CN113361715A - Wind-storage hybrid power station daily operation strategy optimization method based on genetic algorithm - Google Patents

Abstract

The invention relates to the technical field of wind power integration, in particular to a daily operation strategy optimization method of a wind-storage hybrid power station based on a genetic algorithm. The method comprises the following steps: s1: given relevant parameters, including: real-time electricity price, output deviation punishment electricity price, maximum allowable probability of output deviation of the wind-storage hybrid power station, capacity of a battery energy storage system, initial charge state and maximum charge-discharge power, wind power installed capacity, wind power predicted value and scheduling time period number; s2: establishing a daily operation strategy optimization model of the wind-storage hybrid power station, wherein optimization variables are a planned charging and discharging sequence and power of a battery energy storage system within a scheduling day, and an optimization target is that daily operation income of the wind-storage hybrid power station is maximum; s3: on the basis of Monte Carlo simulation of daily operation conditions of the wind-storage hybrid power station, a genetic algorithm is adopted to solve a daily operation strategy optimization model of the wind-storage hybrid power station, and an optimal planned charging and discharging sequence and power of a battery energy storage system in the wind-storage hybrid power station are given.

Description

Wind-storage hybrid power station daily operation strategy optimization method based on genetic algorithm

Technical Field

The invention relates to the technical field of wind power integration, in particular to a daily operation strategy optimization method of a wind-storage hybrid power station based on a genetic algorithm.

Background

In recent years, wind power generation has been rapidly developed worldwide due to the urgent need for depletion of fossil energy and emission reduction of carbon dioxide. By 9 months in 2019, the installed capacity of wind power in China reaches 1.98 hundred million kilowatts, which accounts for 36 percent of the total installed capacity of wind power in the world and is the first in the world. At present, the key point of wind power development in China is shifting from the 'three north' region with rich wind energy resources to the middle east and south regions with wide distribution, flexible application, large power demand and good absorption capacity of land wind energy resources. Meanwhile, the development and construction of offshore wind power also open up a new space for the growth of wind power. With the continuous improvement of the wind-electricity penetration level, the traditional generator set cannot completely balance the random intermittent characteristic of wind power, so that the frequency modulation and peak regulation of a power grid are extremely difficult, and even the safe and stable operation of the power grid is threatened.

The rapid development of the battery energy storage technology provides a brand-new solution for solving a series of problems caused by large-scale wind power integration. The battery energy storage system has the advantages of high response speed, high charging and discharging efficiency and flexible configuration, can be independently accessed into the system and independently operated as an energy storage power station, and can also be configured in a wind power plant to form wind-storage hybrid power station combined operation. For a wind-storage hybrid power station, optimizing the operation strategy is the key for improving the operation benefit.

In the literature, "comparative analysis of energy storage operation strategies for wind power stabilization" (high voltage technology, 2019, volume 45, phase 9, page 2797-2805), 3 wind-storage hybrid power station operation strategies for wind power fluctuation stabilization are proposed, and comparative analysis is performed on wind power fluctuation stabilization effects and stabilization costs corresponding to various operation strategies. The comparison shows that the battery energy storage system is operated in groups, and the synchronous charge-discharge state switching of the charge-discharge states of the two groups of batteries is the optimal strategy in the 3 operation strategies. However, the 3 operation strategies mentioned in this document are all preset strategies, and the operation strategies are not optimized. Document two, Scheduling wind-based energy storage hybrid systems in time-of-use sharing schemes (IET Generation, transmission distribution, volume 12 in 2018, phase 20, pages 4435 to 4442), proposes a wind-storage hybrid power station operation strategy based on time-of-use electricity price, and improves the wind-storage hybrid power station yield on the premise of meeting the wind-power grid-connection technical requirements. Firstly, a charging and discharging plan of the battery energy storage system in a dispatching day is given according to a time-of-use electricity price rule; secondly, simulating the operation condition of the Feng storage hybrid power station by adopting a Monte Carlo simulation technology; and finally, iteratively updating the charge-discharge power plan of the battery energy storage system according to the simulation result. The method needs to give a charge and discharge plan of the battery energy storage system in advance within a scheduling day, and has certain limitation.

In order to fully exert the benefits of the battery energy storage system and improve the income of the wind-storage hybrid power station on the premise of meeting the grid-connected technical requirements of the wind power station, it is necessary to optimize the daily planned charging and discharging sequence and power of the battery energy storage system in the wind-storage hybrid power station. However, the prior art method cannot simultaneously optimize the planned charging and discharging sequence and power of the battery energy storage system in the wind-storage hybrid power station, and has certain limitations.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a method for optimizing the daily operation strategy of a wind-storage hybrid power station based on a genetic algorithm, which improves the operation income of the wind-storage hybrid power station on the premise of meeting the grid-connected technical requirement of a wind power plant by optimizing the planned charging and discharging sequence and power of a battery energy storage system in the wind-storage hybrid power station.

In order to achieve the purpose, the invention adopts the following technical scheme: a wind-storage hybrid power station daily operation strategy optimization method based on a genetic algorithm comprises the following steps:

s1: given relevant parameters, including: real-time electricity price, output deviation punishment electricity price, maximum allowable probability of output deviation of a wind-storage hybrid power station, capacity of a battery energy storage system, initial charge state and maximum charge-discharge power, wind power installed capacity, wind power predicted value and scheduling time period number;

s2: establishing a daily operation strategy optimization model of the wind-storage hybrid power station, wherein optimization variables are a planned charging and discharging sequence and power of a battery energy storage system within a scheduling day, and an optimization target is that daily operation income of the wind-storage hybrid power station is maximum;

s3: on the basis of Monte Carlo simulation of daily operation conditions of the wind-storage hybrid power station, a genetic algorithm is adopted to solve a daily operation strategy optimization model of the wind-storage hybrid power station, and an optimal planned charging and discharging sequence and power of a battery energy storage system in the wind-storage hybrid power station are given.

As a preferred technical scheme of the invention: for a wind-storage hybrid power station, the key for improving daily operation income is to make a reasonable daily output plan on the basis of wind power prediction; based on the above, the invention provides a short-term operation strategy optimization model of a wind-storage hybrid power station, and the optimization goal is that the daily operation yield of the wind-storage hybrid power station is maximum, which is specifically shown in formula (1):

in equation (1), t is a scheduling period index, (t ═ 1,2, …, m); m is the number of scheduling time segments; f is wind-reservoirThe daily operating income of the hybrid power station; f_w,tFor the electricity sales revenue of the wind-storage hybrid power station during the scheduling period t, F_dev,tPunishing cost for output deviation of the wind-storage hybrid power station in a scheduling time period t; f_bessThe operating cost of the battery energy storage system in a scheduling day;

in the formula (1), the electricity selling income F of the wind-storage hybrid power station in the dispatching time t_w,tSpecifically, as shown in formula (2):

F_w,t＝ρ_t×(P_b,t+P_w,t)×ΔT (2)

in the formula (2), ρ_tReal-time electricity prices for a scheduling period t; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; p_w,tActual wind power for a scheduling period t; Δ T is the scheduling period length;

in formula (1), the wind-storage hybrid power station outputs deviation punishment cost F in the scheduling period t_dev,tSpecifically, as shown in formula (3):

F_dev,t＝ρ_dev,t×|P_b,t+P_w,t-P_s,t|×ΔT (3)

in the formula (3), ρ_dev,tPunishment of electricity price for output deviation of a scheduling time period t; p_s,tFor the planned output of the wind-storage hybrid power station in the scheduling time period t, as shown in formula (4):

P_s,t＝P_wf,t+P_bs,t (4)

in the formula (4), P_s,tReporting the planned output of the wind-storage hybrid power station in a scheduling time t to a scheduling center by a wind-storage hybrid power station operator one day in advance; p_wf,tA wind power predicted value of a scheduling time period t; p_bs,tThe planned charging and discharging power of the battery energy storage system in the scheduling time t is provided, a positive value represents the discharging of the battery energy storage system, and a negative value represents the charging of the battery energy storage system;

in the formula (1), F_bessThe running cost of the battery energy storage system in the dispatching day, the investment cost of the battery energy storage system and the number of charging and discharging cycles experienced in the dispatching dayThe number is estimated as shown in equation (5):

in the formula (5), V_totalInvestment cost for a battery energy storage system; n is_totalCycle life of the battery energy storage system; u. of_ch,t、u_dis,tRespectively representing binary variables, u, for switching the charging and discharging states of the battery energy storage system_ch,tTaking 1 indicates that the battery energy storage system is switched from a discharge state to a charge state in a scheduling time t, and u_dis,tTaking 1 to indicate that the battery energy storage system is switched from a charging state to a discharging state in a scheduling time t;

the constraints in the wind-storage hybrid power station daily operation strategy optimization model are as follows:

output deviation probability constraint of the wind-storage hybrid power station:

P_r{|P_b,t+P_w,t-P_s,t|＞P_set}≤p (6)

in the formula (6), P_setThe maximum allowable deviation of the generated output of the wind-storage hybrid power station is obtained; p_rThe power generation output deviation out-of-limit probability of the wind-storage hybrid power station is shown on the left side of the inequality; p is the probability that the output deviation of the wind-storage hybrid power station exceeds the maximum allowable deviation;

and (3) restricting the charge and discharge rate of the battery energy storage system:

-P_ch,m≤P_b,t≤P_dis,m (7)

in the formula (7), P_ch,mAnd P_dis,mRespectively setting the maximum charging and discharging rates of the battery energy storage system;

and (3) constraint of the shortest charging and discharging time of the battery energy storage system:

T_ch≥T_ch,m (8)

T_dis≥T_dis,m (9)

in the formulae (8) and (9), T_chAnd T_disRespectively the charging and discharging duration time, T, of the battery energy storage system_ch,mAnd T_dis,mThe minimum charging and discharging duration of the battery energy storage system can be respectively expressed by formulas (10) and (11);

in the formulae (10), (11), E_cThe capacity of the battery energy storage system; e_maxAnd E_minRespectively the maximum and minimum state of charge allowed by the battery energy storage system; eta_chAnd η_disRespectively charging and discharging efficiencies of the battery energy storage system;

and (3) restraining the state of charge of the battery energy storage system:

E_min≤E_soc,t≤E_max (12)

in the formula (12), E_soc,tFor the state of charge of the battery energy storage system in the scheduling period t, the calculation can be performed according to the formula (13):

in the formula (13), E_cThe capacity of the battery energy storage system; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; Δ T is the scheduling period length; eta_chAnd η_disRespectively the charging and discharging efficiency of the battery energy storage system.

As a preferred technical scheme of the invention: step S3, solving the wind-storage hybrid power station daily operation strategy optimization model, which comprises the following steps:

s3.1: setting genetic algorithm parameters, including: population size N_popCross rate P_cThe rate of variation P_mAnd maximum evolution algebra G_max；

S3.2: random generation of N_popAn initial population of chromosome bars; the chromosome in the initial population consists of m code bits, and the value of the code bit i is '1', which indicates that the battery energy storage system is in a discharging state in the ith scheduling period; the value is "-1", which indicates that the battery energy storage system is in a discharge state in the ith scheduling period (i is 1,2, …, m);

s3.3: the evolution algebra index g is initialized to 0, namely, g is 0;

s3.4: calculating the fitness of the g-th generation of chromosomes by using g as g +1, and initializing a chromosome index k to be 1, namely using k as 1;

s3.5: decoding the kth chromosome in the current population, and determining the charge-discharge state of the battery energy storage system within a scheduling day;

if the battery energy storage system does not meet the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the fitness V of the kth chromosome according to the formula (14)_fit,kAnd jumping to step S3.8;

V_fit,k＝0 (14)

if the battery energy storage system meets the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time period according to the charging and discharging states of the battery energy storage system in the scheduling day, and continuing to execute the step S3.6;

s3.6: simulating the operation condition of the wind-storage hybrid power station within the dispatching day by adopting a Monte Carlo simulation method, and iteratively updating the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system;

s3.7: simulating the operation condition of the wind-storage hybrid power station in a dispatching day according to the optimal planned charging and discharging power of the battery energy storage system, calculating the out-of-limit probability of the daily operation income and the power generation output deviation of the wind-storage hybrid power station, and calculating the fitness V of the kth chromosome according to a formula (15)_fit,k：

V_fit,k＝F-β_pen×max{P_r{|P_b,t+P_w,t-P_s,t|＞P_set}-p,0} (15)

In the formula (15), β_penIs a pre-given penalty factor;

s3.8: judging whether fitness calculation of all chromosomes in the current population is finished or not, namely judging whether k is equal to N or not_pop(ii) a If k is<N_popIf yes, let k be k +1, and go to step S3.5; otherwise, continuing to execute the next step S3.9;

s3.9: judging whether the maximum evolution algebra is reached, namely judging whether the index G of the evolution algebra is equal to G_max(ii) a If G is G_maxIf yes, continuing to execute the step 3.10; otherwise, based on fitness, carrying out copy, crossover and variation operations on the current population, updating the chromosome population, and skipping to the step S3.4;

s3.10: and outputting the wind-storage hybrid power station operation scheme corresponding to the chromosome with the highest chromosome fitness in the current population as an optimal scheme, and ending the algorithm flow.

As a preferred technical scheme of the invention: the chromosome population initialization step of step S3.2 is specifically as follows:

s3.2.1: randomly determining the initial charging and discharging states of the battery energy storage system, and assuming that the initial state of the battery energy storage system is a discharging state for convenience of description, namely the value of a first binary code bit in a chromosome is '1'; the index n is initialized to 1, namely n is 1;

s3.2.2: randomly generating an interval [ T ]_dis,m,m]Integer T obeying uniform distribution_nThe 1 st to the T th code bit of the chromosome_nAll code bits are initialized to be 1, namely from scheduling period 1 to scheduling period T_nThe battery energy storage systems are all in a discharging state;

s3.2.3: if the remaining length of the chromosome

Less than the shortest charging time T of the battery energy storage system_ch,mInitializing all the residual code bits of the chromosome to be '-1', and jumping to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1The T th of chromosome_n+1 to the first

The code bits all take the value of "-1", i.e. from the scheduling period T_n+1 to

The battery energy storage systems are all in a charging state; let n be n + 1; s3.2.4: if the remaining length of the chromosome

Less than the shortest discharge time T of the battery energy storage system_dis,mThen the remaining code bits of the chromosome are initialized to be 1, and the process jumps to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1Then the T-th chromosome_n+1 to the first

The code bits all take the value of '1', namely from the scheduling period T_n+1 to

The battery energy storage systems are all in a discharging state; let n be n +1, S3.2.3 is performed;

s3.2.5: the above steps are repeated until the entire initial chromosome population is produced.

As a preferred technical scheme of the invention: s3.5, decoding the kth chromosome in the current population to determine the battery energy storage systemInitial planned charging and discharging power P in each scheduling period_bs,tThe steps are as follows:

s3.5.1: decoding the kth chromosome in the current population to determine the charge-discharge state of the battery energy storage system at each scheduling period;

s3.5.2: calculating the number of the continuous charging and discharging state periods of the battery energy storage system in a scheduling day; suppose that the battery energy storage system undergoes J continuous charging and discharging periods in a scheduling day, and the jth continuous charging and discharging period is T_c,jEach scheduling period is composed of an initial scheduling period index of t_st,jThe index of the final scheduling period is t_end,j(j＝1,2…,J)；

S3.5.3: sequentially calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time interval in J continuous charging and discharging time intervals; if the jth continuous charging and discharging time interval is a charging time interval, the initial planned charging power of each scheduling time interval is as follows:

if the jth continuous charging and discharging period is a discharging period, the initial planned discharging power of each scheduling period is as follows:

in the formulae (15), (16), E_st,jFor the initial state of charge of the battery energy storage system in the jth continuous charging and discharging period, if j is 1, then E_st,jThe initial state of charge of the battery energy storage system on the whole scheduling day; if j>1, and the jth continuous charging and discharging time interval is a charging time interval, then E_st,j＝E_min(ii) a If j>1, and the jth continuous charging and discharging time interval is the discharging time interval, then E_st,j＝E_max。

As a preferred technical scheme of the invention: s3.6, simulating the operation condition of the wind-storage hybrid power station within the dispatching day by adopting a Monte Carlo simulation method, and iteratively updating the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system, wherein the method comprises the following steps:

s3.6.1: the method comprises the following steps of initializing each evaluation index for measuring the real-time technology and the economic performance of the wind-storage hybrid power station to zero, namely:

in formula (17), M_PPST,t、M_EPDP,tAnd M_EESI,tRespectively taking values of a power generation plan tracking probability PPST, an injection power deviation punishment expected EPDP and an electric energy sales income expected EESI of the wind-storage hybrid power station, wherein the three real-time evaluation indexes are values in a scheduling time period t;

s3.6.2: initializing a scheduling period index t to 1; according to the charging and discharging sequence of the battery energy storage system determined by chromosome decoding, obtaining the working state of the battery energy storage system at an initial period; if the battery energy storage system is in a charging state in the initial period, setting the initial charging state as E_min(ii) a If the battery energy storage system is in a discharging state in the initial period, setting the initial charge state of the battery energy storage system as E_max；

S3.6.3: and determining values of three shape parameters alpha, beta and gamma in Versatile distribution according to the wind work predicted value of the time period t. In the invention, Versatile distribution is adopted to describe the random fluctuation characteristic of the actual wind power near the predicted value, and the probability density function f (x) and the cumulative probability distribution function F (x) of the distribution are respectively shown as follows:

f(x)＝αβexp[-α(x-γ)]/{1+exp[-α(x-γ)]}^β+1 (18)

F(x)＝{1+exp[-α(x-γ)]}^-β (19)

the wind power fluctuation characteristic is closely related to the output level thereof, so that when the output level is different, the shape parameters alpha, beta and gamma in Versatile distribution are different in value;

s3.6.4: generating random wind power obeying a Versatile distribution, namely:

in the formula (20), P_sw,tRandom wind power for a scheduling period t; g_windThe installed capacity of the wind power plant; function F^-1(. is the inverse of the cumulative probability distribution function for a Versatile distribution; c is the interval [0,1]Random numbers uniformly distributed throughout the interior;

s3.6.5: the random wind power P generated in step S3.6.4_sw,tDetermining the real-time charging and discharging power P of the battery energy storage system in the current time period according to the formula (21) as the real-time wind power_b,tDetermining the electricity selling income F according to the formulas (2) and (3) respectively_w,tPenalty cost with output deviation F_dev,t；

S3.6.6: updating the values of the indexes EPDP and EESI in the scheduling time period t according to the calculation result of the step S3.6.5; as shown in the following formula:

in the formula (22), n_simSimulating the number of Monte Carlo times given in advance;

as for the index PPST, if the wind-storage hybrid plant can generate power at the planned output during this period, the index is updated as follows:

s3.6.7: calculating the state of charge E of the battery energy storage system in the current time period according to the formula (13)_soc,t；

S3.6.8: let t be t +1, repeatedly carry out S3.6.3-S3.6.7 until t be m;

s3.6.9: repeatedly executeS3.6.2 to S3.6.8, until reaching the preset maximum Monte Carlo simulation times n_sim；

S3.6.10: if the power generation plan tracking probability M of the wind-storage hybrid power station in all time periods_PPST,tSatisfying the power grid dispatching requirement, namely satisfying the formula (6) to give constraint, and ending the simulation; if the power grid dispatching instruction is not met in certain time intervals, the planned charging and discharging power of the battery energy storage system is set to be overlarge in the time intervals, and at the moment, the planned charging and discharging power in the time intervals can be corrected according to the following formula:

in the formula (24), P'_bs,tThe planned charging and discharging power of the battery energy storage system after the time period t is corrected; gamma is a correction coefficient established in advance; omega_bsA set of scheduling periods which do not meet the scheduling requirements of the power grid; when the planned charging and discharging power of the time interval which does not meet the scheduling requirement is corrected, attention needs to be paid to the value range of the planned charging and discharging power; if the battery energy storage system is in a charging state, the planned charging and discharging power value range is [ -P ]_ch,mE_c,0]On the contrary, if the battery energy storage system is in the discharging state, the planned charging and discharging power value range is [0, P ]_dis,mE_c](ii) a After the planned charge/discharge power is corrected, S3.6.1 is skipped to continue the simulation.

Compared with the prior art, the wind-storage hybrid power station daily operation strategy optimization method based on the genetic algorithm has the following technical effects:

1. the benefits of a battery energy storage system are fully exerted, and the income of a wind-storage hybrid power station is improved;

2. the technical requirement of wind power plant grid connection is met, and the adverse effect of large-scale wind power grid connection on the operation of a power grid is relieved.

Drawings

FIG. 1 is a schematic flow chart of the method proposed by the present invention;

FIG. 2 is a flow chart of a wind-storage hybrid power station daily operation strategy optimization model solution based on a genetic algorithm;

fig. 3 is a flow of performing simulation and planning charge-discharge power iterative optimization on daily operation conditions of a wind-storage hybrid power station based on a monte carlo simulation method.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

The invention provides a method for optimizing a daily operation strategy of a wind-storage hybrid power station based on a genetic algorithm, which is divided into 3 steps shown in figure 1 and specifically comprises the following steps:

s1: given relevant parameters, including: real-time electricity price, output deviation punishment electricity price, maximum allowable probability of output deviation of a wind-storage hybrid power station, capacity of a battery energy storage system, initial charge state and maximum charge-discharge power, wind power installed capacity, wind power predicted value and scheduling time period number;

s2: establishing a daily operation strategy optimization model of the wind-storage hybrid power station, wherein optimization variables are a planned charging and discharging sequence and power of a battery energy storage system within a scheduling day, and an optimization target is that daily operation income of the wind-storage hybrid power station is maximum;

s3: on the basis of Monte Carlo simulation of daily operation conditions of the wind-storage hybrid power station, a genetic algorithm is adopted to solve a daily operation strategy optimization model of the wind-storage hybrid power station, and an optimal planned charging and discharging sequence and power of a battery energy storage system in the wind-storage hybrid power station are given.

For a wind-storage hybrid power station, the key for improving daily operation income is to make a reasonable daily output plan on the basis of wind power prediction; based on the above, the invention provides a short-term operation strategy optimization model of a wind-storage hybrid power station, and the optimization goal is that the daily operation yield of the wind-storage hybrid power station is maximum, which is specifically shown in formula (1):

in equation (1), t is a scheduling period index, (t ═ 1,2, …, m); m is the number of scheduling time segments; f is the daily operation income of the wind-storage hybrid power station; f_w,tFor windStoring the electricity selling income of the hybrid power station in the dispatching time t, F_dev,tPunishing cost for output deviation of the wind-storage hybrid power station in a scheduling time period t; f_bessThe operating cost of the battery energy storage system in a scheduling day;

in the formula (1), the electricity selling income F of the wind-storage hybrid power station in the dispatching time t_w,tSpecifically, as shown in formula (2):

F_w,t＝ρ_t×(P_b,t+P_w,t)×ΔT (2)

in the formula (2), ρ_tReal-time electricity prices for a scheduling period t; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; p_w,tActual wind power for a scheduling period t; Δ T is the scheduling period length;

in formula (1), the wind-storage hybrid power station outputs deviation punishment cost F in the scheduling period t_dev,tSpecifically, as shown in formula (3):

F_dev,t＝ρ_dev,t×|P_b,t+P_w,t-P_s,t|×ΔT (3)

in the formula (3), ρ_dev,tPunishment of electricity price for output deviation of a scheduling time period t; p_s,tFor the planned output of the wind-storage hybrid power station in the scheduling time period t, as shown in formula (4):

P_s,t＝P_wf,t+P_bs,t (4)

in the formula (4), P_s,tReporting the planned output of the wind-storage hybrid power station in a scheduling time t to a scheduling center by a wind-storage hybrid power station operator one day in advance; p_wf,tA wind power predicted value of a scheduling time period t; p_bs,tThe planned charging and discharging power of the battery energy storage system in the scheduling time t is provided, a positive value represents the discharging of the battery energy storage system, and a negative value represents the charging of the battery energy storage system;

in the formula (1), F_bessThe operation cost of the battery energy storage system in the dispatching day is estimated according to the investment cost of the battery energy storage system and the number of charging and discharging cycles experienced in the dispatching day, and is specifically shown in a formula (5):

in the formula (5), V_totalInvestment cost for a battery energy storage system; n is_totalCycle life of the battery energy storage system; u. of_ch,t、u_dis,tRespectively representing binary variables, u, for switching the charging and discharging states of the battery energy storage system_ch,tTaking 1 indicates that the battery energy storage system is switched from a discharge state to a charge state in a scheduling time t, and u_dis,tTaking 1 to indicate that the battery energy storage system is switched from a charging state to a discharging state in a scheduling time t;

the constraints in the wind-storage hybrid power station daily operation strategy optimization model are as follows:

output deviation probability constraint of the wind-storage hybrid power station:

P_r{|P_b,t+P_w,t-P_s,t|＞P_set}≤p (6)

in the formula (6), P_setThe maximum allowable deviation of the generated output of the wind-storage hybrid power station is obtained; p_rThe power generation output deviation out-of-limit probability of the wind-storage hybrid power station is shown on the left side of the inequality; p is the probability that the output deviation of the wind-storage hybrid power station exceeds the maximum allowable deviation;

and (3) restricting the charge and discharge rate of the battery energy storage system:

-P_ch,m≤P_b,t≤P_dis,m (7)

in the formula (7), P_ch,mAnd P_dis,mRespectively setting the maximum charging and discharging rates of the battery energy storage system;

and (3) constraint of the shortest charging and discharging time of the battery energy storage system:

T_ch≥T_ch,m (8)

T_dis≥T_dis,m (9)

in the formulae (8) and (9), T_chAnd T_disRespectively the charging and discharging duration time, T, of the battery energy storage system_ch,mAnd T_dis,mRespectively of battery energy storage systemsMinimum charge and discharge durations, which may be expressed by equations (10) and (11), respectively;

in the formulae (10), (11), E_cThe capacity of the battery energy storage system; e_maxAnd E_minRespectively the maximum and minimum state of charge allowed by the battery energy storage system; eta_chAnd η_disRespectively charging and discharging efficiencies of the battery energy storage system;

and (3) restraining the state of charge of the battery energy storage system:

E_min≤E_soc,t≤E_max (12)

in the formula (12), E_soc,tFor the state of charge of the battery energy storage system in the scheduling period t, the calculation can be performed according to the formula (13):

in the formula (13), E_cThe capacity of the battery energy storage system; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; Δ T is the scheduling period length; eta_chAnd η_disRespectively the charging and discharging efficiency of the battery energy storage system.

As shown in fig. 2, the solving step of the wind-storage hybrid power plant daily operation strategy optimization model in step S3 is specifically as follows:

s3.1: setting genetic algorithm parameters, including: population size N_popCross rate P_cThe rate of variation P_mAnd maximum evolution algebra G_max；

S3.2: random generation of N_popAn initial population of chromosome bars; the chromosome in the initial population consists of m code bits, and the value of the code bit i is '1', which indicates that the battery energy storage system is in a discharging state in the ith scheduling period; the value is "-1", which indicates that the battery energy storage system is in a discharge state in the ith scheduling period (i is 1,2, …, m);

s3.3: the evolution algebra index g is initialized to 0, namely, g is 0;

s3.4: calculating the fitness of the g-th generation of chromosomes by using g as g +1, and initializing a chromosome index k to be 1, namely using k as 1;

s3.5: decoding the kth chromosome in the current population, and determining the charge-discharge state of the battery energy storage system within a scheduling day;

if the battery energy storage system does not meet the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the fitness V of the kth chromosome according to the formula (14)_fit,kAnd jumping to step S3.8;

V_fit,k＝0 (14)

if the battery energy storage system meets the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time period according to the charging and discharging states of the battery energy storage system in the scheduling day, and continuing to execute the step S3.6;

s3.6: simulating the operation condition of the wind-storage hybrid power station within the dispatching day by adopting a Monte Carlo simulation method, and iteratively updating the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system;

s3.7: simulating the operation condition of the wind-storage hybrid power station in a dispatching day according to the optimal planned charging and discharging power of the battery energy storage system, calculating the out-of-limit probability of the daily operation income and the power generation output deviation of the wind-storage hybrid power station, and calculating the fitness V of the kth chromosome according to a formula (15)_fit,k：

V_fit,k＝F-β_pen×max{P_r{|P_b,t+P_w,t-P_s,t|＞P_set}-p,0} (15)

In the formula (15), the first and second groups,β_penis a pre-given penalty factor;

s3.8: judging whether fitness calculation of all chromosomes in the current population is finished or not, namely judging whether k is equal to N or not_pop(ii) a If k is<N_popIf yes, let k be k +1, and go to step S3.5; otherwise, continuing to execute the next step S3.9;

s3.9: judging whether the maximum evolution algebra is reached, namely judging whether the index G of the evolution algebra is equal to G_max(ii) a If G is G_maxIf yes, continuing to execute the step 3.10; otherwise, based on fitness, carrying out copy, crossover and variation operations on the current population, updating the chromosome population, and skipping to the step S3.4;

s3.10: and outputting the wind-storage hybrid power station operation scheme corresponding to the chromosome with the highest chromosome fitness in the current population as an optimal scheme, and ending the algorithm flow.

The chromosome population initialization step of step S3.2 is specifically as follows:

s3.2.1: randomly determining the initial charging and discharging states of the battery energy storage system, and assuming that the initial state of the battery energy storage system is a discharging state for convenience of description, namely the value of a first binary code bit in a chromosome is '1'; the index n is initialized to 1, namely n is 1;

s3.2.2: randomly generating an interval [ T ]_dis,m,m]Integer T obeying uniform distribution_nThe 1 st to the T th code bit of the chromosome_nAll code bits are initialized to be 1, namely from scheduling period 1 to scheduling period T_nThe battery energy storage systems are all in a discharging state;

s3.2.3: if the remaining length of the chromosome

Less than the shortest charging time T of the battery energy storage system_ch,mInitializing all the residual code bits of the chromosome to be '-1', and jumping to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1The T th of chromosome_n+1 to the first

The code bits all take the value of "-1", i.e. from the scheduling period T_n+1 to

The battery energy storage systems are all in a charging state; let n be n + 1;

s3.2.4: if the remaining length of the chromosome

Less than the shortest discharge time T of the battery energy storage system_dis,mThen the remaining code bits of the chromosome are initialized to be 1, and the process jumps to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1Then the T-th chromosome_n+1 to the first

The code bits all take the value of '1', namely from the scheduling period T_n+1 to

The battery energy storage systems are all in a discharging state; let n be n +1, S3.2.3 is performed;

s3.2.5: the above steps are repeated until the entire initial chromosome population is produced.

S3.5, decoding the kth chromosome in the current population, and determining the initial planned charging and discharging power P of the battery energy storage system at each scheduling period_bs,tThe steps are as follows:

s3.5.1: decoding the kth chromosome in the current population to determine the charge-discharge state of the battery energy storage system at each scheduling period;

s3.5.2: calculating the number of the continuous charging and discharging state periods of the battery energy storage system in a scheduling day; suppose that the battery energy storage system undergoes J continuous charging and discharging periods in a scheduling day, and the jth continuous charging and discharging period is T_c,jEach scheduling period is composed of an initial scheduling period index of t_st,jThe index of the final scheduling period is t_end,j(j＝1,2…,J)；

S3.5.3: sequentially calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time interval in J continuous charging and discharging time intervals; if the jth continuous charging and discharging time interval is a charging time interval, the initial planned charging power of each scheduling time interval is as follows:

if the jth continuous charging and discharging period is a discharging period, the initial planned discharging power of each scheduling period is as follows:

in the formulae (15), (16), E_st,jFor the initial state of charge of the battery energy storage system in the jth continuous charging and discharging period, if j is 1, then E_st,jThe initial state of charge of the battery energy storage system on the whole scheduling day; if j>1, and the jth continuous charging and discharging time interval is a charging time interval, then E_st,j＝E_min(ii) a If j>1, and the jth continuous charging and discharging time interval is the discharging time interval, then E_st,j＝E_max。

As shown in fig. 3, the step S3.6 of simulating the operation condition of the wind-storage hybrid power station within the scheduling day by using the monte carlo simulation method, and iteratively updating the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system includes the following steps:

s3.6.1: the method comprises the following steps of initializing each evaluation index for measuring the real-time technology and the economic performance of the wind-storage hybrid power station to zero, namely:

in formula (17), M_PPST,t、M_EPDP,tAnd M_EESI,tThe method comprises the steps of respectively evaluating values of three real-time evaluation indexes, namely, probability of generation plan Tracking (PPST), injection Power Deviation penalty Expectation (EPDP) and electric Energy sales income expectation (EESI) of a wind-storage hybrid Power station in a scheduling period t;

s3.6.2: initializing a scheduling period index t to 1; according to the charging and discharging sequence of the battery energy storage system determined by chromosome decoding, obtaining the working state of the battery energy storage system at an initial period; if the battery energy storage system is in a charging state in the initial period, setting the initial charging state as E_min(ii) a If the battery energy storage system is in a discharging state in the initial period, setting the initial charge state of the battery energy storage system as E_max；

S3.6.3: and determining values of three shape parameters alpha, beta and gamma in Versatile distribution according to the wind work predicted value of the time period t. In the invention, Versatile distribution is adopted to describe the random fluctuation characteristic of the actual wind power near the predicted value, and the probability density function f (x) and the cumulative probability distribution function F (x) of the distribution are respectively shown as follows:

f(x)＝αβexp[-α(x-γ)]/{1+exp[-α(x-γ)]}^β+1 (18)

F(x)＝{1+exp[-α(x-γ)]}^-β (19)

the wind power fluctuation characteristic is closely related to the output level thereof, so that when the output level is different, the shape parameters alpha, beta and gamma in Versatile distribution are different in value;

s3.6.4: generating random wind power obeying a Versatile distribution, namely:

in the formula (20), P_sw,tRandom wind power for a scheduling period t; g_windThe installed capacity of the wind power plant; function F^-1(. is the inverse of the cumulative probability distribution function for a Versatile distribution; c is the interval [0,1]Random numbers uniformly distributed throughout the interior;

s3.6.5: the random wind power P generated in step S3.6.4_sw,tDetermining the real-time charging and discharging power P of the battery energy storage system in the current time period according to the formula (21) as the real-time wind power_b,tDetermining the electricity selling income F according to the formulas (2) and (3) respectively_w,tPenalty cost with output deviation F_dev,t；

S3.6.6: updating the values of the indexes EPDP and EESI in the scheduling time period t according to the calculation result of the step S3.6.5; as shown in the following formula:

in the formula (22), n_simSimulating the number of Monte Carlo times given in advance;

as for the index PPST, if the wind-storage hybrid plant can generate power at the planned output during this period, the index is updated as follows:

s3.6.7: calculating the state of charge E of the battery energy storage system in the current time period according to the formula (13)_soc,t；

S3.6.8: let t be t +1, repeatedly carry out S3.6.3-S3.6.7 until t be m;

s3.6.9: S3.6.2-S3.6.8 are repeatedly executed until reaching the preset valueFixed maximum Monte Carlo simulation number n_sim；

S3.6.10: if the power generation plan tracking probability M of the wind-storage hybrid power station in all time periods_PPST,tSatisfying the power grid dispatching requirement, namely satisfying the formula (6) to give constraint, and ending the simulation; if the power grid dispatching instruction is not met in certain time intervals, the planned charging and discharging power of the battery energy storage system is set to be overlarge in the time intervals, and at the moment, the planned charging and discharging power in the time intervals can be corrected according to the following formula:

in the formula (24), P'_bs,tThe planned charging and discharging power of the battery energy storage system after the time period t is corrected; gamma is a correction coefficient established in advance; omega_bsA set of scheduling periods which do not meet the scheduling requirements of the power grid; when the planned charging and discharging power of the time interval which does not meet the scheduling requirement is corrected, attention needs to be paid to the value range of the planned charging and discharging power; if the battery energy storage system is in a charging state, the planned charging and discharging power value range is [ -P ]_ch,mE_c,0]On the contrary, if the battery energy storage system is in the discharging state, the planned charging and discharging power value range is [0, P ]_dis,mE_c](ii) a After the planned charge/discharge power is corrected, S3.6.1 is skipped to continue the simulation.

According to the invention, by optimizing the planned charging and discharging sequence and power of the battery energy storage system, the wind-storage hybrid power station benefit is improved on the premise of meeting the grid-connected technical requirements of the wind power plant.

The above description is only an example of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A wind-storage hybrid power station daily operation strategy optimization method based on a genetic algorithm is characterized by comprising the following steps:

s1: given relevant parameters, including: real-time electricity price, output deviation punishment electricity price, maximum allowable probability of output deviation of the wind-storage hybrid power station, capacity of a battery energy storage system, initial charge state and maximum charge-discharge power, wind power installed capacity, wind power predicted value and scheduling time period number;

s2: establishing a daily operation strategy optimization model of the wind-storage hybrid power station, wherein optimization variables are a planned charging and discharging sequence and power of a battery energy storage system within a scheduling day, and an optimization target is that daily operation income of the wind-storage hybrid power station is maximum;

s3: on the basis of Monte Carlo simulation of daily operation conditions of the wind-storage hybrid power station, a genetic algorithm is adopted to solve a daily operation strategy optimization model of the wind-storage hybrid power station, and an optimal planned charging and discharging sequence and power of a battery energy storage system in the wind-storage hybrid power station are given.

2. The method for optimizing the daily operation strategy of the wind-storage hybrid power station based on the genetic algorithm as claimed in claim 1, wherein the daily operation strategy optimization model of the wind-storage hybrid power station in step S2 is specifically shown in formula (1):

in equation (1), t is a scheduling period index, (t ═ 1,2, …, m); m is the number of scheduling time segments; f is the daily operation income of the wind-storage hybrid power station; f_w,tFor the electricity sales revenue of the wind-storage hybrid power station during the scheduling period t, F_dev,tPunishing cost for output deviation of the wind-storage hybrid power station in a scheduling time period t; f_bessThe operating cost of the battery energy storage system in a scheduling day;

in the formula (1), the electricity selling income F of the wind-storage hybrid power station in the dispatching time t_w,tSpecifically, as shown in formula (2):

F_w,t＝ρ_t×(P_b,t+P_w,t)×ΔT (2)

in the formula (2), ρ_tReal-time electricity prices for a scheduling period t; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; p_w,tActual wind power for a scheduling period t; Δ T is the scheduling period length;

in formula (1), the wind-storage hybrid power station outputs deviation punishment cost F in the scheduling period t_dev,tSpecifically, as shown in formula (3):

F_dev,t＝ρ_dev,t×|P_b,t+P_w,t-P_s,t|×ΔT (3)

in the formula (3), ρ_dev,tPunishment of electricity price for output deviation of a scheduling time period t; p_s,tFor the planned output of the wind-storage hybrid power station in the scheduling time period t, as shown in formula (4):

P_s,t＝P_wf,t+P_bs,t (4)

in the formula (4), P_s,tReporting the planned output of the wind-storage hybrid power station in a scheduling time t to a scheduling center by a wind-storage hybrid power station operator one day in advance; p_wf,tA wind power predicted value of a scheduling time period t; p_bs,tThe planned charging and discharging power of the battery energy storage system in the scheduling time t is provided, a positive value represents the discharging of the battery energy storage system, and a negative value represents the charging of the battery energy storage system;

in the formula (1), F_bessThe operation cost of the battery energy storage system in the dispatching day is estimated according to the investment cost of the battery energy storage system and the number of charging and discharging cycles experienced in the dispatching day, and is specifically shown in a formula (5):

in the formula (5), V_totalInvestment cost for a battery energy storage system; n is_totalCycle life of the battery energy storage system; u. of_ch,t、u_dis,tRespectively representing binary variables, u, for switching the charging and discharging states of the battery energy storage system_ch,t1 is taken to represent that the battery energy storage system is put in the scheduling time tSwitching of the electrical state to the charging state u_dis,tTaking 1 to indicate that the battery energy storage system is switched from a charging state to a discharging state in a scheduling time t;

the constraints in the wind-storage hybrid power station daily operation strategy optimization model are as follows:

output deviation probability constraint of the wind-storage hybrid power station:

P_r{|P_b,t+P_w,t-P_s,t|＞P_set}≤p (6)

in the formula (6), P_setThe maximum allowable deviation of the generated output of the wind-storage hybrid power station is obtained; p_rThe power generation output deviation out-of-limit probability of the wind-storage hybrid power station is shown on the left side of the inequality; p is the probability that the output deviation of the wind-storage hybrid power station exceeds the maximum allowable deviation;

and (3) restricting the charge and discharge rate of the battery energy storage system:

-P_ch,m≤P_b,t≤P_dis,m (7)

in the formula (7), P_ch,mAnd P_dis,mRespectively setting the maximum charging and discharging rates of the battery energy storage system;

and (3) constraint of the shortest charging and discharging time of the battery energy storage system:

T_ch≥T_ch,m (8)

T_dis≥T_dis,m (9)

in the formulae (8) and (9), T_chAnd T_disRespectively the charging and discharging duration time, T, of the battery energy storage system_ch,mAnd T_dis,mThe minimum charging and discharging duration of the battery energy storage system can be respectively expressed by formulas (10) and (11);

equation (10)) In (11), E_cThe capacity of the battery energy storage system; e_maxAnd E_minRespectively the maximum and minimum state of charge allowed by the battery energy storage system; eta_chAnd η_disRespectively charging and discharging efficiencies of the battery energy storage system;

and (3) restraining the state of charge of the battery energy storage system:

E_min≤E_soc,t≤E_max (12)

in the formula (12), E_soc,tFor the state of charge of the battery energy storage system in the scheduling period t, the calculation can be performed according to the formula (13):

in the formula (13), E_cThe capacity of the battery energy storage system; p_b,tActual charging and discharging power of the battery energy storage system in a scheduling time period t is provided, a positive value represents discharging of the battery energy storage system, and a negative value represents charging of the battery energy storage system; Δ T is the scheduling period length; eta_chAnd η_disRespectively the charging and discharging efficiency of the battery energy storage system.

3. The method for optimizing the daily operation strategy of the wind-storage hybrid power station based on the genetic algorithm as claimed in claim 2, wherein the step of solving the daily operation strategy optimization model of the wind-storage hybrid power station in step S3 is as follows:

s3.1: setting genetic algorithm parameters, including: population size N_popCross rate P_cThe rate of variation P_mAnd maximum evolution algebra G_max；

S3.2: random generation of N_popAn initial population of chromosome bars; the chromosome in the initial population consists of m code bits, and the value of the code bit i is '1', which indicates that the battery energy storage system is in a discharging state in the ith scheduling period; the value is "-1", which indicates that the battery energy storage system is in a discharge state in the ith scheduling period (i is 1,2, …, m);

s3.3: the evolution algebra index g is initialized to 0, namely, g is 0;

s3.4: calculating the fitness of the g-th generation of chromosomes by using g as g +1, and initializing a chromosome index k to be 1, namely using k as 1;

s3.5: decoding the kth chromosome in the current population, and determining the charge-discharge state of the battery energy storage system within a scheduling day;

if the battery energy storage system does not meet the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the fitness V of the kth chromosome according to the formula (14)_fit,kAnd jumping to step S3.8;

V_fit,k＝0 (14)

if the battery energy storage system meets the shortest charging and discharging time constraints given by the formulas (8) and (9), calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time period according to the charging and discharging states of the battery energy storage system in the scheduling day, and continuing to execute the step S3.6;

s3.6: simulating the operation condition of the wind-storage hybrid power station within the dispatching day by adopting a Monte Carlo simulation method, and iteratively updating the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system;

s3.7: simulating the operation condition of the wind-storage hybrid power station in a dispatching day according to the optimal planned charging and discharging power of the battery energy storage system, calculating the out-of-limit probability of the daily operation income and the power generation output deviation of the wind-storage hybrid power station, and calculating the fitness V of the kth chromosome according to a formula (15)_fit,k：

V_fit,k＝F-β_pen×max{P_r{|P_b,t+P_w,t-P_s,t|＞P_set}-p,0} (15)

In the formula (15), β_penIs a pre-given penalty factor;

s3.8: judging whether fitness calculation of all chromosomes in the current population is finished or not, namely judging whether k is equal to N or not_pop(ii) a If k is<N_popIf yes, let k be k +1, and go to step S3.5; otherwise, continuing to execute the next step S3.9;

s3.9: is judged to beIf not, judging whether the index G of the evolution algebra is equal to G_max(ii) a If G is G_maxIf yes, continuing to execute the step 3.10; otherwise, based on fitness, carrying out copy, crossover and variation operations on the current population, updating the chromosome population, and skipping to the step S3.4;

s3.10: and outputting the wind-storage hybrid power station operation scheme corresponding to the chromosome with the highest chromosome fitness in the current population as an optimal scheme, and ending the algorithm flow.

4. The wind-storage hybrid power station daily operation strategy optimization method based on genetic algorithm as claimed in claim 3, wherein the chromosome population initialization step of step S3.2 is as follows:

s3.2.1: randomly determining the initial charging and discharging states of the battery energy storage system, and assuming that the initial state of the battery energy storage system is a discharging state for convenience of description, namely the value of a first binary code bit in a chromosome is '1'; the index n is initialized to 1, namely n is 1;

s3.2.2: randomly generating an interval [ T ]_dis,m,m]Integer T obeying uniform distribution_nThe 1 st to the T th code bit of the chromosome_nAll code bits are initialized to be 1, namely from scheduling period 1 to scheduling period T_nThe battery energy storage systems are all in a discharging state;

s3.2.3: if the remaining length of the chromosome

Less than the shortest charging time T of the battery energy storage system_ch,mInitializing all the residual code bits of the chromosome to be '-1', and jumping to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1The T th of chromosome_n+1 to the first

The code bits all take the value of "-1", i.e. from the scheduling period T_n+1 to

The battery energy storage systems are all in a charging state; let n be n + 1;

s3.2.4: if the remaining length of the chromosome

Less than the shortest discharge time T of the battery energy storage system_dis,mThen the remaining code bits of the chromosome are initialized to be 1, and the process jumps to S3.2.5; otherwise, randomly generating intervals

Integer T obeying uniform distribution_n+1Then the T-th chromosome_n+1 to the first

The code bits all take the value of '1', namely from the scheduling period T_n+1 to

The battery energy storage systems are all in a discharging state; let n be n +1, S3.2.3 is performed;

s3.2.5: the above steps are repeated until the entire initial chromosome population is produced.

5. The method for optimizing the daily operation strategy of the wind-storage hybrid power station based on the genetic algorithm as claimed in claim 3, wherein the step S3.5 is performed to decode the kth chromosome in the current population and determine the scheduling time interval of the battery energy storage systemInitial planned charging and discharging power P_bs,tThe steps are as follows:

s3.5.1: decoding the kth chromosome in the current population to determine the charge-discharge state of the battery energy storage system at each scheduling period;

s3.5.2: calculating the number of the continuous charging and discharging state periods of the battery energy storage system in a scheduling day; suppose that the battery energy storage system undergoes J continuous charging and discharging periods in a scheduling day, and the jth continuous charging and discharging period is T_c,jEach scheduling period is composed of an initial scheduling period index of t_st,jThe index of the final scheduling period is t_end,j(j＝1,2…,J)；

S3.5.3: sequentially calculating the initial planned charging and discharging power of the battery energy storage system in each scheduling time interval in J continuous charging and discharging time intervals; if the jth continuous charging and discharging time interval is a charging time interval, the initial planned charging power of each scheduling time interval is as follows:

if the jth continuous charging and discharging period is a discharging period, the initial planned discharging power of each scheduling period is as follows:

in the formulae (15), (16), E_st,jFor the initial state of charge of the battery energy storage system in the jth continuous charging and discharging period, if j is 1, then E_st,jThe initial state of charge of the battery energy storage system on the whole scheduling day; if j>1, and the jth continuous charging and discharging time interval is a charging time interval, then E_st,j＝E_min(ii) a If j>1, and the jth continuous charging and discharging time interval is the discharging time interval, then E_st,j＝E_max。

6. The wind-storage hybrid power station daily operation strategy optimization method based on the genetic algorithm as claimed in claim 3, wherein the step S3.6 is to simulate the operation conditions of the wind-storage hybrid power station within the scheduling day by adopting a Monte Carlo simulation method, and iteratively update the planned charging and discharging power of the battery energy storage system according to the simulation result to obtain the optimal planned charging and discharging power of the battery energy storage system as follows:

s3.6.1: the method comprises the following steps of initializing each evaluation index for measuring the real-time technology and the economic performance of the wind-storage hybrid power station to zero, namely:

in formula (17), M_PPST,t、M_EPDP,tAnd M_EESI,tRespectively taking values of a power generation plan tracking probability PPST, an injection power deviation punishment expected EPDP and an electric energy sales income expected EESI of the wind-storage hybrid power station, wherein the three real-time evaluation indexes are values in a scheduling time period t;

s3.6.2: initializing a scheduling period index t to 1; according to the charging and discharging sequence of the battery energy storage system determined by chromosome decoding, obtaining the working state of the battery energy storage system at an initial period; if the battery energy storage system is in a charging state in the initial period, setting the initial charging state as E_min(ii) a If the battery energy storage system is in a discharging state in the initial period, setting the initial charge state of the battery energy storage system as E_max；

S3.6.3: and determining values of three shape parameters alpha, beta and gamma in Versatile distribution according to the wind work predicted value of the time period t. In the invention, Versatile distribution is adopted to describe the random fluctuation characteristic of the actual wind power near the predicted value, and the probability density function f (x) and the cumulative probability distribution function F (x) of the distribution are respectively shown as follows:

f(x)＝αβexp[-α(x-γ)]/{1+exp[-α(x-γ)]}^β+1 (18)

F(x)＝{1+exp[-α(x-γ)]}^-β (19)

the wind power fluctuation characteristic is closely related to the output level thereof, so that when the output level is different, the shape parameters alpha, beta and gamma in Versatile distribution are different in value;

s3.6.4: generating random wind power obeying a Versatile distribution, namely:

in the formula (20), P_sw,tRandom wind power for a scheduling period t; g_windThe installed capacity of the wind power plant; function F^-1(. is the inverse of the cumulative probability distribution function for a Versatile distribution; c is the interval [0,1]Random numbers uniformly distributed throughout the interior;

s3.6.5: the random wind power P generated in step S3.6.4_sw,tDetermining the real-time charging and discharging power P of the battery energy storage system in the current time period according to the formula (21) as the real-time wind power_b,tDetermining the electricity selling income F according to the formulas (2) and (3) respectively_w,tPenalty cost with output deviation F_dev,t；

S3.6.6: updating the values of the indexes EPDP and EESI in the scheduling time period t according to the calculation result of the step S3.6.5; as shown in the following formula:

in the formula (22), n_simSimulating the number of Monte Carlo times given in advance;

as for the index PPST, if the wind-storage hybrid plant can generate power at the planned output during this period, the index is updated as follows:

s3.6.7: calculating the current battery energy storage system according to formula (13)State of charge of time interval E_soc,t；

S3.6.8: let t be t +1, repeatedly carry out S3.6.3-S3.6.7 until t be m;

s3.6.9: S3.6.2-S3.6.8 are repeatedly executed until reaching the preset maximum Monte Carlo simulation times n_sim；

S3.6.10: if the power generation plan tracking probability M of the wind-storage hybrid power station in all time periods_PPST,tSatisfying the power grid dispatching requirement, namely satisfying the formula (6) to give constraint, and ending the simulation; if the power grid dispatching instruction is not met in certain time intervals, the planned charging and discharging power of the battery energy storage system is set to be overlarge in the time intervals, and at the moment, the planned charging and discharging power in the time intervals can be corrected according to the following formula:

in the formula (24), P'_bs,tThe planned charging and discharging power of the battery energy storage system after the time period t is corrected; gamma is a correction coefficient established in advance; omega_bsA set of scheduling periods which do not meet the scheduling requirements of the power grid; when the planned charging and discharging power of the time interval which does not meet the scheduling requirement is corrected, attention needs to be paid to the value range of the planned charging and discharging power; if the battery energy storage system is in a charging state, the planned charging and discharging power value range is [ -P ]_ch,mE_c,0]On the contrary, if the battery energy storage system is in the discharging state, the planned charging and discharging power value range is [0, P ]_dis,mE_c](ii) a After the planned charge/discharge power is corrected, S3.6.1 is skipped to continue the simulation.

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