CN114123354A - Wind storage integrated system optimal scheduling method based on t distribution weed algorithm - Google Patents

Wind storage integrated system optimal scheduling method based on t distribution weed algorithm Download PDF

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CN114123354A
CN114123354A CN202210090266.4A CN202210090266A CN114123354A CN 114123354 A CN114123354 A CN 114123354A CN 202210090266 A CN202210090266 A CN 202210090266A CN 114123354 A CN114123354 A CN 114123354A
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distribution
wind
weed
power
storage integrated
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CN114123354B (en
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付波
康毅恒
赵熙临
权轶
黄晓啸
邓竞成
黄志斌
冯万璐
徐康
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Hubei University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention relates to a wind storage integrated system optimal scheduling method based on a t-distribution weed algorithm, which comprises the steps of dividing a scheduling period of a power system into a plurality of time periods, acquiring actual operation parameters of the power system, giving wind power predicted output and loads in each time period based on a target function and set constraint conditions of an optimal scheduling mathematical model of the wind storage integrated power system, optimizing the problem by using the t-distribution weed algorithm, and initializing to obtain different wind storage integrated power system optimal scheduling schemes; then judging whether all the schemes meet constraint conditions or not, and adjusting unqualified schemes; finally, judging whether the maximum iteration times are met, if so, outputting an optimal scheduling scheme, and ending the optimal scheduling method; if not, the iterative times are repeatedly judged after a new scheme is generated by the t-distribution weed algorithm. The invention provides a more economic and environment-friendly optimal scheduling method for the wind storage integrated power system, reduces the cost and enhances the wind power consumption capability of the system.

Description

Wind storage integrated system optimal scheduling method based on t distribution weed algorithm
Technical Field
The invention relates to the technical field of power system automation, in particular to a wind storage integrated system optimal scheduling method based on a t-distribution weed algorithm.
Background
The wind-storage integrated power system is a new power system, and has better negative effects on eliminating wind power on-line and wind power consumption than distributed energy storage effects, so the wind-storage integrated system is developed in the future. The optimized dispatching of the wind storage integrated system is to carry out reasonable load distribution in the processing range of each unit, so that the generated power generation cost of a target system is reduced to the minimum under the conditions of meeting load requirements, operation constraint requirements and the like.
With the development of computer and artificial intelligence technology, a large number of elegant intelligent optimization algorithms are developed and used for solving optimization problems, and are also widely applied to solving optimization scheduling models. Compared with the traditional mathematical solving method, the intelligent algorithm has more flexible setting of the objective function, higher searching efficiency, better suitability for processing high-dimensional, discrete and non-convex nonlinear problems, good global convergence, no limitation of solving object function characteristics and the like, and is widely applied to optimal scheduling solving of the power system. Particle swarm algorithm, differential evolution algorithm, genetic algorithm and the like are applied to the problem of optimizing and scheduling of the power system, and certain effect is achieved. The weed algorithm is often used for solving some complicated problems due to simple structure, few parameters and good robustness. the t-distribution weed algorithm is an improved weed algorithm, and the Latin hypercube sampling is utilized to initialize the population, so that the diversity of the initialized population is increased; the t distribution is used for carrying out space diffusion, so that the self-adaptive conversion of global search and local optimization is realized, and the algorithm search mode is more flexible; and an intrusion population strategy is introduced, so that the probability of trapping the algorithm into local optimum is reduced.
Disclosure of Invention
The invention aims to provide a method for optimizing and scheduling a wind-storage integrated power system, which improves the search efficiency and the search precision of the optimized and scheduled method, increases the consumption of wind power, reduces the operation cost of the power system and improves the economical efficiency of the operation of a power grid.
In order to achieve the aim, the invention provides a wind storage integrated system optimal scheduling method based on a t distribution weed algorithm, which is characterized by comprising the following steps of:
s1: dividing a scheduling cycle of the power system into a plurality of time periods, collecting actual operation parameters of the power system, giving wind power predicted output and loads of each time period based on a target function of an optimized scheduling mathematical model of the wind storage integrated power system and set constraint conditions, optimizing the problem by using a t-distribution weed algorithm, and initializing to obtain different wind storage integrated power system optimized scheduling schemes;
s2: judging whether all the schemes meet constraint conditions or not, and adjusting unqualified schemes;
s3: judging whether the maximum iteration times are met, if so, outputting an optimal scheduling scheme, and ending the optimal scheduling method; if not, a new scheme is generated by the t-distribution weed algorithm and S2 is entered.
The wind-storage integrated system optimization scheduling method based on the t distribution weed algorithm is used for collecting the electric power systemActual operating parameters of the system: maximum and minimum output of thermal power generating unit
Figure 744854DEST_PATH_IMAGE001
、
Figure 516501DEST_PATH_IMAGE002
(ii) a Downward and upward climbing power R of thermal power generating uniti,down、Ri,up(ii) a Energy storage capacity limitationE C,max (ii) a Maximum charge-discharge power per unit time of energy storage
Figure 998298DEST_PATH_IMAGE003
、
Figure 880804DEST_PATH_IMAGE004
。
In the wind storage integrated system optimal scheduling method based on the t distribution weed algorithm, the objective function of the wind storage integrated power system optimal scheduling mathematical model is based on the following formula
minf=FH+FM+FW+FC （1）
In the formula (1), FHFor cost of coal consumption, FMThermal power pollution cost, FWCost for wind abandonment, FCEnergy storage operating costs.
In the wind-storage integrated system optimal scheduling method based on the t-distribution weed algorithm, the constraint conditions include power balance constraint, unit output constraint, thermal power unit climbing constraint, energy storage constraint, wind-storage integrated constraint and rotation standby constraint, and specifically:
power balance constraint
Figure 764446DEST_PATH_IMAGE005
（2）
In the formula (2), PD,tFor total system load demand, P, during a period of tl,tFor the system loss, P, during the period of tC,tThe magnitude of energy storage charging and discharging power is t period, PH,i,tThe power output of the ith thermal power generating unit in the t period of time PW,l,tAnd predicting output force for the wind power in the t period.
Unit output constraint
Figure 706994DEST_PATH_IMAGE006
（3）
Figure 676087DEST_PATH_IMAGE007
（4）
In the formulas (3) and (4),
Figure 96704DEST_PATH_IMAGE008
、
Figure 569274DEST_PATH_IMAGE009
respectively the minimum and maximum output values of the ith thermal power generating unit,
Figure 213882DEST_PATH_IMAGE010
and predicting the maximum output for the wind power at the time t.
Thermal power generating unit climbing restraint
-Ri,down≤PH,i,t- PH,i,t-1 ≤Ri,up （5）
In the formula (5), Ri,down、Ri,upThe maximum climbing speeds of the ith thermal power generating unit in the downward direction and the upward direction are respectively.
Restraint of stored energy
Figure 139113DEST_PATH_IMAGE011
（6）
Figure 894579DEST_PATH_IMAGE012
（7）
In the formulae (6) and (7),
Figure 956076DEST_PATH_IMAGE013
for maximum charging and discharging of stored energy per unit timeThe power of the electric motor is controlled by the power controller,E C,t for the capacity of the energy storage system at time t,E C,max in order to maximize the capacity of the energy storage system, E C,min for the lowest capacity of the energy storage system,P C,t,in andP C,t,out respectively charging and discharging power of the energy storage system at the moment t,η in andη out respectively the charging and discharging efficiency of the energy storage system; Δ t represents the amount of time change.
Wind-storage integrated restraint
Figure 240427DEST_PATH_IMAGE014
（8）
In the formula (8), the reaction mixture is,P C,t,in representing stored energy charging, with a value less than 0.
Rotational back-up restraint
Figure 184112DEST_PATH_IMAGE015
（9）
In the formula (9), the reaction mixture is,
Figure 212111DEST_PATH_IMAGE009
the maximum output of the ith thermal power generating unit;
Figure 924852DEST_PATH_IMAGE016
the maximum output of the first wind power plant; ruAnd (t) is a positive rotation standby at the time t, and the value is 10% of the maximum load at the time t in the system.
In the wind-storage integrated system optimization scheduling method based on the t-distribution weed algorithm, the initialization process of the t-distribution weed algorithm in S1 specifically comprises the following steps: defining the number of units as N, scheduling time as one day, dividing into 24 time intervals, generating an initial population by Latin hypercube sampling in a constraint satisfying range, and generating P0Each weed seed represents a scheduling scheme and is respectively marked as P1、P2、P3、……、P0。
In the wind-storage integrated system optimization scheduling method based on the t-distribution weed algorithm, the process of generating a new scheme by the t-distribution weed algorithm in S3 is specifically as follows:
and S31, individual breeding. Selecting a fitness function, selecting an objective function to be solved by the fitness function, calculating an individual fitness value, determining the number of filial generations propagated by each weed according to the fitness value of the weed, wherein the closer the individual fitness value is to the optimal value, the more seeds are generated, and the calculation formula is as follows:
Figure 114525DEST_PATH_IMAGE017
（10）
in the formula (10), fiIs the fitness value of the ith weed, fmax、fminRespectively the current maximum and minimum fitness value, Smax、SminRespectively setting the maximum seed number and the minimum seed number;
s32, weed spreading. Using the parent of the weed as the center, and diffusing according to the distribution of t to generate WiAnd (3) forming a filial generation population by using filial generation, wherein the calculating formula of the diffusion position of the filial generation is as follows:
Pt,i=Pi+l·t(iter) （11）
in the formula (11), Pt,iTo distribute the offspring positions after diffusion through t, PiIs the parent position, l is the diffusion coefficient, t (iter) is the t distribution with degree of freedom iter;
and S33, the advantages are eliminated. The population scale will continuously increase along with the reproduction iteration, but the ecological environment bearing capacity is limited, and the number exceeds the maximum population scale PmaxWhen in use, all weed individuals are required to be arranged according to the size of the fitness value, the individuals with poor fitness are eliminated, and the pre-P is reservedmaxA weed is planted;
and S34, invading population. Introducing an intrusion strategy in a number of inequalities
Figure 545506DEST_PATH_IMAGE018
If the continuous n generations are established, the algorithm is judged to be trapped into local optimum, and new population with 20% of maximum population number is generated according to random distributionAnd (3) replacing old individuals with lower fitness value, wherein pbest (k) is the k-th generation optimal fitness value, and n and alpha are positive integers.
In the wind-storage integrated system optimal scheduling method based on the t-distribution weed algorithm, the t-distribution diffusion in S32 is specifically as follows:
the degree of freedom n is set as the iteration number, t distribution is close to Cauchy distribution in the early operation period, the t distribution weed algorithm is wider in distribution compared with the common weed algorithm, so that the global search capability is stronger, the t distribution gradually approaches to the standard normal distribution along with the increase of the iteration number in the later operation period, the distribution tends to be concentrated, so that the local search capability is enhanced along with the increase of the iteration number, and the probability density function of the t distribution is as follows:
Figure 111617DEST_PATH_IMAGE019
（12）
in the formula (12), n is a degree of freedom.
The invention has the advantages that:
(1) by adopting Latin hypercube sampling initialization, the diversity of an initial population is increased, the offspring diffusion is carried out by utilizing t distribution, the self-adaptive conversion of the global search and the local search of the algorithm is realized, the optimization searching capability is enhanced, an invasive population strategy is introduced, the probability that the algorithm falls into the local optimum is reduced, the optimization searching precision is enhanced after the algorithm is improved, a more economic and environment-friendly optimal scheduling method is provided for a wind storage integrated power system, the cost is reduced, and the wind power consumption capability of the system is enhanced;
(2) the weed algorithm is improved correspondingly, and the algorithm can be applied to solve a more complex scene model.
Drawings
FIG. 1 is an overall flow chart of a wind storage integrated system optimization scheduling method based on a t-distribution weed algorithm;
FIG. 2 is a flow chart of the t-distribution weed algorithm of the present invention;
FIG. 3 is a graph of wind power prediction and load prediction in a simulation experiment of the present invention;
FIG. 4 is a comparison graph of optimization of a weed algorithm (IWO), a particle swarm optimization algorithm (PSO), a particle swarm optimization algorithm with decreasing inertial weight Logarithm (LOGWPSO) and a t-distribution-weed algorithm) on a wind storage integrated system in a simulation experiment of the invention;
FIG. 5 is a comparison graph of the optimization of the distributed energy storage power system by the weed algorithm (IWO), the Particle Swarm Optimization (PSO), the particle swarm optimization with decreasing logarithm of inertial weight (LOGWPSO), and the t-distribution-weed algorithm) in the simulation experiment of the invention;
FIG. 6 is a diagram of wind power consumption of the wind-fire-storage combined system and the wind-storage-containing integrated system in a simulation experiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a wind storage integrated system optimal scheduling method based on a t-distribution weed algorithm includes the following steps:
s1: according to the actual operation parameters of the power system, a wind storage integrated power system optimization scheduling mathematical model is established, and the objective function formula is as follows:
minf=FH+FM+FW+FC （1）
in the formula (1), FHFor cost of coal consumption, FMThermal power pollution cost, FWCost for wind abandonment, FCEnergy storage operating costs;
s2: dividing a scheduling cycle of a power system into a plurality of time periods, setting constraint conditions, and giving wind power predicted output and loads of each time period, wherein the constraint conditions comprise power balance constraint, unit output constraint, thermal power unit climbing constraint, charging and discharging power constraint, energy storage constraint, wind and storage integrated constraint and rotation standby constraint;
s3: calculating and optimizing the problem by using a t-distribution weed algorithm, and initializing to obtain different wind storage integrated power system optimal scheduling schemes;
s4: judging whether all the schemes meet constraint conditions or not, and adjusting unqualified schemes;
s5: judging whether the maximum iteration times are met, if so, outputting an optimal scheduling scheme, and ending the optimal scheduling method; if not, comparing all schemes and generating a new scheme by using a t distribution weed algorithm, and entering S4;
in the embodiment, firstly, an optimized scheduling mathematical model of the power system is established according to the actual operation characteristics of the wind storage integrated power system, relevant constraint conditions are set, then a t-distribution weed algorithm is introduced to solve the established optimized scheduling mathematical model, and the optimized scheduling scheme of the power system is obtained. In this embodiment, the load prediction and the wind power output prediction are performed by software in the prior art.
As shown in fig. 1, the mathematical model for optimizing and scheduling of the power system in step S1 is specifically expressed as:
Figure 413285DEST_PATH_IMAGE020
（2）
in the formula (2), FHFor cost of coal consumption, FMThe thermal power pollution cost, T is the number of dispatching time segments, N is the number of thermal power units, PH,i,tThe power of the ith thermal power generating unit in the t periodi、bi、ciIs the fuel cost coefficient, alpha, of the ith thermal power generating uniti、βi、γiPollution cost coefficient P for the ith thermal power generating unitW,l,tThe wind power output of the first wind power generator in the period of t,
Figure 305018DEST_PATH_IMAGE010
the maximum value of the output of the first wind power generator in the t period, KwqWind curtailment coefficient, CSCFor energy storage cost factor, PC,t,outIs the magnitude of the stored energy discharge, P, of the t periodC,t,inFor a period of tThe charging capacity can be increased.
In this embodiment, the established optimal scheduling mathematical model is solved by using a t-distribution weed algorithm, and the algorithm flow is shown in fig. 2, and the specific steps are as follows:
(1) setting a maximum population size P0Dimension D of weed seed, maximum iteration number iter, maximum and minimum number of generated offspring seeds SmaxAnd SminWeed progeny diffusion coefficient l and other parameters;
(2) generating initial population by Latin hypercube sampling, and recording seeds as P1、P2、P3、……、P0；
(3) Calculating the fitness function value f of all the current seedsiSearching the current minimum fitness function value f of all the seedsminAnd a maximum fitness value fmaxThen determining the generation number of each weed;
(4) each weed is subjected to spatial diffusion in a t distribution to generate offspring, and the offspring and the parents form a new population;
(5) calculating the fitness function values of all the current seeds, judging whether the population exceeds the maximum population scale, and if so, eliminating the seeds with poor fitness values until the population meets the population maximum scale P0；
(6) Repeating the steps (3) to (5) until the maximum iteration number iter is reached, and outputting the current global optimal seed position gbest；
In this embodiment, the constraint conditions set according to the wind-storage integrated power system should be met as much as possible during the optimization of the algorithm, and the unit output in the scheme that does not meet the constraint conditions is adjusted according to the limit data in the constraint conditions during the optimization. The constraints are as follows:
power balance constraint
Figure 692137DEST_PATH_IMAGE005
（3）
In the formula (3), PD,tFor total system load demand, P, during a period of tl,tFor the system loss, P, during the period of tC,tFor a period of tCapable of discharging power, PH,i,tThe power output of the ith thermal power generating unit in the t period of time PW,l,tAnd predicting output force for the wind power in the t period.
Unit output constraint
Figure 593096DEST_PATH_IMAGE006
（4）
Figure 483692DEST_PATH_IMAGE007
（5）
In the formulas (4) and (5),
Figure 811905DEST_PATH_IMAGE008
、
Figure 420741DEST_PATH_IMAGE009
respectively the minimum and maximum output values of the ith thermal power generating unit,
Figure 125392DEST_PATH_IMAGE010
and predicting the maximum output for the wind power at the time t.
Thermal power generating unit climbing restraint
-Ri,down≤PH,i,t- PH,i,t-1 ≤Ri,up （6）
In the formula (6), Ri,down、Ri,upThe maximum climbing speeds of the ith thermal power generating unit in the downward direction and the upward direction are respectively.
Restraint of stored energy
Figure 136073DEST_PATH_IMAGE011
（7）
Figure 635188DEST_PATH_IMAGE012
（8）
In the formulae (7) and (8),
Figure 731320DEST_PATH_IMAGE013
is the maximum charge-discharge power of the energy storage unit time,E C,t for the capacity of the energy storage system at time t,E C,max in order to maximize the capacity of the energy storage system, E C,min for the lowest capacity of the energy storage system,P C,t,in andP C,t,out respectively charging and discharging power of the energy storage system at the moment t,η in andη out respectively the charging and discharging efficiency of the energy storage system; Δ t represents the amount of time change.
Wind-storage integrated restraint
Figure 974082DEST_PATH_IMAGE014
（9）
In the formula (9), PC,t,inRepresenting stored energy charging, with a value less than 0.
Rotational back-up restraint
Figure 839270DEST_PATH_IMAGE015
（10）
In the formula (10), the compound represented by the formula (10),
Figure 509286DEST_PATH_IMAGE009
the maximum output of the ith thermal power generating unit;
Figure 92714DEST_PATH_IMAGE016
the maximum output of the first wind power plant; ruAnd (t) is a positive rotation standby at the time t, and the value is 10% of the maximum load at the time t in the system.
The following describes the optimal scheduling method of the power system according to the present invention through simulation experiments.
In the embodiment, the wind-storage integrated power system comprises 5 thermal power generating units and a wind-storage integrated power station, the scheduling period is 1 day, the time period number is T =24, the time intervals are all 1h, the predicted output and load curves of wind power are shown in figure 3, the investment cost of the energy storage system is not considered, and the energy storage mode is a lithium iron phosphate battery.
Substituting the relevant parameter data of the example for optimization calculation, and obtaining a minimum operation cost under the condition of meeting the constraint condition, wherein the results of the optimization (t-IWO) of the weed algorithm (IWO), the particle swarm algorithm (PSO), the particle swarm optimization algorithm (LOGWPSO) with the logarithmic decrement of the inertial weight and the optimization (t-IWO) of the t-distribution weed algorithm provided by the invention are shown in a table 1:
table 1 comparison of optimized scheduling results of four algorithms for wind power storage integrated power system
Figure 342430DEST_PATH_IMAGE021
Table 2 comparison of optimized scheduling results of distributed energy storage power system by four algorithms
PSO LOGWPSO IWO t-IWO
Average running cost/$ 731176 707957 695535 686792
Air loss rate/%) 19.4 13.7 13.6 11.5
As can be seen from tables 1 and 2, the target values of the t-distribution weed algorithm are superior to the optimization results of other algorithms of the control group, and the operation cost and the wind abandon rate can be reduced at the same time. It can be seen from fig. 4 that the overall optimizing capability of the t-IWO is superior to that of the comparison algorithm, and the optimizing capability of the improved algorithm provided by the invention on the short-term power dispatching cost of the wind-storage integrated power system has significant advantages compared with other optimization algorithms.
As can be seen from fig. 5, the wind-storage integrated system can achieve a smaller wind abandon rate by using less stored energy, and the improvement of the wind power utilization rate can reduce the use of fossil energy, thereby reducing environmental pollution, thus proving that the wind-storage integrated system can well solve the goals of reducing wind abandon and reducing cost under the condition of meeting various constraints. The wind storage integrated system optimization scheduling method based on the t-distribution weed algorithm has a good optimization scheduling result, is superior to the traditional power system optimization scheduling, and has a field application value and a development prospect.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (7)

1. A wind-storage integrated system optimal scheduling method based on a t-distribution weed algorithm is characterized by comprising the following steps:
s1: dividing a scheduling cycle of the power system into a plurality of time periods, collecting actual operation parameters of the power system, giving wind power predicted output and loads of each time period based on a target function of an optimized scheduling mathematical model of the wind storage integrated power system and set constraint conditions, optimizing the problem by using a t-distribution weed algorithm, and initializing to obtain different wind storage integrated power system optimized scheduling schemes;
s2: judging whether all the schemes meet constraint conditions or not, and adjusting unqualified schemes;
s3: judging whether the maximum iteration times are met, if so, outputting an optimal scheduling scheme, and ending the optimal scheduling method; if not, a new scheme is generated by the t-distribution weed algorithm and S2 is entered.
2. The wind-storage integrated system optimal scheduling method based on the t-distribution weed algorithm according to claim 1, characterized by collecting actual operation parameters of a power system: maximum and minimum output of thermal power generating unit
Figure 1217DEST_PATH_IMAGE001
、
Figure 449516DEST_PATH_IMAGE002
(ii) a Downward and upward climbing power R of thermal power generating uniti,down、Ri,up(ii) a Energy storage capacity limitationE C,max (ii) a Maximum charge-discharge power per unit time of energy storage
Figure 291570DEST_PATH_IMAGE003
、
Figure 217938DEST_PATH_IMAGE004
。
3. The wind-storage integrated system optimal scheduling method based on t-distribution weed algorithm according to claim 1, characterized in that the objective function of the wind-storage integrated power system optimal scheduling mathematical model is based on the following formula
minf=FH+FM+FW+FC （1）
In the formula (1), FHFor cost of coal consumption, FMThermal power pollution cost, FWCost for wind abandonment, FCEnergy storage operating costs.
4. The wind-storage integrated system optimal scheduling method based on the t-distribution weed algorithm according to claim 1, wherein the constraint conditions include power balance constraint, unit output constraint, thermal power unit climbing constraint, energy storage constraint, wind-storage integrated constraint and rotation standby constraint, and specifically include:
power balance constraint
Figure 563468DEST_PATH_IMAGE005
（2）
In the formula (2), PD,tFor total system load demand, P, during a period of tl,tFor the system loss, P, during the period of tC,tThe magnitude of energy storage charging and discharging power is t period, PH,i,tThe power output of the ith thermal power generating unit in the t period of time PW,l,tPredicting output power for the wind power in the t time period;
unit output constraint
Figure 917089DEST_PATH_IMAGE006
（3）
Figure 715281DEST_PATH_IMAGE007
（4）
In the formulas (3) and (4),
Figure 445340DEST_PATH_IMAGE008
、
Figure 645377DEST_PATH_IMAGE009
respectively the minimum and maximum output values of the ith thermal power generating unit,
Figure 701058DEST_PATH_IMAGE010
predicting the maximum output for the wind power at the time t;
thermal power generating unit climbing restraint
-Ri,down≤PH,i,t- PH,i,t-1 ≤Ri,up （5）
In the formula (5), Ri,down、Ri,upThe maximum climbing speeds of the ith thermal power generating unit in the downward direction and the upward direction are respectively set;
restraint of stored energy
Figure 986545DEST_PATH_IMAGE011
（6）
Figure 254716DEST_PATH_IMAGE012
（7）
In the formulae (6) and (7),
Figure 309259DEST_PATH_IMAGE013
is the maximum charge-discharge power of the energy storage unit time,E C,t for the capacity of the energy storage system at time t,E C,max in order to maximize the capacity of the energy storage system, E C,min for the lowest capacity of the energy storage system,P C,t,in andP C,t,out respectively charging and discharging power of the energy storage system at the moment t,η in andη out respectively the charging and discharging efficiency of the energy storage system; Δ t represents a time variation;
wind-storage integrated restraint
Figure 270262DEST_PATH_IMAGE014
（8）
In the formula (8), the reaction mixture is,P C,t,in representing an energy storage charge, having a value less than 0;
rotational back-up restraint
Figure 43046DEST_PATH_IMAGE015
（9）
In the formula (9), the reaction mixture is,
Figure 380486DEST_PATH_IMAGE009
the maximum output of the ith thermal power generating unit;
Figure 555116DEST_PATH_IMAGE016
the maximum output of the first wind power plant; ruAnd (t) is a positive rotation standby at the time t, and the value is 10% of the maximum load at the time t in the system.
5. The wind-storage integrated system optimization scheduling method based on the t-distribution weed algorithm according to claim 1, wherein the initialization process of the t-distribution weed algorithm in S1 specifically comprises the following steps: defining the number of units as N, scheduling time as one day, dividing into 24 time intervals, generating an initial population by Latin hypercube sampling in a constraint satisfying range, and generating P0Each weed seed represents a scheduling scheme and is respectively marked as P1、P2、P3、……、P0。
6. The wind-storage integrated system optimal scheduling method based on the t-distribution weed algorithm according to claim 1, wherein the process of generating the new scheme by the t-distribution weed algorithm is specifically as follows:
s61, individual reproduction; selecting a fitness function, selecting an objective function to be solved by the fitness function, calculating an individual fitness value, determining the number of filial generations propagated by each weed according to the fitness value of the weed, wherein the closer the individual fitness value is to the optimal value, the more seeds are generated, and the calculation formula is as follows:
Figure 421441DEST_PATH_IMAGE017
（10）
in the formula (10), fiIs the fitness value of the ith weed, fmax、fminRespectively the current maximum and minimum fitness value, Smax、SminRespectively setting the maximum seed number and the minimum seed number;
s62, weed spreading; using the parent of the weed as the center, and diffusing according to the distribution of t to generate WiAnd (3) forming a filial generation population by using filial generation, wherein the calculating formula of the diffusion position of the filial generation is as follows:
Pt,i=Pi+l·t(iter) （11）
in the formula (11), Pt,iTo distribute the offspring positions after diffusion through t, PiIs the parent position, l is the diffusion coefficient, t (iter) is the t distribution with degree of freedom iter;
s63, the advantages are eliminated; the population scale will continuously increase along with the reproduction iteration, but the ecological environment bearing capacity is limited, and the number exceeds the maximum population scale PmaxWhen in use, all weed individuals are required to be arranged according to the size of the fitness value, the individuals with poor fitness are eliminated, and the pre-P is reservedmaxA weed is planted;
s64, invading population; introducing an intrusion strategy in a number of inequalities
Figure 212679DEST_PATH_IMAGE018
And if the continuous n generations are established, judging that the algorithm is trapped into local optimality, and generating new individuals with 20% of maximum population number according to random distribution to replace old individuals with lower fitness values, wherein pbest (k) is the k-th generation optimal fitness value, and n and alpha are positive integers.
7. The wind-storage integrated system optimization scheduling method based on the t-distribution weed algorithm according to claim 1, wherein t-distribution diffusion in S62 is specifically as follows:
the degree of freedom n is set as the iteration number, t distribution is close to Cauchy distribution in the early operation period, the t distribution weed algorithm is wider in distribution compared with the common weed algorithm, so that the global search capability is stronger, the t distribution gradually approaches to the standard normal distribution along with the increase of the iteration number in the later operation period, the distribution tends to be concentrated, so that the local search capability is enhanced along with the increase of the iteration number, and the probability density function of the t distribution is as follows:
Figure 822652DEST_PATH_IMAGE019
（12）
in the formula (12), n is a degree of freedom.
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