CN111178642A - Micro-grid optimization method based on simulated annealing particle swarm algorithm - Google Patents

Micro-grid optimization method based on simulated annealing particle swarm algorithm Download PDF

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CN111178642A
CN111178642A CN202010009714.4A CN202010009714A CN111178642A CN 111178642 A CN111178642 A CN 111178642A CN 202010009714 A CN202010009714 A CN 202010009714A CN 111178642 A CN111178642 A CN 111178642A
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周西峰
李书益
郭前岗
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a micro-grid optimization method based on a simulated annealing particle swarm algorithm, which is based on the fact that the pursuit targets are different when a micro-grid operates, the lowest cost and the lowest pollution cannot be met at the same time, and a corresponding objective function is established in consideration of economy and environmental protection. In order to avoid the defect that the traditional particle swarm algorithm is easy to fall into the local optimal solution, the algorithm combining the simulated annealing algorithm and the particle swarm algorithm is adopted, and compared with the traditional particle swarm algorithm, the local optimal solution is not easy to fall into, so that the running mode more suitable for the micro-grid is obtained, and the cost is reduced.

Description

Micro-grid optimization method based on simulated annealing particle swarm algorithm
Technical Field
The invention belongs to the field of microgrid optimization operation, and particularly relates to a microgrid optimization method combining a simulated annealing algorithm and a particle swarm algorithm, so that an operation mode more suitable for a microgrid is obtained.
Background
The distributed power generation system is used as powerful supplement of a centralized power supply system, and has certain advantages in the aspects of power supply economy and environmental pollution degree. However, the cost of power generation and pollution to the environment of distributed power sources remain largely unnoticeable. The distributed power supply is connected to the power grid in a micro-grid mode, and the distributed power supply is the most effective mode for exerting the energy efficiency of the distributed power supply. The combination and output mode of various distributed power supplies can meet different power consumption requirements, and on the premise of ensuring the reliability and stability of power consumption, scientific proportioning enables the power consumption to achieve the maximization of economic benefits and the minimization of environmental pollution is the key point of current discussion.
To date, various algorithms have been applied to the research of optimization algorithms of the microgrid, such as genetic algorithms, particle swarm optimization algorithms, and the like. The optimization problem has two main aspects: firstly, a global minimum point is required to be found, and secondly, a higher convergence rate is required. The genetic algorithm has three basic operators: selection, crossover, and mutation. The implementation of these operators requires many parameters, and the selection of these parameters, which are mostly empirical, seriously affects the quality of the solution. The particle swarm optimization algorithm has no intersection and variation used by the genetic algorithm, the system is initialized to a group of random solutions, and an optimal value is searched through iteration. The self-state of the particles is adjusted by fully utilizing self experience and group experience, and system parameters can be effectively optimized. The method has the defects that the particle swarm is easy to get early and fall into the local optimal solution, so that the optimal solution cannot be found out frequently.
Disclosure of Invention
Based on the defect condition in the prior art, the invention provides a microgrid optimization method combining a simulated annealing algorithm and a particle swarm algorithm for avoiding the defect that the traditional particle swarm algorithm is easy to fall into the local optimal solution, has the advantages of easy realization, fast convergence, higher accuracy and the like, and obtains an operation mode more suitable for a microgrid under the constraint condition of a power system.
Compared with the ordinary Greedy algorithm, the simulated annealing algorithm introduces random factors in the searching process. That is to say, it has a certain probability to accept a solution which is worse than the current solution, so that it can jump out the extreme value, i.e. get rid of the local optimum solution, so as to find out the global optimum solution, and solve the defect that the particle swarm algorithm is easy to fall into the local optimum solution.
The technical solution for realizing the purpose of the invention is as follows:
a micro-grid optimization method based on simulated annealing particle swarm optimization is characterized by comprising the following steps:
step 1: establishing an objective function C comprehensively considering the operating performance of the microgrid3；
Step 2: setting constraint conditions;
and step 3: initializing parameter inertial weight omega and learning factor c1、c2And an annealing speed δ;
and 4, step 4: randomly generating a population, wherein the population comprises m particles, and randomly initializing the speed and the position of the particles;
and 5: calculating the fitness (x (i)) of each particle i, f (x) for short, and recording the optimal position P of the ith particleidGlobal optimum position PpdFitness f (P)id) And global optimum fitness f (P)pd)；
Step 6: according to the global optimum fitness f (P)pd) Calculating to obtain the initial temperature T of the annealing algorithm0；
And 7: note fSA(x) The function is a function for calculating the fitness of the annealing algorithm, and each P is at the current temperature TidFitness of annealing algorithm
Figure BDA0002356680520000021
And 8: selecting genetic algorithm from individual optimum position P by rouletteidOne of the alternative global optimum positions P is selectedpdIs denoted by Prd；
And step 9: will PpdBy substitution of PrdSubstituting a particle swarm formula, and updating the speed of each particle;
step 10: then calculating the fitness of each particle, and updating the optimal position P of each particleidAnd a population global optimum position Ppd；
Step 11: carrying out annealing operation;
step 12: and judging whether a termination condition is met, if so, stopping searching, outputting a calculation result to obtain a global optimal solution of the target function, and if not, turning to the step 7.
Further, the purpose defined in step 1The operation cost of the standard function only considering the economic operation of the microgrid is C1The operation cost when only considering the environmental protection operation of the micro-grid is C2。
Further, the air conditioner is provided with a fan,
Figure BDA0002356680520000022
wherein, C1Only considering the operation cost of the micro-grid during economic operation;
Figure BDA0002356680520000023
the average daily running cost is calculated, and T is 24 hours, namely the running cost of 1-24 hours; cf(t) converting the fuel consumption cost to every kilowatt-hour at time t; cma(t) converting each electric energy unit to the maintenance and management cost of each kilowatt hour at the moment t; cdep(t) depreciation losses of each power unit at time t; cgov(t) government subsidy amount of clean energy at time t; kCgrid(t) the interaction cost with the large power grid at the moment t, and when electricity is purchased from the large power grid, k is more than 0; when selling electricity to a large power grid, k is less than 0.
Further, the air conditioner is provided with a fan,
Figure BDA0002356680520000024
wherein, C2only considering the operation cost (element) of the micro-grid during the environmental protection operation and mainly considering the treatment cost of the polluted gas, j represents the j-th type polluted gas, α j represents the treatment cost of the j-th type polluted gas, and betaijand betamjThe discharge coefficient of the discharge amount of the j-th type polluted gas converted into unit power in a micro-grid power generation unit and a large power grid respectively; pitAnd PmtThe current actual working power of the micro-grid power generation unit and the large power grid at the moment t is indicated; j is the total number of polluted gases; l is the total number of the micro-grid power generation units
Further, the constraint conditions set in the step 2 include power balance constraint of a power grid, output constraint of each power generation unit in a micro power grid, climbing rate constraint of a controllable power generation unit in the micro power grid, transmission power constraint of a micro power grid tie line, charge and discharge capacity constraint of an energy storage device and electric quantity constraint of exchange with a large power grid.
Further, the setting formula of the inertia weight is as follows:
Figure BDA0002356680520000031
wherein, ω ismaxAnd ωminRespectively the initial value and the final value of the inertia weight, K is the current iteration number, KmaxIs the maximum number of iterations.
Further, the roulette selection genetic algorithm in step 8 comprises the following steps:
step 1: pBet ═ rand (), pBet being a random number from 0 to 1;
step 2: fitness f according to annealing algorithmsA(Pid) Calculating cumulative probability
Figure BDA0002356680520000032
And step 3: according to the cumulative probability, selecting the individual optimal position P of the r-th particle meeting the conditionrdReplacing the global optimum position PpdThe conditions are as follows: comfit (r-1) < pBet < comfit (r).
Further, the update speed of each particle is set in step 9 as follows:
Figure BDA0002356680520000033
wherein:
Figure BDA0002356680520000034
-the d-dimensional component of the (k + 1) th iterative particle i airspeed vector;
Figure BDA0002356680520000035
-a d-dimensional component of a k-th iterative particle i-airspeed vector;
c1，c2-acceleration constant, adjusting the learning maximum step size;
r1，r2two random functions, over a range of values [0, 1 ]]To increase search randomness;
omega-inertial weight, non-negative, adjusts the search range for the solution space;
Figure BDA0002356680520000036
-the d-dimensional component of the location vector of the particle i at the k-th iteration;
Figure BDA0002356680520000037
-an individual optimal position of the kth iteration particle i;
Figure BDA0002356680520000038
-global optimal position of the kth iteration particle i.
Further, the update positions of the particles in step 10 are:
Figure BDA0002356680520000039
Figure BDA00023566805200000310
-a d-dimensional component of a location vector of the k-th iteration particle i;
Figure BDA00023566805200000311
-the d-dimensional component of the location vector of the (k-1) th iteration particle i;
Figure BDA00023566805200000312
the d-dimensional component of the velocity vector of the (k-1) th iteration particle i
Further, the annealing operation of each particle in step 11 is:
Tnew＝δTold
Told-the annealing temperature at the previous moment;
Tnew-an iterated annealing temperature;
delta-annealing speed.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the invention establishes a comprehensive objective function considering economy and environmental protection, considers various realistic factors such as power generation cost, maintenance cost, depreciation cost, pollution gas treatment cost and the like, and has realistic significance in optimizing the result.
(2) The method comprehensively considers the factors of economy and environmental protection under the constraint condition of multiple micro-grids, obtains the running mode more suitable for the micro-grids, and has the advantages of lowest total running cost and optimal environmental protection effect.
(3) The simulated annealing particle swarm algorithm provided by the invention can effectively avoid the defect that the traditional particle swarm algorithm is easy to fall into the local optimal solution, and in the particle motion process, when the next generation position of the particle is better than the current position, the particle moves to the next generation position; on the other hand, if the next generation position is worse than the current position, the particles do not move directly to the next generation position, but move with a certain probability, and this probability is controlled by the temperature. Thus, when the temperature drops sufficiently slowly, the particles do not easily jump out of the search area where they are "wanted", thereby enhancing the local search capability of the particles.
Drawings
Fig. 1 is a schematic diagram of a grid-connected microgrid structure according to the present invention.
FIG. 2 is a diagram of the operation of each micro-power source under the conventional particle swarm optimization.
FIG. 3 is a diagram of the operation of each micro-power source under the simulated annealing particle swarm algorithm of the present invention.
Fig. 4 is an algorithm flow diagram of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings, specific data, and accompanying simulation examples.
As shown in fig. 4, the method for optimizing the microgrid based on the simulated annealing particle swarm algorithm obtains an operation mode more suitable for the microgrid by adopting an algorithm combining the simulated annealing algorithm and the particle swarm algorithm. The method comprises the following steps:
step 1: establishing an operation cost objective function C when the economy and the environmental protection performance of the microgrid are comprehensively considered3。
Figure BDA0002356680520000041
In the formula: c3The operation cost (element) is the operation cost when the economy and the environmental protection performance of the micro-grid are comprehensively considered. Lambda [ alpha ]1、λ2Are weight coefficients. Wherein C is1Only the operating cost of the microgrid during economic operation is taken into account, C2Only the operating cost of the micro-grid during the environmental protection operation is considered.
Step 2: a constraint is set.
And step 3: initializing parameter inertial weight omega and learning factor c1、c2And an annealing speed δ.
And 4, step 4: a population is randomly generated containing m particles, and the velocity and position of the particles are randomly initialized.
And 5: calculating the fitness (x (i)) of each particle i into a fitness function, namely an objective function, f (x) for short, and recording the optimal position P of each (ith) particleidGlobal optimum position PpdFitness f (P)id) And global optimum fitness f (P)pd)。
Step 6: according to the global optimum fitness f (P)pd) Calculating to obtain the initial temperature T of the annealing algorithm0。
And 7: note fSA(x) The function is a function for calculating the fitness of the annealing algorithm, and each P is at the current temperature TidFitness of annealing algorithm
Figure BDA0002356680520000051
And 8: using the idea of selecting genetic algorithms by roulette, from an individual optimum position PidOne of the alternative global optimum positions P is selectedpdIs denoted by Prd。
And step 9: will PpdBy substitution of PrdSubstituting a particle swarm formula, and updating the speed of each particle as follows:
Figure BDA0002356680520000052
wherein:
Figure BDA0002356680520000053
-the d-dimensional component of the (k + 1) th iterative particle i airspeed vector;
Figure BDA0002356680520000054
-a d-dimensional component of a k-th iterative particle i-airspeed vector;
c1，c2-acceleration constant, adjusting the learning maximum step size;
r1，r2two random functions, over a range of values [0, 1 ]]To increase search randomness;
omega-inertial weight, non-negative, adjusts the search range for the solution space;
Figure BDA0002356680520000055
-a d-dimensional component of a location vector of the k-th iteration particle i;
Figure BDA0002356680520000056
-an individual optimal position of the kth iteration particle i;
Figure BDA00023566805200000512
-global optimal position of the kth iteration particle i.
Step 10: then calculating the fitness of each particle, and updating the optimal position P of each particleidAnd the population optimal position Ppd。
Figure BDA0002356680520000058
Figure BDA0002356680520000059
-a d-dimensional component of a location vector of the k-th iteration particle i;
Figure BDA00023566805200000510
-the d-dimensional component of the location vector of the (k-1) th iteration particle i;
Figure BDA00023566805200000511
-the d-dimensional component of the velocity vector of the (k-1) th iteration particle i;
step 11: and (3) carrying out annealing operation:
Tnew＝δTold
Told-the annealing temperature at the previous moment;
Tnew-an iterated annealing temperature;
delta-annealing speed.
Step 12: and judging whether the termination condition is met, if so, stopping searching, outputting a calculation result, and if not, turning to the step 7.
Only considering the operation cost C of the microgrid during economic operation in the objective function defined in the step 11And the operation cost C only considering the environment-friendly operation of the microgrid2Comprises the following steps:
Figure BDA0002356680520000061
wherein, C1Only considering the operation cost of the micro-grid during economic operation;
Figure BDA0002356680520000062
means that the average daily operating cost is calculated herein, T ═ 24, i.e. 1-24 hours of operating cost; cf(t) converting the fuel consumption cost to every kilowatt-hour at time t; cma(t) converting each electric energy unit to the maintenance and management cost of each kilowatt hour at the moment t; cdep(t) depreciation losses of each power unit at time t; cgov(t) government subsidy amount of clean energy at time t; kCgrid(t) the interaction cost with the large power grid at the moment t, and when electricity is purchased from the large power grid, k is more than 0; when selling electricity to a large power grid, k is less than 0.
Figure BDA0002356680520000063
Wherein, C2Only considering the operation cost (element) of the micro-grid during the environmental protection operation and mainly considering the treatment cost of the polluted gas; j denotes the j-th pollution gas (including CO and CO generated in micro-grid and large-grid)2、SO2And NOxderivatives of alpha)jthe treatment cost (yuan/kw) of the j-th type of contaminated gas, betaijand betamjThe discharge coefficient of the discharge amount of the j-th type polluted gas converted into unit power in a micro-grid power generation unit and a large power grid respectively; pitAnd PmtThe current actual working power (kw) of the micro-grid power generation unit and the large power grid at the moment t is indicated; j is the total number of polluted gases; l is the total number of the micro-grid power generation units
The constraint conditions set in the step 2 are as follows:
and power balance constraint of the power grid:
Figure BDA0002356680520000064
in the formula: pl(t) represents the generated power (kw) of the ith power generation unit which is currently working at the time t; pbat(t) represents the power (kw) emitted by the energy storage device, and if the power (kw) is a negative number, the microgrid charges the energy storage device; pgrid(t) power (kw) for purchasing electricity from the large power grid is represented, and if the power (kw) is negative, the micro-grid sells electricity to the large power grid; pe(t) represents the load power (kw) required by the user at the present time.
And (3) output constraint of each power generation unit in the microgrid:
Plmin(t)≤Pil(t)≤Plmax(t)
in the formula: plmin(t)、PlmaxAnd (t) is the minimum value and the maximum value of the generated power of the ith power generation unit which is currently operated at the time t.
And (3) restricting the climbing rate of the controllable power generation unit in the micro-grid:
Figure BDA0002356680520000065
in the formula: pl，up(t) represents the active power (kw) increased by the ith power generation unit currently operating at time t; pl，up(t-1) represents the active power (kw) increased by the ith power generation unit currently in operation at time t-1; pl，down(t) represents the active power (kw) reduced by the ith power generation unit currently operating at time t; pl，down(t-1) represents the active power (kw) reduced by the ith power generation unit currently operating at time t-1; rl，upAn active power limit (kw) representing the increase of the current working ith power generation unit; rl，downRepresenting the reduced active power limit (kw) of the current working ith power generation unit. The climbing rate of the system can reflect the performance of each controllable power generation unit, and the feasibility of the system is guaranteed by combining with the reality.
Microgrid tie line transmission power constraint:
Pline，min≤Pline≤Pline，max
in the formula: plineThe transmission capacity (kw) of the line between the microgrid and the distribution network is represented, and the upper limit P in a certain range is required to be met according to actual conditionsline，maxAnd a lower limit Pline，minAnd (4) the following steps.
And (3) restricting the charge and discharge capacity of the energy storage device:
SSOCmin≤SSOC(t)≤SSOCmax；
Figure BDA0002356680520000071
in the formula: sSOC(t) represents the state of charge of the energy storage device at time tSatisfy a certain upper and lower limits SSOCmax、SSOCmin；PBESSin(t) and PBESSout(t) respectively represents the charging and discharging power of the energy storage device at the moment t, and a certain upper limit P needs to be metBESSin，max、PBESSout，max。
And (3) electric quantity exchange constraint with a large power grid:
considering the black start capability of the micro-grid, the annual exchange quantity of the micro-grid and the large grid is set to be restricted to be not more than 40% of the annual power consumption.
Figure BDA0002356680520000072
The inertial weight in step 3 is set as:
Figure BDA0002356680520000073
wherein, ω ismaxAnd ωminRespectively the initial value and the final value of the inertia weight, K is the current iteration number, KmaxIs the maximum number of iterations.
The concept of the roulette selection genetic algorithm in step 8 is as follows:
step 1: pBet ═ rand () (pBet is a random number from 0 to 1)
Step 2: fitness f according to annealing algorithmSA(Pid) Calculating cumulative probability
Figure BDA0002356680520000074
And step 3: according to the cumulative probability, selecting the individual optimal position P of the r-th particle meeting the conditionrdReplacing the global optimum position Ppd. The conditions are as follows: comfit (r-1) < pBet < comfit (r)
In step 9, the update speed of each particle is set as follows:
Figure BDA0002356680520000081
the update positions of the particles in step 10 are:
Figure BDA0002356680520000082
the annealing operation of each particle in the step 11 is as follows:
Tnew＝δTold。
example 1
In the embodiment, a higher altitude area of a remote area is taken as an example, an actual micro-grid system is analyzed, and a calculation period is set as one day on average.
1) Setting system parameters: and predicting the photovoltaic power generation and wind power generation power by referring to the local area illumination intensity and the wind speed. Fig. 1 shows a general grid-connected microgrid structure model, comprising a microgrid device: photovoltaic power generation systems (PV for short), Wind power generation systems (WT for short), Micro gas turbines (MT for short), Fuel cells (FC for short), and Diesel generators (DEG for short). In the energy storage device in the model, a Lead-acid batteries (BAT for short) is considered, and the load refers to daily load requirements in life.
The discharge coefficient and the treatment cost corresponding to the type of pollutant discharge are shown in Table 2, and the electricity prices at different local time zones are shown in Table 3.
TABLE 1 micro-grid system for each processing unit
Figure BDA0002356680520000083
TABLE 2 pollutant discharge coefficient and treatment cost
Figure BDA0002356680520000084
Figure BDA0002356680520000091
TABLE 3 local different time zone electricity price table
Figure BDA0002356680520000092
Note: the load peak time period is as follows: 10:00-13:00, 17:00-20: 00;
the load trough period is as follows: 0:00-7:00, 23:00-0: 00;
the rest is a load leveling period.
Based on the parameters, an operation cost objective function C in the operation process of comprehensively considering the economy and the environmental protection of the microgrid is established3。
Figure BDA0002356680520000093
Figure BDA0002356680520000094
Figure BDA0002356680520000095
2) A constraint is set. And (4) predicting the photovoltaic and wind power generation amount and the load of cell users on the same day, wherein PV and WT are operated at the maximum power. The maximum charge-discharge power of the energy storage device is 10kw, and the electric quantity purchased from a large power grid does not exceed 40% of the electric load on the same day. The premise is that the large power grid has sufficient electric quantity, and power failure loss is not considered temporarily.
3) Initializing parameter inertial weight omega and learning factor c1、c2And an annealing speed δ. Through experiments, the parameter omegamaxAnd ωminWhen the values of (A) and (B) are set to 0.9 and 0.4, respectively, the search ability of the particles is better, c1、c2Is a learning factor which respectively represents the learning ability of the particles under the best state of the particles and the best state of the population, and the invention c is proved by experiments1、c22.05 of the sample is taken. In the simulated annealing particle swarm optimization, the simulated annealing process can be as slow as possible to improve the probability of finding a global optimal solution, so the simulated annealing speed delta is 0.95.
Figure BDA0002356680520000096
4) A population is randomly generated containing 600 particles, and the velocity and position of the particles are randomly initialized.
5) Calculating the fitness (x (i)) of each particle i into a fitness function, namely an objective function, f (x) for short, and recording the optimal position P of each (ith) particleidGlobal optimum position PpdFitness f (P)id) And global optimum fitness f (P)pd)。
6) According to the global optimum fitness f (P)pd) The annealing algorithm initial temperature T0 is calculated.
Figure BDA0002356680520000101
7) Note fSA(x) The function is a function for calculating the fitness of the annealing algorithm, and each P is at the current temperature TidFitness of annealing algorithm
Figure BDA0002356680520000102
8) Using the idea of selecting genetic algorithms by roulette, from an individual optimum position PidOne of the alternative global optimum positions P is selectedpdIs denoted by Prd。
9) Will PpdBy substitution of PrdSubstituting a particle swarm formula, and updating the speed of each particle as follows:
Figure BDA0002356680520000103
10) then calculating the fitness of each particle, and updating the optimal position P of each particleidAnd a population global optimum position Ppd。
11) And (3) carrying out annealing operation:
Tnew＝δTold
12) the dimension of the search space set by the invention is 48; the maximum number of iterations is 300. After the termination condition is met, the output calculation result is 37453.7 yuan, and about 3000 yuan is saved compared with the original algorithm.
Fig. 2 shows the operation of each micro power source in the particle swarm optimization distribution mode. FIG. 3 shows the operation of each micro-power source in the simulated annealing particle swarm algorithm distribution mode. Compared with the original method, the method has the advantages that the cost is lower, the power supply of a public power grid is not depended, and the independence of the micro-grid is increased. Indicating the effectiveness of the present invention.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can make several modifications without departing from the principle of the present invention, therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (10)

1. A micro-grid optimization method based on simulated annealing particle swarm optimization is characterized by comprising the following steps:
step 1: establishing an objective function C comprehensively considering the operating performance of the microgrid3；
Step 2: setting constraint conditions;
and step 3: initializing parameter inertial weight omega and learning factor c1、c2And an annealing speed δ;
and 4, step 4: randomly generating a population, wherein the population comprises m particles, and randomly initializing the speed and the position of the particles;
and 5: calculating the fitness (x (i)) of each particle i, f (x) for short, and recording the optimal position P of the ith particleidGlobal optimum position PpdFitness f (P)id) And global optimum fitness f (P)pd)；
Step 6: according to the global optimum fitness f (P)pd) Calculating to obtain the initial temperature T of the annealing algorithm0；
And 7: note fSA(x) The function is a function for calculating the fitness of the annealing algorithm, and each P is at the current temperature TidFitness of annealing algorithm
Figure FDA0002356680510000011
Wherein n is the total number of particles;
and 8: selecting genetic algorithm from individual optimum position P by rouletteidOne of the alternative global optimum positions P is selectedpdIs denoted by Prd；
And step 9: will PpdBy substitution of PrdSubstituting a particle swarm formula, and updating the speed of each particle;
step 10: then calculating the fitness of each particle, and updating the optimal position P of each particleidAnd a population global optimum position Ppd；
Step 11: carrying out annealing operation;
step 12: and judging whether a termination condition is met, if so, stopping searching, outputting a calculation result to obtain a global optimal solution of the target function, and if not, turning to the step 7.
2. The method for optimizing the microgrid based on the simulated annealing particle swarm algorithm according to claim 1, characterized in that the operation cost C of the microgrid during economic operation is only considered in the objective function defined in the step 11The operation cost when only considering the environmental protection operation of the micro-grid is C2。
3. The method for optimizing a microgrid based on a simulated annealing particle swarm algorithm according to claim 2, characterized in that,
Figure FDA0002356680510000012
wherein, C1Only considering the operation cost of the micro-grid during economic operation;
Figure FDA0002356680510000013
the average daily running cost is calculated, and T is 24 hours, namely the running cost of 1-24 hours; cf(t) converting the fuel consumption cost to every kilowatt-hour at time t; cma(t) time of each electricityThe maintenance and management cost per kilowatt hour can be converted by a unit; cdep(t) depreciation losses of each power unit at time t; cgov(t) government subsidy amount of clean energy at time t; kCgrid(t) the interaction cost with the large power grid at the moment t, and when electricity is purchased from the large power grid, k is more than 0; when selling electricity to a large power grid, k is less than 0.
4. The method for optimizing a microgrid based on a simulated annealing particle swarm algorithm according to claim 2, characterized in that,
Figure FDA0002356680510000021
wherein, C2only considering the operation cost (element) of the micro-grid during the environmental protection operation and mainly considering the treatment cost of the polluted gas, j represents the j-th type polluted gas, alphajthe treatment cost of the j-th type polluted gas, betaijand betamjThe discharge coefficient of the discharge amount of the j-th type polluted gas converted into unit power in a micro-grid power generation unit and a large power grid respectively; pitAnd PmtThe current actual working power of the micro-grid power generation unit and the large power grid at the moment t is indicated; j is the total number of polluted gases; and L is the total number of the micro-grid power generation units.
5. The method for optimizing the microgrid based on the simulated annealing particle swarm algorithm according to claim 1, wherein the constraint conditions set in the step 2 comprise power balance constraint of the microgrid, output constraint of each power generation unit in the microgrid, climbing rate constraint of controllable power generation units in the microgrid, transmission power constraint of a microgrid connecting line, charging and discharging capacity constraint of an energy storage device and electric quantity constraint of exchange with a large power grid.
6. The method for optimizing the microgrid based on the simulated annealing particle swarm optimization of claim 1, wherein the setting formula of the inertia weight ω is as follows:
Figure FDA0002356680510000022
wherein, ω ismaxAnd ωminRespectively the initial value and the final value of the inertia weight, K is the current iteration number, KmaxIs the maximum number of iterations.
7. The method of claim 1, wherein the roulette selection genetic algorithm in step 8 comprises the following steps:
step 1: pBet ═ rand (), pBet being a random number from 0 to 1;
step 2: fitness f according to annealing algorithmSA(Pid) Calculating cumulative probability
Figure FDA0002356680510000023
And step 3: according to the cumulative probability, selecting the individual optimal position P of the r-th particle meeting the conditionrdReplacing the global optimum position PpdThe conditions are as follows: comfit (r-1) < pBet < comfit (r).
8. The method for optimizing the microgrid based on the simulated annealing particle swarm algorithm of claim 1, wherein the updating speed of each particle in the step 9 is set as follows:
Figure FDA0002356680510000024
wherein:
Figure FDA0002356680510000025
-the d-dimensional component of the (k + 1) th iterative particle i airspeed vector;
Figure FDA0002356680510000026
-the d-dimensional component of the k-th iterative particle i's airspeed vector;
c1,c2accelerationA constant for adjusting a learning maximum step length;
r1,r2two random functions, over a range of [0, 1 ]]For increasing search randomness;
omega-inertial weight, non-negative, used to adjust the search range for solution space;
Figure FDA0002356680510000031
-the d-dimensional component of the location vector of the particle i at the k-th iteration;
Figure FDA0002356680510000032
-individual optimal positions of the kth iteration particle i;
Figure FDA0002356680510000033
-global optimal position of the kth iteration particle i.
9. The method for optimizing the microgrid based on the simulated annealing particle swarm algorithm of claim 1, wherein the update positions of the particles in the step 10 are as follows:
Figure FDA0002356680510000034
Figure FDA0002356680510000035
-the d-dimensional component of the location vector of the particle i at the k-th iteration;
Figure FDA0002356680510000036
-the d-dimensional component of the location vector of the (k-1) th iteration particle i;
Figure FDA0002356680510000037
the (k-1) th iteration particlei d-th dimension component of the airspeed vector.
10. The method for optimizing the microgrid based on the simulated annealing particle swarm algorithm of claim 1, wherein the annealing operation of each particle in the step 11 is as follows:
Tnew＝δTold
Told-the annealing temperature at the previous moment;
Tnew-an annealing temperature after iteration;
delta. annealing speed.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112183841A (en) * 2020-09-23 2021-01-05 上海电机学院 Optimized dispatching method of micro-grid containing electric automobile based on simulated annealing algorithm
CN113361146A (en) * 2021-07-21 2021-09-07 国网江西省电力有限公司供电服务管理中心 Improved particle swarm optimization-based manganese-copper shunt structure parameter optimization method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112183841A (en) * 2020-09-23 2021-01-05 上海电机学院 Optimized dispatching method of micro-grid containing electric automobile based on simulated annealing algorithm
CN113361146A (en) * 2021-07-21 2021-09-07 国网江西省电力有限公司供电服务管理中心 Improved particle swarm optimization-based manganese-copper shunt structure parameter optimization method

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