CN109950933B - Wind-solar-storage combined peak regulation optimization method based on improved particle swarm optimization - Google Patents
Wind-solar-storage combined peak regulation optimization method based on improved particle swarm optimization Download PDFInfo
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Abstract
The invention discloses a wind-solar-energy-storage combined peak regulation optimization method based on an improved particle swarm optimization algorithm, which is used for acquiring power plants, transformer substations and load data in an Anyang area based on a PSASP data packet in the Anyang area. And then establishing a peak shaving optimization model for the Anyang area, simultaneously considering the action of an energy storage technology, establishing an energy storage model, and adding the peak shaving model to obtain a wind-light-storage combined peak shaving model. And taking load data, wind power output data and photovoltaic output data of the Anyang region on a typical day as examples to perform MATLAB simulation, and solving by adopting an improved particle swarm algorithm. The simulation can obtain a better optimization result, which shows the effectiveness of the improved particle swarm optimization for solving the wind-solar-storage combined peak regulation model, and has certain guiding significance for engineering practice. The patent has good popularization value and application prospect.
Description
Technical Field
The invention belongs to the field of operation and control of power systems, and relates to a wind-solar-energy-storage combined peak regulation optimization method based on an improved particle swarm algorithm.
Background
The consumption of fossil energy in the world is increasing year by year, so that the problem of environmental pollution is more serious. Energy transformation and energy structure reformation are advocated in various countries and regions. The large-scale development and utilization of clean energy has become a reality. At present, wind power generation and photovoltaic power generation occupy the main part in new energy power generation, and the installed capacity of the new energy power generation is also increased year by year. By the end of 2017, the national thermal power generation machine 110604 ten thousand kilowatts, the hydroelectric power 34119 ten thousand kilowatts, the nuclear power 3582 ten thousand kilowatts, the wind power 16367 ten thousand kilowatts and the solar power generation 13025 ten thousand kilowatts. The output of the wind turbine generator and the photovoltaic power station is mainly determined by the wind speed and the illumination intensity, and the wind speed and the illumination intensity are influenced by the environment and have randomness, so that the output of the wind power station and the photovoltaic power station changes along with the wind speed and the illumination intensity and has volatility. Due to the instability of the output of new energy power generation, after large-scale new energy power generation (mainly wind power generation and solar power generation) is connected into a power grid, the stable operation and control of a power system are greatly influenced.
The energy storage technology is a key technology for ensuring the power generation reliability of the new energy because the energy storage technology can stabilize the output fluctuation of the new energy and is developed rapidly.
As the installed capacity of the new energy power generation in China is increased rapidly, the energy storage demand is also increasing continuously. According to statistics, in 2013, the total amount of the pumped storage installed in China is 21.5GW, and the installed amount of other energy storage technologies is 65 GW. The pumped storage is widely applied due to low operation cost, large capacity, mature technology and perfect operation management mechanism, but the construction of a pumped storage power station has high dependence on local geographic environment and cannot be used in a large scale. The invention carries out modeling aiming at battery energy storage, establishes a wind-solar-energy-storage combined peak regulation optimization model and carries out simulation analysis. Considering that the optimization model has nonlinear constraints, the Cplex solver may be called to solve after the nonlinear constraints are converted into linear constraints, but part of useful information may be lost in the conversion process, and at this time, an intelligent algorithm may be used for solving, such as a genetic algorithm, an ant colony algorithm, a particle swarm algorithm, and the like. The genetic algorithm needs mutation, selection, hybridization and other processes, while the ant colony algorithm needs pheromone and continuous random interference, so that the two algorithms are more complex. The particle swarm algorithm is simple to implement, so the particle swarm algorithm is adopted in the invention.
The particle swarm algorithm has been widely regarded by the academic world since being proposed, and has the characteristics of easy realization, high solving precision and fast convergence, but is easy to fall into a local optimal solution. Over the development of twenty years, Particle Swarm Optimization (PSO) is improved, such as fuzzy adaptive PSO (PSO) based on inertial weight and fuzzy rule. Most of the improvement algorithms are mainly aimed at improvement in some aspect, but the situation of falling into a local optimal solution still occurs. Aiming at the situation, the particle swarm optimization is improved in various ways, and suitable parameters or parameter change modes are obtained through repeated debugging.
The invention improves the particle swarm optimization and solves the wind-solar-energy-storage combined peak regulation optimization problem based on the improved particle swarm optimization.
Disclosure of Invention
The invention provides a wind-solar-storage combined peak regulation optimization method based on an improved particle swarm optimization algorithm, which mainly aims at the improvement aspect of the algorithm and the application of the improved algorithm to the wind-solar-storage combined peak regulation optimization problem.
The invention introduces a nonlinear curve to describe the change process of particle swarm optimization parameters, and the thought is inspired by the eagle predation process. The hawk is at a high altitude before the beginning of the prey, then the suspected prey is seen, the suspected prey flies fast and dives downwards, the approximate area is only known, the prey is fast and fast, the speed is slowed down, the prey is found by fine search, and the enemy is controlled at once. The characteristics of the logarithmic curve, the arctangent function and some power functions are that the change is very fast at the beginning, which is beneficial to the algorithm to jump out of a local optimum point, and then the algorithm is beneficial to local fine search after the algorithm is slowed down, and the algorithm is easy to converge to a global optimum solution.
The invention comprises the following steps:
a wind-solar-storage combined peak regulation optimization method based on an improved particle swarm optimization algorithm comprises the following steps:
an objective function:
wherein C is gi (t) Fuel cost of thermal power generating unit, C pi (t) cost of light rejection, C wi (T) is the cost of wind curtailment, T is the scheduling period, T is a scheduling period, N g Number of thermal power generating units, N p Number of photovoltaic power stations, N w For number of wind farms, P BZ Is the total power of the energy storage device, K B Is the power cost coefficient of the energy storage device;
wherein the thermal power generating unit generates fuel with low cost
The light rejection peak-shaving penalty function is as follows:
C pi (t)＝α i [P pi.fore (t)-P pi (t)]·Δt (3)
the wind abandon peak regulation penalty function is as follows:
C wi (t)＝β i [P wi.fore (t)-P wi (t)]·Δt (4)
wherein P is gi (t) power of thermal power generating unit at t moment, P pi.fore (t) is the predicted force value of the photovoltaic at time t, P pi (t) is the actual output value of photovoltaic at time t, P wi.fore (t) wind power prediction output value at t moment, P wi (t) is the actual wind power output value at the moment t, and delta t is a time interval;
wherein, P B (t) the power of the stored energy at the time t is greater than 0 to indicate discharging, less than 0 to indicate charging, X (t) is discharged when the value is 1, 0 is not discharged, Y (t) is charged when the value is 1, 0 is not charged, and X (t) + Y (t) is less than or equal to 1;
the constraint conditions comprise thermal power unit constraint conditions and other constraint conditions, wherein the thermal power unit constraint conditions comprise
And power upper and lower limit constraint conditions:
P gi.min ≤P gi (t)≤P gi,max (6)
the thermal power generating unit climbing constraint condition is as follows:
wind and light abandoning constraint conditions:
P pi (t)≤P pi.fore (t) (8)
P wi (t)≤P wi.fore (t) (9)
wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P gi.min Is the minimum power, P, of the ith thermal power generating unit gi,max Is the maximum power of the ith thermal power generating unit,
the lower limit of the climbing of the thermal power generating unit,
the upper limit of the climbing of the thermal power generating unit is set;
other constraints include
Power balance constraint conditions:
outgoing power limit constraints:
P in.min ≤P in (t)≤P in.max (11)
wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P pi (t) power at time t of ith photovoltaic power station, P wi (t) is the power of the ith wind turbine generator set at the moment t, P d (t) power of the load at time t, P in (t) is the tie line power at time t, P in.min For minimum power of the tie line, P in.max Is the maximum power of the tie line;
step 2, carrying out wind-solar-storage combined peak regulation optimization based on improved particle swarm optimization, specifically comprising
Step 2.1, in order to enable the particles to better jump out of the local optimal solution, the diversity of the particles can be kept in the later iteration stage, and the particles are unevenly divided into three groups;
step 2.2, three different brand-new inertia weight change modes and learning factor change modes are adopted for each group of particles, the whole particle swarm is arranged according to the adaptability value from small to large after each iteration is finished, the whole particle swarm is grouped again according to the adaptability value of the particles, the first group comprises the particles 25 before the adaptability value, the second group comprises the particles 25 after the adaptability value, and the third group comprises the remaining 50 particles;
step 2.3, adding disturbance to the first group of particles after the regrouping, and randomly initializing the second group; elicited by a Bacterial Foraging Algorithm (BFA), eliminating the particles with the worst fitness of the third group according to the rule of 'win or loss and survival of the fittest' of the BFA algorithm, and then copying the first group of particles and the second group of particles to form a brand new third group of particles, wherein the total number of the particles is kept unchanged, so that the third group of particles is also better; calculating global adaptive values of the formed brand new three groups of particles, and then repeatedly iterating;
and 3, performing simulation analysis by adopting an improved particle swarm algorithm based on typical solar wind power output, photovoltaic output and load data in the Anyang region.
The improved particle swarm algorithm is applied to the wind-solar energy storage and peak regulation optimization model, a simpler energy storage model is established, and the method has practical guiding significance on energy storage capacity configuration in engineering calculation; compared with the traditional particle swarm algorithm, the feasibility of improving the particle swarm algorithm is verified through MATLAB simulation research, the local optimal solution jumping-out aspect and the iteration convergence times are obviously improved, and a better optimization result can be obtained.
Drawings
FIG. 1 is a particle position update diagram of the particle swarm algorithm of the present invention.
FIG. 2 is a block diagram of an improved particle swarm algorithm.
FIG. 3 is a wind power output curve for a typical day in the Anyang region in the simulation example.
Fig. 4 is a photovoltaic output curve for a typical day in the region of anhang in the simulation example.
Fig. 5 is a load curve of a typical day in the ann yang region in the simulation example.
Fig. 6 is an iteration curve of a conventional particle swarm algorithm in a simulation example.
FIG. 7 is a particle swarm algorithm iteration curve for improving inertial weight and learning factor in a simulation example.
FIG. 8 is an iteration curve of the improved particle swarm optimization algorithm of the present invention in a simulation example.
FIG. 9 is a wind-solar energy storage capacity curve and a load curve of a simulation example using a conventional particle swarm optimization.
FIG. 10 is a wind-solar energy storage capacity curve and a load curve of a particle swarm algorithm adopting improved inertia weight and learning factors in a simulation calculation example.
FIG. 11 is a wind-solar energy storage capacity curve and a load curve of a simulation example using the improved particle swarm optimization of the present invention.
FIG. 12 is a comparison of the results of the three algorithms in the simulation example.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
1. Energy storage participating peak regulation system model
The system model of energy storage participating in peak shaving is as follows:
wherein C is gi (t) Fuel cost of thermal power generating unit, C pi (t) cost of light rejection, C wi (T) is the cost of wind curtailment, T is the scheduling period, T is a scheduling period, N g Number of thermal power generating units, N p Number of photovoltaic power stations, N w For the number of wind farms, P BZ Is the total power of the energy storage device, K B Is the power cost factor of the energy storage device.
Wherein the thermal power generating unit generates fuel with low cost
The light rejection peak-toning penalty function is as follows:
C pi (t)＝α i [P pi.fore (t)-P pi (t)]·Δt (3)
the wind abandon peak regulation penalty function is as follows:
C wi (t)＝β i [P wi.fore (t)-P wi (t)]·Δt (4)
wherein P is gi (t) power of thermal power generating unit at t moment, P pi.fore (t) is the predicted force value of the photovoltaic at time t, P pi (t) is the actual output value of the photovoltaic at time t, P wi.fore (t) wind power prediction output value at t moment, P wi (t) is the actual wind power output value at the time t, delta t is the time interval, and the fuel cost coefficient a i 、b i 、c i The value is related to the rated power of the unit and can be obtained by looking up related documents, and the penalty coefficient alpha of abandoned light and abandoned wind i 、β i And the information can also be obtained by consulting relevant documents.
Wherein, P B (t) represents the power of stored energy at time t, more than 0 represents discharge, less than 0 represents charge, X (t) is discharged when it takes on the value 1, 0 is not discharged, Y (t) is charged when it takes on the value 1, 0 is not charged, and X (t) + Y (t) is not more than 1.
2. Constraint condition of thermal power generating unit
And (3) power upper and lower limit constraint:
P gi.min ≤P gi (t)≤P gi,max (6)
and (3) climbing restraint of the thermal power generating unit:
the constraint conditions of wind abandonment and light abandonment are as follows:
P pi (t)≤P pi.fore (t) (8)
P wi (t)≤P wi.fore (t) (9)
wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P gi.min Minimum power, P, for the ith thermal power plant gi,max Is the maximum power of the ith thermal power generating unit,
the lower limit of the climbing of the thermal power generating unit,
and the upper limit of the climbing of the thermal power generating unit is determined.
3. Other constraints
Constraint of power balance
Outgoing power limit constraints
P in.min ≤P in (t)≤P in.max (11)
Wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P pi (t) power at time t of ith photovoltaic power station, P wi (t) is the power of the ith wind turbine generator set at the moment t, P d (t) power of the load at time t, P in (t) is the tie line power at time t, P in.min For minimum power of the tie line, P in.max Is the maximum power of the tie line.
4. Solving algorithm-improved particle swarm algorithm
The improved particle swarm algorithm is also based on the traditional particle swarm algorithm, and a mathematical model of the traditional particle swarm algorithm is introduced in this chapter.
4.1 traditional particle swarm optimization
Assuming that the number of particles is N, the position and velocity of the ith particle are expressed as follows:
x i ＝(x i,1 ,x i,2 ,…,x i,D ),i＝1,2,…,N (12)
v i ＝(v i,1 ,v i,2 ,…,v i,D ),i＝1,2,..,N (13)
they are all D-dimensional vectors, where the velocity ranges from (-v) max ,v max ) And the particles are prevented from being far away from the solution space in a certain dimension.
Each particle in the particle swarm algorithm retains memory in that the particle can follow the best position of its own history and the best position of the population to adjust the next movement, which includes direction and distance, and the two best positions are expressed as follows:
p i ＝(p i,1 ,p i,2 ,…,p i,D ) (14)
p g ＝(p g,1 ,p g,2 ,…,p g,D ) (15)
the particle update equation is shown below:
wherein c is 1 、c 2 Is an acceleration constant, also known as a learning factor, which is a positive number; r is 1 、r 2 Is a random number with a value between 0 and 1 and is subject to uniform distribution; t and t +1 represent the number of iterations;
representing the d-dimensional velocity component of the ith particle during the t-th iteration,
the same process is carried out;
representing the d-dimensional position component of the ith particle during the t-th iteration,
the same process is carried out;
representing the position component of the ith particle in the d-dimension corresponding to the best adaptive value in the process of the t iteration;
indicating the position component of the whole particle swarm in the d-dimension corresponding to the best adaptive value in the t-th iteration process.
Fig. 1 is a graph of the position velocity update of a particle.
4.2 improved particle swarm optimization
The traditional particle swarm algorithm has the defects of easiness in falling into local optimization, low convergence precision, easiness in divergence and the like in the later iteration stage, and the conventional particle swarm algorithm needs to be improved. The particle swarm optimization algorithm is improved mainly in the following aspects:
(1) the improvement on the parameters mainly comprises the following steps: inertial weight, learning factor, population size;
(2) combined with other optimization algorithms;
(3) the algorithm topology, including the global version and the local version, can be improved separately.
The improved particle swarm algorithm mainly comprises the following improvements:
firstly, a grouping method is adopted, and a particle swarm grouping strategy is adopted in the method, because 100 particles are set, the particles are unevenly divided into three groups, namely a first group of 25 particles, a second group of 25 particles and a third group of 50 particles.
The inertial weights and learning factors are then improved in terms of parameters.
After the particles are divided into three groups, the particles in different groups adopt different changing inertia weights and learning factors, even if one particle group falls into the local optimal solution, the other two groups of particles do not fall into the local optimal solution, and the diversity of later-stage groups is kept, so that the whole particle swarm is facilitated to jump out of the local optimal solution and converge to the global optimal solution.
The change formula of the three groups of inertia weights and learning factors is as follows along with the increase of the iteration number.
(ii) inertial weight
A first group:
second group:
third group:
wherein w 1 (i)、w 2 (i)、w 3 (i) Three sets of particle inertial weights, w, respectively 1s 、w 2s 、w 3s Are the initial values of the inertial weights, w, of the three groups of particles, respectively 1e 、w 2e 、w 3e The three sets of inertia weights are the last values, i is the current iteration number, and maxgen is the maximum iteration number.
Learning factor-
A first group:
second group:
third group:
c 31 (i)＝1.49445 (25)
c 32 (i)＝1.49445 (26)
wherein c is 11 (1)、c 12 (1)、c 21 (1)、c 22 (1) Is the learning factor at the start of the iteration, maxgen is the maximum number of iterations, c 11 (maxgen)、c 12 (maxgen)、c 21 (maxgen)、c 22 (maxgen) is the learning factor at the maximum number of iterations, and i is the current number of iterations.
In addition to the improvement of the inertial weight and the learning factor, the invention also performs the selection operation on the whole particle swarm, and the idea is to sort the fitness value of each particle in the order from small to large after each iteration, add small disturbance to the first 25 sorted particles, group the next 25 particles, and group the next 50 particles. And according to the principle of 'eliminating the best and the bad and survival of the fittest' of the bacterial foraging optimization algorithm (BFA), the particle swarm algorithm is combined to improve the particle swarm algorithm. After the particles are sorted according to the fitness, the particles imitate the reproduction process of bacteria, 50 particles with poor fitness in the third group are eliminated, the particles in the first group and the particles in the second group are respectively reproduced once, and 50 particles are obtained as the third group.
Because the three groups are changed according to different inertial weights and learning factors and have no interaction, the particles are grouped again according to the fitness value, namely, each group of particles can interact during each iteration, and each group of particles after grouping updates the population according to the inertial weight and the learning factor of each group, so that the population of the particles can be more favorably converged to the global optimal solution by the updating.
FIG. 2 is a flow chart of an improved particle swarm algorithm.
5. 6MATLAB simulation case analysis
In this chapter, simulation analysis is performed based on a wind power output curve, a photovoltaic output curve and a load curve of a typical day in the Anyang region. At that time, the Anyang region has three thermal power plants, namely an Anyang Tang power plant (2 × 300+2 × 320MW), a forest State Sheng Tang power plant (2 × 350MW) and a county thermal power plant (2 × 350MW), and the data in the brackets are the number of thermal power generating units and rated power of each thermal power plant. However, in practical conditions, only six thermal power generating units are opened in the Anyang region, including four 300MW units and two 350MW units, and the rated maximum output is 1900 MW. At that time, a large amount of new energy is also accessed in the Anyang area, the planned accessed photovoltaic power is 2225MW, and the wind power is 1650 MW.
(1) Wind power, photovoltaic and load data curve of typical day in Anyang region
The wind power output curve is shown in fig. 3, the photovoltaic output curve is shown in fig. 4, and the load curve is shown in fig. 5.
(2) Simulation parameter setting
The population quantity sizepop is set to 100, the particle dimension D to 240, and the maximum iteration maxgen to 200. According to the relevant documents, power generation fuel cost coefficients a, b and c of a 300MW thermal power generating unit are respectively 0.010875 ﹩, 12.8875 ﹩ and 6.78 ﹩, power generation fuel cost coefficients a, b and c of a 350MW thermal power generating unit are respectively 0.003 ﹩, 10.76 ﹩ and 32.96 ﹩, and the minimum output coefficient of the thermal power generating unit is 63%. Thermal power generating unit climbing restraint
Tie line, i.e. outrun power limit P in.min ＝-240MW，P in.max ＝240MW，K B ＝1500。
Each particle is initialized under each constraint condition to obtain an initial position of each particle, and the velocity of each particle is also initialized within the velocity limit range.
(3) Simulation analysis
Mode setting
In order to better compare and analyze the improved particle swarm algorithm provided by the invention, 3 modes are constructed in this chapter, and are divided as follows:
mode 1: solving the model by using a traditional particle swarm algorithm;
mode 2: solving the model by using a particle swarm algorithm for improving inertial weight and learning factors;
mode 3: the model is solved by using the improved particle swarm algorithm.
Pattern solution
(1) An iteration curve of the traditional particle swarm algorithm is shown in FIG. 6, and a wind-solar energy storage capacity curve and a load curve are shown in FIG. 9;
(2) the particle swarm algorithm iteration curve for changing the inertia weight and the learning factor is shown in FIG. 7, and the wind-solar storage capacity curve and the load curve are shown in FIG. 10;
(3) an iteration curve of the improved particle swarm algorithm is shown in FIG. 8, and a wind-solar energy storage capacity curve and a load curve are shown in FIG. 11.
The calculation results pairs of the three modes are shown in fig. 12.
As can be seen from fig. 6, 7, 8 and 12, the conventional particle swarm optimization iterates 37 times and converges right and left, but the solution obtained is the worst, that is, the total cost is the highest, the particle swarm optimization changing the inertia weight and the learning factor has poor convergence, and iterates 123 times and converges right and left, although the iteration is slower, the solution obtained is better than the conventional particle swarm optimization.
Therefore, the improved particle swarm optimization adopted by the method has obvious advantages of solving wind-solar-energy-storage combined peak regulation, fast iterative convergence and optimal calculation result, namely the total cost is lowest, and a better optimization result is obtained, so that the total cost is reduced a lot, the feasibility of the improved algorithm is explained, and the simulation has guiding significance for engineering practice.
Claims (1)
1. A wind-solar-storage combined peak regulation optimization method based on an improved particle swarm optimization algorithm comprises the following steps:
step 1, establishing a mathematical model of a wind-solar-energy-storage combined peak regulation system, based on the following objective function and constraint conditions;
an objective function:
wherein C is gi (t) Fuel cost of thermal power generating unit, C pi (t) cost of light rejection, C wi (T) is the cost of abandoned wind, T is a scheduling period, N g Number of thermal power generating units, N p Number of photovoltaic power stations, N w For number of wind farms, P BZ Is the total power of the energy storage device, K B Is the power cost coefficient of the energy storage device;
wherein the thermal power generating unit generates fuel with low cost
The light rejection peak-toning penalty function is as follows:
C pi (t)＝α i [P pi.fore (t)-P pi (t)]·Δt (3)
the wind abandon peak-load regulation penalty function is as follows:
C wi (t)＝β i [P wi.fore (t)-P wi (t)]·Δt (4)
wherein P is gi (t) power of thermal power generating unit at t moment, P pi.fore (t) is the predicted power value of the photovoltaic at time t, P pi (t) is the actual output value of photovoltaic at time t, P wi.fore (t) wind power prediction output value at t moment, P wi (t) is an actual wind power output value at the moment t, and delta t is a time interval;
wherein, P B (t) is the power of the stored energy at the moment t, more than 0 represents discharging, less than 0 represents charging, X (t) is discharged when the value is 1, 0 is not discharged, Y (t) is charged when the value is 1, 0 is not charged and fullX (t) + Y (t) is less than or equal to 1;
the constraint conditions comprise thermal power unit constraint conditions and other constraint conditions, wherein the thermal power unit constraint conditions comprise
And power upper and lower limit constraint conditions:
P gi.min ≤P gi (t)≤P gi,max (6)
the thermal power generating unit climbing constraint condition is as follows:
wind and light abandoning constraint conditions:
P pi (t)≤P pi.fore (t) (8)
P wi (t)≤P wi.fore (t) (9)
wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P gi.min Is the minimum power, P, of the ith thermal power generating unit gi,max Is the maximum power of the ith thermal power generating unit,
the lower limit of the climbing of the thermal power generating unit,
the upper limit of the climbing of the thermal power generating unit is set;
other constraints include
Power balance constraint conditions:
outgoing power limit constraints:
P in.min ≤P in (t)≤P in.max (11)
wherein P is gi (t) is the power of the ith thermal power generating unit at the moment t, P pi (t) power at time t of ith photovoltaic power station, P wi (t) is the power of the ith wind turbine generator set at the moment t, P d (t) power of the load at time t, P in (t) is the tie line power at time t, P in.min For minimum power of the tie line, P in.max Is the maximum power of the tie line;
step 2, carrying out wind-solar-storage combined peak regulation optimization based on improved particle swarm optimization, specifically comprising
Step 2.1, in order to enable the particles to better jump out of the local optimal solution, the diversity of the particles can be kept in the later iteration stage, and the particles are unevenly divided into three groups;
step 2.2, three different brand-new inertia weight change modes and learning factor change modes are adopted for each group of particles, the whole particle swarm is arranged according to the adaptability value from small to large after each iteration is finished, the whole particle swarm is grouped again according to the adaptability value of the particles, the first group comprises the particles 25 before the adaptability value, the second group comprises the particles 25 after the adaptability value, and the third group comprises the remaining 50 particles;
step 2.3, adding disturbance to the first group of particles after being regrouped, and randomly initializing the second group; inspired by a Bacterial Foraging Algorithm (BFA), eliminating the particles with the worst fitness of the third group according to the rule of 'winning or losing and survival of the fittest' of the BFA algorithm, copying the first group of particles and the second group of particles to form a brand-new third group of particles, keeping the total number of the particles unchanged, and thus the third group of particles is also better particles; calculating global adaptive values of the formed brand new three groups of particles, and then repeatedly iterating;
and 3, performing simulation analysis by adopting an improved particle swarm algorithm based on typical solar wind power output, photovoltaic output and load data in the Anyang region.
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