CN104036320A - Dynamic economical dispatch method for microgrid system on the basis of improved particle swarm optimization - Google Patents

Abstract

The invention relates to a dynamic economical dispatch method for a microgrid system on the basis of an improved particle swarm optimization. The dynamic economical dispatch method for the microgrid system on the basis of the improved particle swarm optimization comprises the following steps: setting a particle swarm optimization; generating an initial particle swarm; setting the upper and lower limit constraint of the speed of particles; determining the adaptive values of the particles; comparing the adaptive values of the particles, finding the local optimal value and the position of each particle, and also finding a particle which achieves a global optimal value and the position of the particle; updating the position and the speed of each particle; judging whether the position and the speed of each particle are out of limit; determining the transmitting power of PCC (point of common coupling) serving as a swing bus; carrying out acceleration processing to the speed of the particles by an adaptive algorithm; and if the speed of the particles achieves iterations, stopping iteration, and obtaining a final result. According to the dynamic economic dispatch method for the microgrid system on the basis of the improved particle swarm optimization, the speed of the particles is regulated, a targeted search can be carried out when a search becomes a local search; meanwhile, the PCC used for connecting a microgrid with a major network is used as the swing bus; and the PCC is used for balancing when on-line load and the power output of a distributed power supply are not matched.

Description

A kind of micro-grid system dynamic economic dispatch method based on improving particle cluster algorithm

Technical field

The present invention relates to a kind of economic load dispatching method of micro-grid system optimization operation, be specifically related to a kind of micro-grid system dynamic economic dispatch method based on improving particle cluster algorithm.

Background technology

The operation of unit needs cost, and the operation of micro-grid system will be considered performance driving economy, unit difference, and cost function difference, the exert oneself financial cost of scheme of difference is also different.The object of economic load dispatching is to optimize exerting oneself of each unit in some cycles, goes out force level to obtain optimum, makes cost minimization.Exerting oneself of period also relates to the climbing capacity constraint of unit before and after unit, and this is closely related with exerting oneself of unit front and back period, so need to dispatch the dynamic economy of micro-grid system.Meanwhile, because unit capacity in net is limited, microgrid need be connected with major network by PCC point, and in microgrid, distributed power source cannot be supplied with while load, to transmission power in microgrid, to ensure the equilibrium of supply and demand of electric power.Micro-grid system dynamic economic dispatch is a part for power system security economical operation, its objective is the method for operation of determining unit under various security constraints and quality of power supply requirement condition meeting, and the total operating cost of system is minimized.

The variable that dynamic economic dispatch relates to is many, need to consider the constraint condition of equation and inequality simultaneously, and general optimized algorithm is difficult to reach calculation requirement.Problem dimension is higher, constraint condition is strict, causes the corresponding target function value of its each feasible solution comparatively approaching, in the time of the particle cluster algorithm of the standard of use, easily causes search to be absorbed in local optimum, and Premature Convergence, stagnates the search to globally optimal solution.

The introducing of PCC point (common coupling node of user's side and system side), balance node can be regarded as trend calculating time, after distributed power source is exerted oneself and determined in microgrid, by it, microgrid internal loading is carried out the strong constraint problem of the balancing the load in balance solution microgrid economic load dispatching.

The method that solves Premature Convergence is mainly the diversity that ensures particle, avoids the iteration later stage, and population individuality is similar, is absorbed in Local Search.As in conjunction with genetic algorithm to the position of particle intersect, variation etc. to be to ensure the diversity of particle.But because the constraint condition of dynamic economic dispatch is comparatively strict, solution space is limited in very little scope, and this processing ease to particle position is out-of-limit, specific aim is also strong not simultaneously, has some limitations for dynamic economic dispatch problem.

Summary of the invention

For the deficiencies in the prior art, the object of this invention is to provide a kind of micro-grid system dynamic economic dispatch method based on improving particle cluster algorithm, the present invention improves the iterative formula of particle cluster algorithm, add adaptive approach, so that speed is adjusted, and according to constraint condition, particle is adjusted, not out-of-limit to ensure particle.

The present invention adopts the particle cluster algorithm after improvement to solve the dynamic economic dispatch of micro-grid system, to obtain the force level that of each unit day part after optimization.Calculate by trend the power that PCC is ordered simultaneously, realize the equilibrium of supply and demand of microgrid internal loading.

The object of the invention is to adopt following technical proposals to realize:

The invention provides a kind ofly based on improving the micro-grid system dynamic economic dispatch method of particle cluster algorithm, its improvements are, described method comprises the steps:

A, the parameter of particle cluster algorithm is set;

B, generation primary group;

C, the constraint of the speed bound of particle in micro-grid system dynamic economic dispatch is set;

D, determine the adaptive value of particle;

The adaptive value of E, comparison particle, finds out local optimum and the position thereof of each particle, and reaches particle and the position thereof of global optimum;

F, according to local optimum and all optimal value upgrade position and the speed of each particle;

G, according to constraint condition, and the bound of particle rapidity constraint judge the position of particle and speed whether out-of-limit, if out-of-limit, the position of particle and speed are adjusted in restriction range;

H, the method for calculating by trend, calculate the transmission power of ordering as the PCC of balance node;

I, adopt adaptive approach, to the particle rapidity processing of raising speed;

If J reaches iterations, stop iteration, obtain final result.

Further, in described steps A, the required parameters of particle cluster algorithm is set, described parameter comprises weight factor, the study factor, population scale number and iterations, and weight factor is got ω _max=0.9, ω _min=0.4, the study factor is got c ₁=c ₂=3.05, population scale and iterations, according to actual conditions setting, comprise that being set to population scale counts N=40, iterations C=500.

Further, in described step B, share at random the force level that of each unit according to the load level of day part in the computing interval, repeat this step according to population scale number, obtain primary group; Comprise:

Random generate the random array that line number is unit number, columns hop count while being, the element of this array is positive number, and each column element sum is one; To load the period and be multiplied by corresponding row and can obtain primary, repeat this operation, obtain whole primary group, generate as follows for m particle:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(1\right);$

P _i,t＝a _i,t·P _D,t (2)；

a _1,t+a _2,t+…+a _i,t+…a _M,t＝1 (3)；

Wherein: T represents the time hop count of economic load dispatching, be one day 24 hours, taking per hour as a section, totally 24 periods, M is the unit number that participates in dynamic economic dispatch; a _i,trepresenting the i platform proportional element of unit t period, is random generation, meets (3) formula, therefore (3) formula represents that for the proportional element sum of all M platforms of t period be 1, P _d,tbe the size of the load of t period, adopt same procedure to generate other particles.

Further, in described step C, determine the speed bound of each particle according to the bound constraint of each unit, the difference that the bound of particle rapidity is exerted oneself according to unit bound is reduced, reduction scope is 1%, be in particle rapidity, be limited to the unit output upper limit deduct the lower limit of exerting oneself difference 1%, under particle rapidity, be limited to unit output lower limit deduct the upper limit of exerting oneself difference 1%.

Further, in described step D, according to the primary group who obtains in step B, determine the adaptive value of particle according to Electrical Power System Dynamic economic load dispatching objective function, micro-grid system dynamic economic dispatch objective function is:

$f=\underset{t}{\overset{T}{\mathrm{\Σ}}}\underset{i}{\overset{M}{\mathrm{\Σ}}}f\left(P_{\mathrm{it}}\right)---\left(4\right);$

Wherein: P _i,tbe exerting oneself of i platform unit t period, the time hop count that T is dynamic economic dispatch, M is the unit number that participates in dynamic economic dispatch;

Wherein the cost function of diesel engine unit is as follows:

f(P _it)＝a _i·P _it ²+b _i·P _it+c _i (5)；

In formula, a _i, b _i, c _ifor cost coefficient;

Fuel cell, miniature gas turbine cost function are as follows:

$f\left(P_{i,t}\right)=(C_i^f+C_i^r)P_{i,t}---\left(6\right);$

In formula, be respectively corresponding fuel cost coefficient, maintenance cost coefficient;

Adaptive value adopts target function value,

$f_a=f=\underset{t}{\overset{T}{\mathrm{\Σ}}}\underset{i}{\overset{M}{\mathrm{\Σ}}}f\left(P_{\mathrm{it}}\right)---\left(7\right);$

Using micro-grid system dynamic economic dispatch objective function result of calculation as particle adaptive value, target function value is less, and adaptive value is less, and the fitness of particle is higher.

Further, the constraint of the bound of micro-grid system dynamic economic dispatch comprises power-balance constraint, the constraint of unit output bound and unit ramp loss;

Wherein power-balance constraint represents by following expression formula:

$\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{P}_{i,t}+P_{\mathrm{PCC},t}={P_t}^D+{P_t}^{\mathrm{loss}}---\left(8\right);$

Wherein: P _{pCC, t}for the power to microgrid input that PCC is ordered, P _t ^dbe the payload of t period, P _t ^lossthe network loss size of t period;

The constraint of unit output bound represents by following expression formula:

P _i ^min≤P _i,t≤P _i ^max (9)；

Wherein: P _i ^minand P _i ^maxrepresent respectively i the platform lower limit of exerting oneself and the upper limit of unit t period;

Unit ramp loss represents by following expression formula:

$R_i^d\mathrm{\Δt}\≤P_{i,t}-P_{i,t-1}\≤R_i^u\mathrm{\Δt}---\left(10\right);$

Wherein: for downward Ramp Rate, it is negative value; for Ramp Rate upwards, on the occasion of; Δ t represents two time intervals between scheduling slot.

Further, in described step e, the adaptive value of more each particle, determines the highest particle of particle fitness, finds the historical optimal value of each particle, i.e. local optimum, and particle position, and global optimum and particle position; If iteration for the first time, finds global optimum and corresponding particle position;

In particle cluster algorithm, the population number of population is comprised number of particles, and the each individuality in population the inside is all called a particle; Each feasible solution is all particles in population, and each particle has two attributes, and displacement and speed are all expressed as a matrix-vector, are shown below:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(11\right);$

$V_m=\left[\begin{array}{ccccc}{V}_{\mathrm{1,1}}& ...& V_{1,t}& ...& V_{1,T}\\ V_{i,1}& ...& V_{i,t}& ...& V_{i,T}\\ ...& ...& ...& ...& ...\\ V_{M,1}& ...& V_{M,t}& ...& V_{M,T}\end{array}\right]---\left(12\right);$

Wherein: P _mbe the displacement of m particle, P _i,tbe exerting oneself of i platform unit t period, i.e. the position of particle; V _mthe speed of m particle, V _i,tit is the correction of exerting oneself of the i platform unit t period of correspondence; T is the time hop count of dynamic economic dispatch; M is the unit number that participates in dynamic economic dispatch; In micro-grid system dynamic economic dispatch, the position of particle represents exerting oneself of each unit day part, and line number represents unit number, hop count when columns represents.

Further, in described step F, the local optimum obtaining according to step e and particle position, and all optimal value and particle position, according to more speed and the position of new particle of following expression formula:

$V_m^{k+1}=\mathrm{\ω}\·V_m^k+c_1\·r_1\·({\mathrm{pbest}}_m^k-P_m^k)+c_2\·r_2\·({\mathrm{gbest}}^k-P_m^k)+{\mathrm{\ΔV}}_m^k---\left(13\right);$

$P_m^{k+1}=P_m^k+V_m^{k+1}+{\mathrm{\ΔP}}_m^k---\left(14\right);$

Wherein: represent respectively the adjustment amount of speed and position; be the speed in the k generation of m particle, the speed in the k+1 generation of m particle; be the k generation of m particle, for the k+1 generation of m particle; be the historical optimal value of m particle, gbest ^kit is the global optimum of the k time iteration; ω is inertial factor, for weighing global search and the local search ability of particle cluster algorithm; ω value is more greatly more easy to algorithm and increases search space, and the more little local optimal searching of more easily carrying out of value adopts self-adaptation to adjust the mode of inertial factor, that is:

$\mathrm{\ω}={\mathrm{\ω}}_{\max }-\frac{{\mathrm{\ω}}_{\max }-{\mathrm{\ω}}_{\min }}{C}k---\left(15\right);$

Wherein: ω _maxget 0.9, ω _minget the maximum times that 0.4, C is iteration, k is current iterations; c ₁, c ₂for the study factor, be respectively and control the maximum step-length of particle to individual optimum and global optimum's locality flight; Get c ₁=c ₂=3.05;

for adjusting out-of-limit particle, when particle is out-of-limit, according to (9), (10), (11) formula, according to following rule, particle is adjusted:

1) according to speed bound, constraint is adjusted unit output, on unit, in limited time, is exerted oneself and is limited to the upper limit, and unit is more lower in limited time, is exerted oneself and is limited to lower limit;

2) according to climbing constraint, unit output is adjusted, when unit is upwards climbed, if out-of-limit, unit output is limited to the upwards upper limit of climbing; When unit is climbed, if out-of-limit, unit output is limited to the lower limit of downward climbing downwards;

3) according to the unit output after adjusting, carry out trend calculating, obtain the transmission power that PCC is ordered.

Further, for particle rapidity, be limited within the scope of constraint of velocity, constraint of velocity formula is as follows:

$V_m^k\left[i\right]\left[j\right]=\left\{\begin{array}{cc}{V}_m^k{\left[i\right]\left[j\right]}_{\max },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]>V_m^k{\left[i\right]\left[j\right]}_{\max }\\ V_m^k{\left[i\right]\left[j\right]}_{\min },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]<V_m^k{\left[i\right]\left[j\right]}_{\min }\end{array}\right.---\left(16\right);$

Wherein: for the i of particle rapidity is capable, the element of j row, for the i of particle rapidity is capable, the maximal value defined in the element of j row, for the i of particle rapidity is capable, the minimum value defined in the element of j row; The maximum of particle rapidity, minimum value are according to following formula setting:

$V_m^k{\left[i\right]}_{\max }=({P_i}^{\max }-{P_i}^{\min })/100---\left(17\right);$

$V_m^k{\left[i\right]}_{\min }=({P_i}^{\min }-{P_i}^{\max })/100---\left(18\right);$

Wherein: be respectively the upper and lower bound of corresponding i platform unit speed in particle, will be extended to and the transversal vector of period corresponding length, obtain speed limit and lower limit that particle i is capable.

Further, in described step H, according to not out-of-limit particle position after adjusting in step G, be exerting oneself of each unit day part, obtain after the exerting oneself of each unit day part, the method for calculating by trend is calculated the power input to microgrid of PCC point day part, adopt Niu Lafa, PCC point is considered as to the balance node in trend calculating, calculates exerting oneself of its day part, thereby obtain the active power of PCC point to micro-grid system input.

Further, in described step I, according to obtaining global optimum in step e, the global optimum of twice relatively, adopts adaptive approach, according to the order of magnitude of the difference of twice global optimum, particle rapidity is adjusted, and absolute value is larger, and adjustment degree is larger; Carry out as follows:

In particle cluster algorithm, along with the increase of iterations, can be absorbed in local optimum, now the speed of particle can reduce thereupon, thus particle rapidity is adjusted, that is:

v ₁＝v ₀+Δv (19)；

Wherein: v ₀, v ₁be respectively the particle rapidity before and after adjusting, Δ v is the speed amount of adjusting, and adopts adaptive algorithm when adjustment, and adaptive algorithm formula is as follows:

$\mathrm{\&Delta;v}=\left\{\begin{array}{c}0,\mathrm{\&lambda;}>0.01\\ v_{\mathrm{ini}}\&CenterDot;r_3,0.001<\mathrm{\&lambda;}\&le;0.01\\ v_{\mathrm{ini}}\&CenterDot;r_4,0<\mathrm{\&lambda;}\&le;0.001\end{array}\right.---\left(20\right);$

Wherein: v _inifor initial velocity set in particle cluster algorithm, r ₃for-0.1 to 0.1 random number, r ₄for-0.01 to 0.01 random number, for random number r ₃, r ₄need be with setting according to reality, λ calculates according to following formula:

$\mathrm{\&lambda;}=\frac{|{f_{\mathrm{best}}}^k-{f_{\mathrm{best}}}^{k-1}|}{{f_{\mathrm{best}}}^k}---\left(21\right);$

Wherein: f _best ^kbe the global optimum position of k for particle, f _best ^k-1twice global optimum difference relative scale before and after k-1 is for the position λ of global optimum of particle.

Further, in described step J, the iterative algorithm of having set according to steps A, judge whether iterations reaches: if reach iterations C=500, stop calculating, obtain particle global optimum and be final particle position, this particle position is each unit exerting oneself at day part, thereby calculate net result, net result comprises going out force level and calculating the unit operation total expenses in economic cycle of each unit day part; If do not reach iterations, turn back to step D, continue to calculate.

Compared with the prior art, the beneficial effect that the present invention reaches is:

Prior art is in search procedure, search one period of stage, when iterative process is easily absorbed in Local Search, the basic vanishing of particle rapidity now, be unfavorable for searching in maximum scope, therefore in the present invention, particle rapidity is raised speed, to jump out Local Search, in speed-raising process, adopt adaptive approach, the adaptive value size calculating according to twice of front and back iteration global optimum, judge whether particle rapidity to raise speed, can jump out Local Search to ensure particle, also can ensure the function of search among a small circle of particle, and adopt the method for regulating the speed can go more targetedly to carry out search, thereby carry out better optimizing.

In prior art, in the time processing balancing the load constraint, mainly adopt the method for penalty function, in the time not meeting balancing the load constraint, add corresponding penalty term in adaptive value, reduce so that do not meet the particle adaptive value of balancing the load, thereby eliminate this part particle, but can cause like this decline of computing velocity, PCC point is not considered as to the technology that the balance node in trend calculating is carried out balancing the load adjustment, and in the present invention, process balancing the load when constraint, balance node using PCC point when trend is calculated, in the definite situation of other unit outputs, the power input of orderring by PCC realizes the balance of microgrid load, be conducive to meet fast the equality constraint of balancing the load, thereby raising computing velocity.

Brief description of the drawings

Fig. 1 is the process flow diagram of the micro-grid system dynamic economic dispatch method based on improvement particle cluster algorithm provided by the invention;

Fig. 2 is three class daily load proportional curves provided by the invention, wherein: for industrial load, for Commercial Load, for resident load;

Fig. 3 is algorithm iteration process comparison diagram provided by the invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.

The process flow diagram of the micro-grid system dynamic economic dispatch method based on improvement particle cluster algorithm provided by the invention as shown in Figure 1, comprises the steps:

A, the required parameters of particle cluster algorithm is set, described parameter comprises weight factor, the study factor, population scale number and iterations, and weight factor is got ω _max=0.9, ω _min=0.4, the study factor is got c ₁=c ₂=3.05, population scale and iterations, according to actual conditions setting, comprise that being set to population scale counts N=40, iterations C=500.

B, share at random the force level that of each unit according to the load level of day part in the computing interval, repeat this step according to population scale number, obtain primary group; Comprise:

Random generate the random array that line number is unit number, columns hop count while being, the element of this array is positive number, and each column element sum is one; To load the period and be multiplied by corresponding row and can obtain primary, repeat this operation, obtain whole primary group, generate as follows for m particle:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(1\right);$

P _i,t＝a _i,t·P _D,t (2)；

a _1,t+a _2,t+…+a _i,t+…a _M,t＝1 (3)；

Wherein: T represents the time hop count of economic load dispatching, be one day 24 hours, taking per hour as a section, totally 24 periods, M is the unit number that participates in dynamic economic dispatch; a _i,trepresenting the i platform proportional element of unit t period, is random generation, meets (3) formula, therefore (3) formula represents that for the proportional element sum of all M platforms of t period be 1, P _d,tbe the size of the load of t period, adopt same procedure to generate other particles.

C, the constraint of the speed bound of particle in micro-grid system dynamic economic dispatch is set; Determine the speed bound of each particle according to the bound constraint of each unit, the difference that the bound of particle rapidity is exerted oneself according to unit bound is reduced, reduction scope is 1%, be in particle rapidity, be limited to the unit output upper limit deduct the lower limit of exerting oneself difference 1%, under particle rapidity, be limited to unit output lower limit deduct the upper limit of exerting oneself difference 1%.

D, determine the adaptive value of particle: according to the primary group who obtains in step B, determine the adaptive value of particle according to Electrical Power System Dynamic economic load dispatching objective function, micro-grid system dynamic economic dispatch objective function is:

$f=\underset{t}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i}{\overset{M}{\mathrm{\&Sigma;}}}f\left(P_{\mathrm{it}}\right)---\left(4\right);$

Wherein: P _i,tbe exerting oneself of i platform unit t period, the time hop count that T is dynamic economic dispatch, M is the unit number that participates in dynamic economic dispatch;

Wherein the cost function of diesel engine unit is as follows:

f(P _it)＝a _i·P _it ²+b _i·P _it+c _i (5)；

In formula, a _i, b _i, c _ifor cost coefficient;

Fuel cell, miniature gas turbine cost function are as follows:

$f\left(P_{i,t}\right)=(C_i^f+C_i^r)P_{i,t}---\left(6\right);$

In formula, be respectively corresponding fuel cost coefficient, maintenance cost coefficient;

Using target function value as adaptive value, corresponding adaptive value is:

$f_a=f=\underset{t}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i}{\overset{M}{\mathrm{\&Sigma;}}}f\left(P_{\mathrm{it}}\right)---\left(7\right);$

Using micro-grid system dynamic economic dispatch objective function result of calculation as particle adaptive value, target function value is less, and adaptive value is less, and the fitness of particle is higher.

The bound constraint of micro-grid system dynamic economic dispatch comprises power-balance constraint, the constraint of unit output bound and unit ramp loss;

Wherein power-balance constraint represents by following expression formula:

$\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{P}_{i,t}+P_{\mathrm{PCC},t}={P_t}^D+{P_t}^{\mathrm{loss}}---\left(8\right);$

Wherein: P _{pCC, t}represent the power input of PCC point t period, P _t ^dbe the payload of t period, P _t ^lossthe network loss size of t period;

The constraint of unit output bound represents by following expression formula:

P _i ^min≤P _i,t≤P _i ^max (9)；

Wherein: P _i ^minand P _i ^maxrepresent respectively i the platform lower limit of exerting oneself and the upper limit of unit t period;

Unit ramp loss represents by following expression formula:

$R_i^d\mathrm{\&Delta;t}\&le;P_{i,t}-P_{i,t-1}\&le;R_i^u\mathrm{\&Delta;t}---\left(10\right);$

Wherein: for downward Ramp Rate, it is negative value; for Ramp Rate upwards, on the occasion of; Δ t represents two time intervals between scheduling slot.

The adaptive value of E, more each particle, determines the highest particle of particle fitness, finds the historical optimal value of each particle, i.e. local optimum, and particle position, and global optimum and particle position; If iteration for the first time, finds global optimum and corresponding particle position;

In particle cluster algorithm, the population number of population is comprised number of particles, and the each individuality in population the inside is all called a particle; Each feasible solution is all particles in population, and each particle has two attributes, and displacement and speed are all expressed as a matrix-vector, are shown below:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(11\right);$

$V_m=\left[\begin{array}{ccccc}{V}_{\mathrm{1,1}}& ...& V_{1,t}& ...& V_{1,T}\\ V_{i,1}& ...& V_{i,t}& ...& V_{i,T}\\ ...& ...& ...& ...& ...\\ V_{M,1}& ...& V_{M,t}& ...& V_{M,T}\end{array}\right]---\left(12\right);$

Wherein: P _mbe the displacement of m particle, P _i,tbe exerting oneself of i platform unit t period, i.e. the position of particle; V _mv _mthe speed of individual particle, V _i,tit is the correction of exerting oneself of the i platform unit t period of correspondence; T is the time hop count of dynamic economic dispatch; M is the unit number that participates in dynamic economic dispatch; In micro-grid system dynamic economic dispatch, the position of particle represents exerting oneself of each unit day part, and line number represents unit number, hop count when columns represents.

F, the local optimum obtaining according to step e and particle position, and all optimal value and particle position, according to more speed and the position of new particle of following expression formula:

$V_m^{k+1}=\mathrm{\&omega;}\&CenterDot;V_m^k+c_1\&CenterDot;r_1\&CenterDot;({\mathrm{pbest}}_m^k-P_m^k)+c_2\&CenterDot;r_2\&CenterDot;({\mathrm{gbest}}^k-P_m^k)+{\mathrm{\&Delta;V}}_m^k---\left(13\right);$

$P_m^{k+1}=P_m^k+V_m^{k+1}+{\mathrm{\&Delta;P}}_m^k---\left(14\right);$

Wherein: represent respectively the adjustment amount of speed and position; be the speed in the k generation of m particle, the speed in the k+1 generation of m particle; be the k generation of m particle, for the k+1 generation of m particle; be the historical optimal value of m particle, gbest ^kit is the global optimum of the k time iteration; ω is inertial factor, for weighing global search and the local search ability of particle cluster algorithm; ω value is more greatly more easy to algorithm and increases search space, and the more little local optimal searching of more easily carrying out of value adopts self-adaptation to adjust the mode of inertial factor, that is:

$\mathrm{\&omega;}={\mathrm{\&omega;}}_{\max }-\frac{{\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{\min }}{C}k---\left(15\right);$

Wherein: ω _maxget 0.9, ω _minget the maximum times that 0.4, C is iteration, k is current iterations; c ₁, c ₂for the study factor, be respectively and control the maximum step-length of particle to individual optimum and global optimum's locality flight; Get c ₁=c ₂=3.05;

for adjusting out-of-limit particle, when particle is out-of-limit, according to (9), (10), (11) formula, according to following rule, particle is adjusted:

1) according to speed bound, constraint is adjusted unit output, on unit, in limited time, is exerted oneself and is limited to the upper limit, and unit is more lower in limited time, is exerted oneself and is limited to lower limit;

2) according to climbing constraint, unit output is adjusted, when unit is upwards climbed, if out-of-limit, unit output is limited to the upwards upper limit of climbing; When unit is climbed, if out-of-limit, unit output is limited to the lower limit of downward climbing downwards;

3) according to the unit output after adjusting, carry out trend calculating, obtain the transmission power that PCC is ordered.

G, according to constraint condition, and the bound of particle rapidity constraint judge the position of particle and speed whether out-of-limit, if out-of-limit, the position of particle and speed are adjusted in restriction range;

For particle rapidity, be limited within the scope of constraint of velocity, constraint of velocity formula is as follows:

$V_m^k\left[i\right]\left[j\right]=\left\{\begin{array}{cc}{V}_m^k{\left[i\right]\left[j\right]}_{\max },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]>V_m^k{\left[i\right]\left[j\right]}_{\max }\\ V_m^k{\left[i\right]\left[j\right]}_{\min },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]<V_m^k{\left[i\right]\left[j\right]}_{\min }\end{array}\right.---\left(16\right);$

Wherein: for the i of particle rapidity is capable, the element of j row, for the i of particle rapidity is capable, the maximal value defined in the element of j row, for the i of particle rapidity is capable, the minimum value defined in the element of j row; The maximum of particle rapidity, minimum value are according to following formula setting:

$V_m^k{\left[i\right]}_{\max }=({P_i}^{\max }-{P_i}^{\min })/100---\left(17\right);$

$V_m^k{\left[i\right]}_{\min }=({P_i}^{\min }-{P_i}^{\max })/100---\left(18\right);$

Wherein: be respectively the upper and lower bound of corresponding i platform unit speed in particle, will be extended to and the transversal vector of period corresponding length, obtain speed limit and lower limit that particle i is capable.

H, the method for calculating by trend, calculate the transmission power of ordering as the PCC of balance node: according to not out-of-limit particle position after adjusting in step G, i.e. and exerting oneself of each unit day part, the method for calculating by trend is calculated exerting oneself of PCC point day part

I, adopt adaptive approach, to the particle rapidity processing of raising speed:

According to obtaining global optimum in step e, before and after comparing, the global optimum of twice, adopts adaptive approach, according to the order of magnitude of the difference of twice global optimum, particle rapidity is adjusted, and absolute value is larger, and adjustment degree is larger; Carry out as follows:

In particle cluster algorithm, along with the increase of iterations, may be absorbed in local optimum, now the speed of particle can reduce thereupon, thus particle rapidity is adjusted, that is:

v ₁＝v ₀+Δv (19)；

Wherein: v ₀, v ₁be respectively the particle rapidity before and after adjusting, Δ v is the speed amount of adjusting, and adopts adaptive algorithm when adjustment, and adaptive algorithm formula is as follows:

$\mathrm{\&Delta;v}=\left\{\begin{array}{c}0,\mathrm{\&lambda;}>0.01\\ v_{\mathrm{ini}}\&CenterDot;r_3,0.001<\mathrm{\&lambda;}\&le;0.01\\ v_{\mathrm{ini}}\&CenterDot;r_4,0<\mathrm{\&lambda;}\&le;0.001\end{array}\right.---\left(20\right);$

Wherein: v _inifor initial velocity set in particle cluster algorithm, r ₃for-0.1 to 0.1 random number, r ₄for-0.01 to 0.01 random number, for random number r ₃, r ₄need be with setting according to reality, λ calculates according to following formula:

$\mathrm{\&lambda;}=\frac{|{f_{\mathrm{best}}}^k-{f_{\mathrm{best}}}^{k-1}|}{{f_{\mathrm{best}}}^k}---\left(21\right);$

Wherein: f _best ^kbe the global optimum position of k for particle, f _best ^k-1twice global optimum difference relative scale before and after k-1 is for the position λ of global optimum of particle.

If J reaches iterations, stop iteration, obtain final result: the iterative algorithm of having set according to steps A, judge whether iterations reaches: if reach iterations C=500, stop calculating, obtain particle global optimum and be final particle position, this particle position is each unit exerting oneself at day part, thereby calculate net result, net result comprises going out force level and calculating the unit operation total expenses in economic cycle of each unit day part; If do not reach iterations, turn back to step D, continue to calculate.

Embodiment

The present embodiment adopts the micro-grid system of 17 nodes, and its structural parameters and load data are as shown in table 1, and in table, last classifies the load of a certain typical case's day as, represents the peak load of end-node, and the power factor of its each node load is 0.85.The daily load proportional curve figure (with the number percent of this day peak load) of industrial load, Commercial Load, resident load as shown in Figure 2.In microgrid, distributed power source parameter is as shown in table 2, and the node that is connected with distributed power source in microgrid is 5,6,7,8,9,12, and unit 1,2 is diesel engine unit, and unit 3,4 is fuel cell, and unit 5,6 is miniature gas turbine.Microgrid is 0.5 yuan/kWh from the electric weight electricity charge of buying of major network.

Table 1 microgrid parameter

Table 2 distributed power source parameter

A) diesel engine unit parameter

Note: unit 3,4 is fuel cell, unit 5,6 is miniature gas turbine.

In the calculating of the present embodiment, particle cluster algorithm parameter is as follows: power inertial factor ω _max=0.9, ω _min=0.4, study factor C ₁=C ₂=2.05, on iterations, be limited to 800 times.

Table 3 is distributed power source 24 hours optimization result and PCC point power to microgrid supply of exerting oneself.Calculating total expenses is 7664.4 yuan.

Fig. 3 has provided consideration adaptive algorithm (shown in solid line) and has not considered the optimizing process of adaptive algorithm (shown in dotted line), and in figure, transverse axis represents iterations, and the longitudinal axis represents the global optimum of each iteration.As can be seen from the figure, add after the self-adaptation rule that improves particle rapidity, solve speed advantage obvious, in the time iterating to the 100th time, restrain effect and just occurred obvious difference, the speed that optimal value declines is faster, effectively solve the problem that is absorbed in local optimum in solution procedure, can obtain quickly the result of convergence.Meanwhile, adding the convergence result of adaptive algorithm also better, is 8121.1 yuan and do not add the last optimum results of adaptive algorithm, and many 456.7 yuan than the result that adds adaptive algorithm of cost of electricity-generatings, exceed 5.955%.Particle cluster algorithm after improving is described, can solves preferably the dynamic economic dispatch problem of microgrid when grid-connected, improved the speed of solving, and can obtain better optimum results.

Table 3 unit and PCC point out force data

The present invention improves the iterative formula of particle cluster algorithm, adds adaptive approach, so that speed is adjusted, and according to constraint condition, particle is adjusted, not out-of-limit to ensure particle.The present invention adopts the particle cluster algorithm after improvement to solve the dynamic economic dispatch of micro-grid system, to obtain the force level that of each unit day part after optimization.Calculate by trend the power that PCC is ordered simultaneously, realize the equilibrium of supply and demand of microgrid internal loading.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention with reference to above-described embodiment; do not depart from any amendment of spirit and scope of the invention or be equal to replacement, within the claim protection domain all awaiting the reply in application.

Claims (12)

1. the micro-grid system dynamic economic dispatch method based on particle cluster algorithm, is characterized in that, described method comprises the steps:

A, particle cluster algorithm parameter is set;

B, generation primary group;

C, the constraint of the speed bound of particle in micro-grid system dynamic economic dispatch is set;

D, determine the adaptive value of particle;

The adaptive value of E, comparison particle, finds out local optimum and the position thereof of each particle, and reaches particle and the position thereof of global optimum;

F, according to local optimum and all optimal value upgrade position and the speed of each particle;

G, according to constraint condition, and the bound of particle rapidity constraint judge the position of particle and speed whether out-of-limit, if out-of-limit, the position of particle and speed are adjusted in restriction range;

H, by tidal current computing method, the transmission power that the PCC of calculated equilibrium node is ordered;

I, adopt adaptive approach, to the particle rapidity processing of raising speed;

If J reaches iterations, stop iteration, obtain final result.

2. micro-grid system dynamic economic dispatch method as claimed in claim 1, is characterized in that, in described steps A, the required parameters of particle cluster algorithm is set, and described parameter comprises ω _max=0.9 or ω _min=0.4 weight factor, c ₁=c ₂=3.05 the study factor, population scale number and iterations, population scale and iterations, according to actual conditions setting, comprise that being set to population scale counts N=40, iterations C=500.

3. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step B, share at random the force level that of each unit according to the load level of day part in the computing interval, repeat this step according to population scale number, obtain primary group; Comprise:

Random generate the random array that line number is unit number, columns hop count while being, the element of this array is positive number, and each column element sum is one; Primary is that period load is multiplied by corresponding row, repeats this operation, obtains whole primary group, generates as follows for m particle:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(1\right);$

P _i,t＝a _i,t·P _D,t (2)；

a _1,t+a _2,t+…+a _i,t+…a _M,t＝1 (3)；

Wherein: T represents the time hop count of economic load dispatching, be one day 24 hours, per hour is a section, totally 24 periods, M is the unit number that participates in dynamic economic dispatch; a _i,trepresenting the i platform proportional element of unit t period, is random generation, meets (3) formula, therefore (3) formula represents that for the proportional element sum of all M platforms of t period be 1, P _d,tbe the size of the load of t period, adopt same procedure to generate other particles.

4. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step C, determine the speed bound of each particle according to the bound constraint of each unit, the bound of the difference reduction particle rapidity of exerting oneself according to unit bound, reduction scope is 1%, or in particle rapidity, be limited to the unit output upper limit deduct the lower limit of exerting oneself difference 1%, under particle rapidity, be limited to unit output lower limit deduct the upper limit of exerting oneself difference 1%.

5. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step D, according to the primary group who obtains in step B, determine the adaptive value of particle with Electrical Power System Dynamic economic load dispatching objective function, micro-grid system dynamic economic dispatch objective function is:

$f=\underset{t}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i}{\overset{M}{\mathrm{\&Sigma;}}}f\left(P_{\mathrm{it}}\right)---\left(4\right);$

(4) in formula: P _i,tbe exerting oneself of i platform unit t period, the time hop count that T is dynamic economic dispatch, M is the unit number that participates in dynamic economic dispatch;

Wherein the cost function of diesel engine unit is as follows:

f(P _it)＝a _i·P _it ²+b _i·P _it+c _i (5)；

(5) in formula, a _i, b _i, c _ifor cost coefficient;

Fuel cell, miniature gas turbine cost function are as follows:

$f\left(P_{i,t}\right)=(C_i^f+C_i^r)P_{i,t}---\left(6\right);$

(6) in formula, be respectively corresponding fuel cost coefficient, maintenance cost coefficient;

Adaptive value adopts (7) formula target function value:

$f_a=f=\underset{t}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i}{\overset{M}{\mathrm{\&Sigma;}}}f\left(P_{\mathrm{it}}\right)---\left(7\right);$

Using micro-grid system dynamic economic dispatch objective function result of calculation as particle adaptive value, target function value is less, and adaptive value is less, and the fitness of particle is higher.

6. micro-grid system dynamic economic dispatch method as claimed in claim 5, it is characterized in that, the bound constraint of micro-grid system dynamic economic dispatch comprises the constraint of unit output bound and (10) formula unit ramp loss of the power-balance constraint of following (8) formula, (9) formula:

$\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{P}_{i,t}+P_{\mathrm{PCC},t}={P_t}^D+{P_t}^{\mathrm{loss}}---\left(8\right);$

(8) in formula: P _{pCC, t}for the power to microgrid input that PCC is ordered, P _t ^dbe the payload of t period, P _t ^lossthe network loss size of t period;

P _i ^min≤P _i,t≤P _i ^max (9)；

(9) in formula: P _i ^minand P _i ^maxrepresent respectively i the platform lower limit of exerting oneself and the upper limit of unit t period;

$R_i^d\mathrm{\&Delta;t}\&le;P_{i,t}-P_{i,t-1}\&le;R_i^u\mathrm{\&Delta;t}---\left(10\right);$

(10) in formula: for downward Ramp Rate, it is negative value; for Ramp Rate upwards, on the occasion of; Δ t represents two time intervals between scheduling slot.

7. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step e, the adaptive value of more each particle, determine the highest particle of particle fitness, find the historical optimal value of each particle, i.e. local optimum, and particle position, and global optimum and particle position; If iteration for the first time, finds global optimum and corresponding particle position;

In particle cluster algorithm, the population number of population is comprised number of particles, and the each individuality in population the inside is all called a particle; Each feasible solution is all particles in population, and each particle has two attributes, and displacement and speed are all expressed as a matrix-vector, are shown below:

$P_m=\left[\begin{array}{ccccc}{P}_{\mathrm{1,1}}& ...& P_{1,t}& ...& P_{1,T}\\ P_{i,1}& ...& P_{i,t}& ...& P_{i,T}\\ ...& ...& ...& ...& ...\\ P_{M,1}& ...& P_{M,t}& ...& P_{M,T}\end{array}\right]---\left(11\right);$

$V_m=\left[\begin{array}{ccccc}{V}_{\mathrm{1,1}}& ...& V_{1,t}& ...& V_{1,T}\\ V_{i,1}& ...& V_{i,t}& ...& V_{i,T}\\ ...& ...& ...& ...& ...\\ V_{M,1}& ...& V_{M,t}& ...& V_{M,T}\end{array}\right]---\left(12\right);$

(11) and in (12) formula: P _mbe the displacement of m particle, P _i,tbe exerting oneself of i platform unit t period, i.e. the position of particle; V _mthe speed of m particle, V _i,tit is the correction of exerting oneself of the i platform unit t period of correspondence; T is the time hop count of dynamic economic dispatch; M is the unit number that participates in dynamic economic dispatch; In micro-grid system dynamic economic dispatch, the position of particle represents exerting oneself of each unit day part, and line number represents unit number, hop count when columns represents.

8. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step F, the local optimum obtaining according to step e and particle position, and all optimal values and particle position, according to more speed and the position of new particle of following expression formula:

$V_m^{k+1}=\mathrm{\&omega;}\&CenterDot;V_m^k+c_1\&CenterDot;r_1\&CenterDot;({\mathrm{pbest}}_m^k-P_m^k)+c_2\&CenterDot;r_2\&CenterDot;({\mathrm{gbest}}^k-P_m^k)+{\mathrm{\&Delta;V}}_m^k---\left(13\right);$

$P_m^{k+1}=P_m^k+V_m^{k+1}+{\mathrm{\&Delta;P}}_m^k---\left(14\right);$

(13) and in (14) formula: represent respectively the adjustment amount of speed and position; be the speed in the k generation of m particle, the speed in the k+1 generation of m particle; be the k generation of m particle, for the k+1 generation of m particle; be the historical optimal value of m particle, gbest ^kit is the global optimum of the k time iteration; ω is inertial factor, for weighing global search and the local search ability of particle cluster algorithm; ω value is more greatly more easy to algorithm and increases search space, and the more little local optimal searching of more easily carrying out of value adopts self-adaptation to adjust the mode of inertial factor, that is:

$\mathrm{\&omega;}={\mathrm{\&omega;}}_{\max }-\frac{{\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{\min }}{C}k---\left(15\right);$

(15) in formula: ω _maxget 0.9 or 0.4, the C maximum times that is iteration, k is current iterations; c ₁, c ₂for the study factor, be respectively and control the maximum step-length of particle to individual optimum and global optimum's locality flight; Get c ₁=c ₂=3.05;

for adjusting out-of-limit particle, when particle is out-of-limit, according to (9), (10), (11) formula, according to following rule, particle is adjusted:

1) according to speed bound, constraint is adjusted unit output, on unit, in limited time, is exerted oneself and is limited to the upper limit, and unit is more lower in limited time, is exerted oneself and is limited to lower limit;

2) according to climbing constraint, unit output is adjusted, when unit is upwards climbed, if out-of-limit, unit output is limited to the upwards upper limit of climbing; When unit is climbed, if out-of-limit, unit output is limited to the lower limit of downward climbing downwards;

3) according to the unit output after adjusting, carry out trend calculating, obtain the transmission power that PCC is ordered.

9. micro-grid system dynamic economic dispatch method as claimed in claim 1, is characterized in that, for particle rapidity, is limited within the scope of constraint of velocity, and constraint of velocity formula is as follows:

$V_m^k\left[i\right]\left[j\right]=\left\{\begin{array}{cc}{V}_m^k{\left[i\right]\left[j\right]}_{\max },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]>V_m^k{\left[i\right]\left[j\right]}_{\max }\\ V_m^k{\left[i\right]\left[j\right]}_{\min },& \mathrm{if}{V}_m^k\left[i\right]\left[j\right]<V_m^k{\left[i\right]\left[j\right]}_{\min }\end{array}\right.---\left(16\right);$

(16) in formula: for the i of particle rapidity is capable, the element of j row, for the i of particle rapidity is capable, the maximal value defined in the element of j row, for the i of particle rapidity is capable, the minimum value defined in the element of j row; The maximum of particle rapidity, minimum value are according to following formula setting:

$V_m^k{\left[i\right]}_{\max }=({P_i}^{\max }-{P_i}^{\min })/100---\left(17\right);$

$V_m^k{\left[i\right]}_{\min }=({P_i}^{\min }-{P_i}^{\max })/100---\left(18\right);$

(18) in formula: be respectively the upper and lower bound of corresponding i platform unit speed in particle, will be extended to and the transversal vector of period corresponding length, obtain speed limit and lower limit that particle i is capable.

10. micro-grid system dynamic economic dispatch method as claimed in claim 1, it is characterized in that, in described step H, according to not out-of-limit particle position after adjusting in step G, be exerting oneself of each unit day part, obtain after the exerting oneself of each unit day part, the method of calculating by trend is calculated the power input to microgrid of PCC point day part, adopt Niu Lafa, PCC point is considered as to the balance node in trend calculating, calculate exerting oneself of its day part, thereby obtain the active power of PCC point to micro-grid system input.

11. micro-grid system dynamic economic dispatch methods as claimed in claim 1, it is characterized in that, in described step I, according to obtaining global optimum in step e, before and after comparing, the global optimum of twice, adopts adaptive approach, according to the order of magnitude of the difference of twice global optimum, particle rapidity is adjusted, and absolute value is larger, and adjustment degree is larger; Carry out as follows:

In particle cluster algorithm, along with the increase of iterations, can be absorbed in local optimum, now the speed of particle can reduce thereupon, thus particle rapidity is adjusted, that is:

v ₁＝v ₀+Δv (19)；

Wherein: v ₀, v ₁be respectively the particle rapidity before and after adjusting, Δ v is the speed amount of adjusting, and adopts adaptive algorithm when adjustment, and adaptive algorithm formula is as follows:

$\mathrm{\&Delta;v}=\left\{\begin{array}{c}0,\mathrm{\&lambda;}>0.01\\ v_{\mathrm{ini}}\&CenterDot;r_3,0.001<\mathrm{\&lambda;}\&le;0.01\\ v_{\mathrm{ini}}\&CenterDot;r_4,0<\mathrm{\&lambda;}\&le;0.001\end{array}\right.---\left(20\right);$

(20) in formula: v _inifor initial velocity set in particle cluster algorithm, r ₃for-0.1 to 0.1 random number, r ₄for-0.01 to 0.01 random number, for random number r ₃, r ₄need be with setting according to reality, λ calculates according to following formula:

$\mathrm{\&lambda;}=\frac{|{f_{\mathrm{best}}}^k-{f_{\mathrm{best}}}^{k-1}|}{{f_{\mathrm{best}}}^k}---\left(21\right);$

(21) in formula: f _best ^kbe the global optimum position of k for particle, f _best ^k-1twice global optimum difference relative scale before and after k-1 is for the position λ of global optimum of particle.

12. micro-grid system dynamic economic dispatch methods as claimed in claim 1, it is characterized in that, in described step J, the iterative algorithm of having set according to steps A, judge whether iterations reaches: if reach iterations C=500, stop calculating, obtain particle global optimum and be final particle position, this particle position is each unit exerting oneself at day part, thereby calculate net result, net result comprises going out force level and calculating the unit operation total expenses in economic cycle of each unit day part; If do not reach iterations, turn back to step D, continue to calculate.