and the wind-solar water power generation is larger than or equal to the power transmission curve of the direct current channel.

The power supply output constraint formula is as follows:

0≤P _w,all ≤P _w,max ,0≤P _pv,all ≤P _pv,max

0≤P _power,all ≤P _power,max

wherein P is _w,all Representing the output power of the wind farm; p (P) _pv,all Representing the output power of the photovoltaic power plant; p (P) _w,max Representing an upper limit of output power of the wind farm; p (P) _pv,max Representing an upper output power limit of the photovoltaic power plant; p (P) _power,all And the upper limit of the output power of the reversible water pump hydroelectric generating set in the hydropower station is indicated.

The constraint formula of the pumping electric mode of the reversible unit is as follows:

wherein P is _p (t) is a non-negative number; p (P) _h (t) is a non-negative number; therefore, both of each period must have a term of 0 to meet constraint requirements.

At any time, the water quantity of the limited reservoir cannot exceed the set capacity value, and the water quantity of the upper reservoir changes by 0 in any day.

The reservoir capacity and water quantity change constraint formula is as follows:

W _cap,min (t)≤W(t)≤W _cap,max (t)

V _cap,min (t)≤V _cap (t)≤V _cap,max (t)

wherein W is _cap,min Representing the guaranteed output of the reservoir; w (W) _cap,max (t) represents the expected force of the reservoir; v (V) _cap,min (t) represents the reservoir capacity under the guaranteed output condition of reservoir capacity; v (V) _cap,max And (t) represents the reservoir capacity under the expected force conditions of the reservoir capacity.

The calculation module 84 is configured to solve a Pareto set for the mathematical model of the wind-solar-water-storage integrated operation system using a multi-objective genetic algorithm based on the typical wind-solar output data.

In the method, factors such as economy, stability, environmental protection and the like are considered, and a multi-objective optimization function is adopted to solve an optimal problem; and then, evaluating the merits of each scheme by adopting a gray correlation method.

Initializing parameters of a multi-objective genetic algorithm. The multi-objective genetic algorithm parameters include: population N size, number of iterations, probability of crossover and mutation, etc.

In this embodiment, first, a coding mode needs to be defined to convert the decision variable into a binary string or other form that can be processed by the genetic algorithm. And then, calculating the fitness value of each population, namely the objective function value, based on a mathematical model of the wind-light-water-storage integrated operation system, and randomly generating a group of solutions serving as an initial population.

And carrying out non-dominant sorting and crowding degree calculation based on the initialized multi-objective genetic algorithm parameters to generate a first generation population.

In multi-objective optimization, non-dominant ranking is to rank solutions in a population according to a dominant relationship, where dominant relationship means that one solution is not inferior to another solution on all objectives.

In this embodiment, solutions in the population may be layered by non-dominant ordering, progressively progressing from inferior to superior. Meanwhile, in order to preserve excellent solutions, it is necessary to calculate the degree of congestion of each solution, i.e., the distance between the solution and the adjacent solution. Solutions with less crowding are more likely to be selected as parents of the next generation.

And performing selection operation, crossover operation and mutation operation on the primary population.

In this embodiment, two solutions are selected from the current population to be interleaved and mutated to produce a new solution. In this process, crossover probabilities and mutation probabilities are used to control the generation of new solutions.

And merging the operated population with the primary population to obtain a first population.

In this embodiment, the offspring of the new generation are combined with the parent to form a new population. And during merging, elite strategy can be adopted, namely, excellent solutions in the parent are reserved, so that the parent can directly enter a new generation population.

The analysis module 85 is configured to make a decision on the Pareto set by using a gray correlation analysis method, so as to obtain a capacity configuration scheme with the maximum gray correlation gamma value by using a policy in unbiased middle, thereby meeting the optimal capacity matching of an objective function and constraint conditions.

Preprocessing is performed based on the data in the Pareto set.

In this embodiment, the data in each Pareto solution set is initialized, so that the data meets the data requirement of the gray correlation analysis method.

And determining a reference sequence based on the preprocessed solution Pareto sets, and calculating gray association degree of the solution in each Pareto set and the reference sequence. The reference number sequence is an optimal number sequence reflecting the association relationship between the systems or factors.

In this embodiment, a solution in the best Pareto solution set is selected as a reference sequence; for each solution in the Pareto solution set, it is necessary to calculate its gray correlation with the reference number columns. The calculation formula of the gray correlation degree can be selected according to specific situations, and generally comprises the steps of data preprocessing, correlation coefficient calculation, correlation degree calculation and the like.

And averaging all the gray correlation degrees, and selecting the solution with the maximum average value as the optimal solution. And analyzing the found optimal solution, and identifying output data with the greatest influence on the result in all the typical wind-solar output data.

In this embodiment, an unbiased compromise policy is adopted to average all gray correlation degrees, and a solution with the maximum average value is selected as an optimal solution.

If the resulting solution set is not optimal, the algorithm needs to be optimized and adjusted. For example: changing algorithm parameters, improving models or reselecting reference arrays. Then, the result is outputted. The end result is a set of capacity allocation schemes that meet all constraints and have the highest gray correlation.

Therefore, by analyzing 3 objective functions of the system cost F1, the annual economic benefit F2, and the annual carbon emission F3 as influence factors of GRA, in order to balance the influence of the 3 objective function values on the capacity allocation scheme selection, the weight ω of each influence factor is set to 0.333, and the number of the system capacity allocation schemes to be decided is N. And adopting a gray correlation analysis (GRA) method to make a decision on the obtained Pareto solution set, and further realizing a capacity configuration scheme with maximum gray correlation gamma value obtained by a policy in unbiased middle.

The wind-solar-water-storage integrated capacity optimization configuration model based on the genetic algorithm is used for constructing a wind-solar-water-storage integrated capacity optimization configuration system based on the genetic algorithm, so that reasonable and effective capacity optimization configuration can be realized according to a power transmission curve of a direct current channel under the conditions of economy, reliability and carbon emission, and a green power system with excellent competitiveness, environmental protection, low carbon and high utilization efficiency is constructed.

It should be noted that, it should be understood that the division of the modules of the above system is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the x module may be a processing element that is set up separately, may be implemented in a chip of the system, or may be stored in a memory of the system in the form of program code, and the function of the x module may be called and executed by a processing element of the system. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more microprocessors (digital signal processor, abbreviated as DSP), or one or more field programmable gate arrays (FieldProgrammable Gate Array, abbreviated as FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

In summary, the wind-solar-water-storage integrated capacity optimization configuration method and system based on the genetic algorithm have the following beneficial effects:

according to the wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm, the problem of multiple constraints and multiple targets in engineering is solved by applying a mathematical model method; the optimal capacity ratio of the system cost, the annual economic benefit and the annual carbon emission multi-objective function under the constraint condition can be comprehensively considered, a capacity optimization configuration model is solved through a mature genetic algorithm based on a mathematical modeling method, an accurate wind, light and water capacity configuration Pareto solution set is solved, and the optimal capacity configuration is selected by adopting a gray correlation analysis (GRA) according to the weight value of each objective function. The method is suitable for the hydropower station large-scale base mainly based on direct current delivery, under the condition that wind and light resources are good, the utilization rate of a channel is improved by additionally installing the reversible unit in the hydropower station, the matching degree of a power supply and a load is improved, and clean energy with high stability, reliability and adjustment capability is provided.

The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.

Claims (9)

1. The wind-solar-water-storage integrated capacity optimization configuration method based on the genetic algorithm is characterized by comprising the following steps of:

constructing a wind, light and water storage integrated system;

simulating typical wind-light output data of a region;

constructing a mathematical model of the wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system;

based on the typical wind-light output data, solving a Pareto set by adopting a multi-target genetic algorithm to the mathematical model of the wind-light-water storage integrated operation system;

and adopting a gray correlation analysis method to make a decision on the Pareto set so as to realize that the capacity configuration scheme with the maximum gray correlation gamma value is acquired by the strategy in the unbiased state, thereby meeting the optimal capacity ratio of the objective function and the constraint condition.

2. The genetic algorithm-based wind-solar-water-storage integrated capacity optimization configuration method of claim 1, wherein constructing the wind-solar-water-storage integrated system comprises the following steps:

constructing a wind, light and water storage integrated system; the wind, light and water storage integrated system comprises: wind power plants, photovoltaic power stations, hydropower stations and collection stations; wherein, the power plant includes: a conventional unit and a reversible unit;

judging the operation mode of the reversible unit based on the wind, light and water storage integrated system; comprising the following steps:

when the sum of wind power, photovoltaic power and hydropower station power is larger than a direct current power transmission curve, the reversible unit operates in a pumping mode; when the sum of wind power, photovoltaic power and hydropower station power is smaller than or equal to a direct current power transmission curve, the reversible unit operates in a power generation mode.

3. The genetic algorithm-based wind-solar-water-storage integrated capacity optimization configuration method according to claim 1, wherein the typical wind-solar-power output data comprises: photovoltaic output data, wind power output data and hydropower station output data;

the calculation formula of the photovoltaic output data is as follows:

P _w (t)＝K _w (t)P _w

wherein K is _w (t) represents the 8760 output coefficient of the photovoltaic typical output of the region, t=1, 2, 3 …, 8760; p (P) _w Representing the installed capacity of the photovoltaic;

the wind power output data calculation formula is as follows:

P _pv (t)＝K _pv (t)P _pv

wherein K is _pv (t) represents the 8760 output coefficient of the wind power typical output of the region, t=1, 2, 3 …, 8760; p (P) _pv Representing the installed capacity of the wind power.

4. The genetic algorithm-based wind, light and water storage integrated capacity optimization configuration method according to claim 1, wherein defining the operation mode of the wind, light and water storage integrated system comprises:

when the system operates, when the total power of wind, light and water power generation at a certain moment is smaller than or equal to a corresponding value in a power transmission curve of a direct current channel at the moment, the reversible unit operates in a power generation mode, and the water quantity of a reservoir is gradually reduced;

when the system operates, when the total power of wind, light and water power generation at a certain moment is larger than a corresponding value in a direct current channel power transmission curve at the moment, the reversible unit operates in a pumping mode, and the water quantity of the reservoir is gradually increased;

wherein:

in the power generation mode, when the total power deduction direct current power generated by wind, light and water in a certain period is larger than the required water pumping power of the reversible unit, the load is not lost, and the power supply is reliable; when the total power of wind-solar water power generation in a certain period is deducted by the water pumping power of the reversible unit required by direct current power transmission to be less than or equal to the required water pumping power, the load is lost, and the power supply is unreliable;

In the pumping mode, if the reservoir capacity is full and the water level is lower than the normal navigation water level, the wind and light are abandoned; and pumping water storage if the reservoir capacity is not full and the water level is greater than or equal to the normal navigation water level.

5. The genetic algorithm-based wind, light and water storage integrated capacity optimization configuration method according to claim 1, wherein defining the objective function comprises: a system cost minimum objective function, a system economic benefit maximum objective function and a system carbon emission minimum objective function;

the calculation formula of the lowest cost objective function of the system is as follows:

minF1＝min{D _w +D _pv +D _ph }

wherein D is _w Representing the total cost of the wind farm; d (D) _pv Representing the total cost of the photovoltaic power station; d (D) _ph Representing the total cost of the hydropower station; c (C) _w Representing the unit price of wind power unit capacity; c (C) _pv Representing the unit price of photovoltaic unit capacity; c (C) _cap Representing unit price of reservoir capacity;

C _power the unit capacity unit price of the reversible water pump turbine unit is shown; n (N) _w Representing the installed capacity of wind power in a wind power plant; n (N) _pv Representing the installed capacity of the photovoltaic; v (V) _cap Representing reservoir capacity; p (P) _power Representing the installed capacity of the reversible hydraulic turbine unit; c (C) _om,w Expressed as wind power operation maintenance cost; c (C) _om,pv Representing the photovoltaic operation maintenance cost; c (C) _om,cap Indicating that the reservoir is operated and maintainedThe cost is high; c (C) _om,power Representing the operation and maintenance cost of the reversible water pump hydroelectric generating set; t (T) _a Representing the life cycle of the upper reservoir; r represents the discount rate;

the calculation formula of the maximum objective function of the economic benefit of the system is as follows:

wherein C is _e (t) represents the electric quantity supplied to the direct current channel by the 3 power supplies at the moment t; Δt represents a time interval;

the minimum objective function calculation formula of the system carbon emission is as follows:

wherein F3 represents the annual carbon emission of the system; r is R _w Carbon emission coefficient expressed as wind power over the full life cycle; r is R _pv Carbon emission coefficient expressed as photovoltaic over the full life cycle; r is R _phes Carbon emission coefficient expressed as reversible unit in full life cycle; r is R _cap Expressed as the carbon emission coefficient of the reservoir over the full life cycle.

6. The genetic algorithm-based wind, light and water storage integrated capacity optimization configuration method according to claim 1, wherein the constraint conditions comprise: the system is limited by the power rejection rate, the power supply stability, the power supply output and the reversible unit pumping power mode, and the reservoir capacity and water quantity change;

the calculation formula of the system power rejection rate limit is as follows:

P _R (t)＝P _pv (t)+P _w (t)+P _phes (t)-P _L (t)-P _p (t)

wherein P is _R (t) represents the wind-discarding and light-discarding power at time t; p (P) _pv (t)+P _w (t) represents the total power of wind and light electricity at the moment t; p (P) _L (t) represents a direct current power transmission curve at time t;

the power supply stability constraint calculation formula is as follows:

Wherein, Z (t) =1 represents that the wind-solar-water power generation is smaller than a direct-current channel power transmission curve; z (t) =1 represents that the wind-solar water power generation is greater than or equal to a direct current channel power transmission curve;

the power supply output constraint formula is as follows:

0≤P _w,all ≤P _w,max ,0≤P _pv,all ≤P _pv,max

0≤P _power,all ≤P _power,max

wherein P is _w,all Representing the output power of the wind farm; p (P) _pv,all Representing the output power of the photovoltaic power plant; p (P) _w,max Representing an upper limit of output power of the wind farm; p (P) _pv,max Representing an upper output power limit of the photovoltaic power plant; p (P) _power,all The upper limit of the output power of a reversible water pump hydroelectric generating set in a hydropower station is represented;

the constraint formula of the pumping electric mode of the reversible unit is as follows:

wherein P is _p (t) is a non-negative number; p (P) _h (t) is a non-negative number;

the reservoir capacity and water quantity change constraint formula is as follows:

W _cap,min (t)≤W(t)≤W _cap,max (t)

V _cap,min (t)≤V _cap (t)≤V _cap,max (t)

wherein W is _cap,min Representing the guaranteed output of the reservoir; w (W) _cap,max (t) represents the expected force of the reservoir; v (V) _cap,min (t) represents the reservoir capacity under the guaranteed output condition of reservoir capacity; v (V) _cap,max And (t) represents the reservoir capacity under the expected force conditions of the reservoir capacity.

7. The genetic algorithm-based wind-solar-water-storage integrated capacity optimization configuration method according to claim 1, wherein solving the Pareto set on the mathematical model of the wind-solar-water-storage integrated operation system by adopting a multi-objective genetic algorithm based on the typical wind-solar output data comprises the following steps:

Initializing parameters of a multi-target genetic algorithm; the multi-objective genetic algorithm parameters include: population size, number of iterations, probability of crossover and mutation operations;

non-dominant sorting and crowding degree calculation are carried out based on the initialized multi-target genetic algorithm parameters, and a primary population is generated;

performing selection operation, crossover operation and mutation operation on the primary population;

combining the operated population with the primary population to obtain a first population;

then, non-dominant sorting and crowding degree calculation are carried out on the new population to obtain a second population;

and sequentially processing the population until the current algebra is equal to the preset algebra, thereby obtaining a solving Pareto set.

8. The genetic algorithm-based wind, light and water storage integrated capacity optimization configuration method according to claim 1, wherein the Pareto set is decided by adopting a gray correlation analysis method so as to realize a capacity configuration scheme with maximum gray correlation gamma value acquired by a policy in unbiased state, and the method comprises the following steps:

preprocessing based on the data in the solving Pareto set;

determining a reference sequence based on the preprocessed solution Pareto sets, and calculating gray correlation degree between the solution in each Pareto set and the reference sequence;

Averaging all the gray correlation degrees, and selecting the solution with the maximum average value as the optimal solution;

and analyzing the found optimal solution, and identifying output data with the greatest influence on the result in all the typical wind-solar output data.

9. A genetic algorithm-based wind, light and water storage integrated capacity optimization configuration system is characterized by comprising:

the system forming module is used for constructing a wind, light and water storage integrated system;

the data initialization module is used for simulating typical wind-light output data of the area;

the mathematical model construction module is used for constructing a mathematical model of the wind-light-water-storage integrated operation system based on the wind-light-water-storage integrated system, and defining an operation mode, an objective function and constraint conditions of the wind-light-water-storage integrated system;

the calculation module is used for solving a Pareto set on the mathematical model of the wind-light-water-storage integrated operation system by adopting a multi-objective genetic algorithm based on the typical wind-light output data;

and the decision module is used for deciding the Pareto set by adopting a gray correlation analysis method so as to realize that the capacity configuration scheme with the maximum gray correlation gamma value is acquired by a strategy in unbiased deflection, thereby meeting the optimal capacity ratio of an objective function and constraint conditions.