CN104218607A - New energy access system application based on artificial fish swarm and tabu search algorithms - Google Patents

Abstract

The invention provides a new energy access system application based on artificial fish swarm and tabu search algorithms. The distributed-type new energy photovoltaic generating system access is optimized by the artificial fish swarm algorithm, two layers of artificial fish swarm algorithms are adopted in a sleeved manner for optimizing solving, the external layer is used for optimizing the access location and capacity of the photovoltaic generating system, and the internal layer is used for planning the trend influence on grids during photovoltaic grid connection; but according to the artificial fish swarm algorithm, the late convergence speed is slow, and the accuracy is low; on the basis, the tabu search algorithm is combined, the detects are remedied, and the convergence speed is increased, and the accuracy is improved.

Description

The application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm

Technical field

The present invention relates to distributed new grid integration field, more precisely a kind of application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm.

Background technology

New forms of energy can supplement centralized power supply system as distributed power source, reduce the possibility of having a power failure on a large scale when electrical network is damaged, thus improve the reliability and stability of power system power supply; These distributed power sources can also at some special occasions as stand-by heat, thus when electric grid large area power cut and even Tie-line Opening, distributed power source can maintain the electricity consumption of responsible consumer to avoid causing heavy losses.Especially in recent years, increasing wind-powered electricity generation, photovoltaic generation project are completed, and the grid-connected demand of new forms of energy energy is day by day strong.

But use existing new forms of energy access technology can have an impact to the quality of power supply of electrical network, make voltage fluctuation, frequency shift, produce high order harmonic component, produce larger harm to electrical network, can new forms of energy grid-connected key be grid-connected position and the selection of capacity.

Summary of the invention

The present invention mainly solves the technical problem existing for prior art, thus a kind of application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm is provided, can effectively make up artificial fish-swarm algorithm slow in late convergence, easily be absorbed in the shortcoming of locally optimal solution, effectively solve distributed new grid-connected time the position of access point and capacity problem, while making loss minimization, also there is optimization function to voltage.

Above-mentioned technical problem of the present invention is mainly solved by following technical proposals:

The application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm, comprises the following steps;

Step one, the random position of generation distributed new photovoltaic parallel in system and the initial population of capacity, initial population comprises more than one access point, is each access point Stochastic choice position and capacity;

Step 2, calculate each access point respectively whether the impact of the trend of electrical network is met the demands, judge whether to meet: if do not met, then this access point is optimized, and produce new position and capacity replaces this access point;

Step 3, judge whether each access point is the optimal solution meeting target function respectively: if then terminate; If not, then turn back to step one;

Wherein in step 2 take network loss as target function, and the network loss variable quantity of the grid-connected front and back of PV is

$\mathrm{\ΔLoss}=\frac{r{l}_1{P}_{\mathrm{PV}}}{3V^2}(2P_L+P_L\tan {\mathrm{\Φ}}_L\tan {\mathrm{\Φ}}_{\mathrm{PV}}-\frac{P_{\mathrm{PV}}}{{\cos }^2{\mathrm{\Φ}}_{\mathrm{PV}}})$

Wherein:

R is system line resistance per unit length, unit Ω/km;

L ₁be respectively the distance of system power supply to PV installation place, unit is km;

P _lbe respectively the active power that load consumes, unit is respectively W;

P _pVbe respectively the active power that PV exports, unit is respectively W;

Φ _l, Φ _pVbe respectively the power-factor angle of load and PV.

As preferred embodiment of the present invention, in step 2, the method for optimization is artificial fish-swarm algorithm.

As preferred embodiment of the present invention, in step 2, be optimized in conjunction with tabu search algorithm again after using artificial fish-swarm algorithm, form hybrid algorithm.

As the embodiment that the present invention is good, the step of described hybrid algorithm is as follows:

Step a, using the initial solution of the optimum state on artificial fish-swarm algorithm bulletin board as TABU search, empty taboo list;

Step b, judge whether target function meets, and is, terminate algorithm and export optimal solution, otherwise enter step c;

Step c, produce the neighborhood solution of current solution, and therefrom select the candidate solution meeting new forms of energy access constraints;

Steps d, judging whether candidate solution meets aspiration criterion, if met, with meeting the optimum value of aspiration criterion as new current solution, replacing the object entering taboo list the earliest simultaneously; If do not met, enter step e;

Step e, judge the taboo attribute of candidate solution corresponding objects, select optimal solution that non-taboo object is corresponding as new current solution, replace the object entering taboo list the earliest simultaneously;

Step f, go to step b.

In sum, the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm provided by the present invention, can effectively make up artificial fish-swarm algorithm slow in late convergence, easily be absorbed in the shortcoming of locally optimal solution, effectively solve distributed new grid-connected time the position of access point and capacity problem, while making loss minimization, also there is optimization function to voltage.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.

Fig. 1 is the grid-connected structural representation of paging type PV access point;

Fig. 2 is the algorithm operational flow diagram of the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm of the present invention;

Fig. 3 is the IEEE 30 node power distribution net system node network topology structure in the specific embodiment of the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm of the present invention;

Fig. 4 is the grid-connected addressing renewal process of the specific embodiment of the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm of the present invention.

Embodiment

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail, can be easier to make advantages and features of the invention be readily appreciated by one skilled in the art, thus more explicit defining is made to protection scope of the present invention.

The advantage of the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm provided by the present invention is: can effectively make up artificial fish-swarm algorithm slow in late convergence, easily be absorbed in the shortcoming of locally optimal solution, effectively solve distributed new grid-connected time the position of access point and capacity problem, while making loss minimization, also there is optimization function to voltage.

The application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm, comprises the following steps;

Step one, the random position of generation distributed new photovoltaic parallel in system and the initial population of capacity, initial population comprises more than one access point, is each access point Stochastic choice position and capacity;

Step 2, calculate each access point respectively whether the impact of the trend of electrical network is met the demands, judge whether to meet: if do not met, then this access point is optimized, and produce new position and capacity replaces this access point;

Step 3, judge whether each access point is the optimal solution meeting target function respectively: if then terminate; If not, then turn back to step one;

Wherein in step 2 take network loss as target function, model as shown in Figure 1, founding mathematical models;

In figure:

I _s, I _pV, I _lbeing respectively the electric current that system power supply output current, PV output current and the flow direction meet, is all monophase current, and unit is A;

L ₁, l ₂be respectively system power supply to the distance of PV installation place, PV installation place to the distance of load, unit is km;

S _l, P _l, Q _lbe respectively apparent power, active power and reactive power that load consumes, unit is respectively V _a, W, Var;

S _pV, P _pV, Q _pVbe respectively apparent power, active power and reactive power that PV exports, unit is respectively VA, W, Var;

In order to obtain the grid-connected impact on network loss of PV, the network loss of the grid-connected front and back of PV need be compared.

No matter whether grid-connected PV is, has

$I_L=\frac{P_L-j{Q}_L}{3V}$

In formula, V is circuit phase voltage;

If the network before PV is grid-connected damages as LOSS ₀, then have

${\mathrm{Loss}}_0=\frac{r(l_1+l_2)(P_L^2+Q_L^2)}{3V^2}$

In formula, r is system line resistance per unit length, unit Ω/km;

After PV is grid-connected, PV output current has

$I_{\mathrm{PV}}=\frac{P_{\mathrm{PV}}-j{Q}_{\mathrm{PV}}}{3V}$

If circuit l ₁, l ₂loss be respectively Loss ₁, Loss ₂, total losses are Loss _pV, then have

${\mathrm{Loss}}_1=3r{l}_1{I}_s^2=\frac{r{l}_1(P_L^2+Q_{\mathrm{PV}}^2+P_{\mathrm{PV}}^2+Q_{\mathrm{PV}}^2-2P_L{P}_{\mathrm{PV}}-2Q_L{Q}_{\mathrm{PV}})}{3V^2}$

${\mathrm{Loss}}_2=3r{l}_2{I}_L^2=\frac{r{l}_2(P_L^2+Q_L^2)}{3V^2}$

$\begin{array}{c}{\mathrm{Loss}}_{\mathrm{PV}}={\mathrm{Loss}}_1+{\mathrm{Loss}}_2\\ =\frac{r(l_1+l_2)}{3V^2}[P_L^2+Q_L^2+\\ <P_{\mathrm{PV}}^2+Q_{\mathrm{PV}}^2+(P_{\mathrm{PV}}^2+Q_{\mathrm{PV}}^2-2P_L{P}_{\mathrm{PV}}-2Q_L{Q}_{\mathrm{PV}})\frac{l_1}{l_1+l_2}>]\end{array}$

So the network loss variable quantity of the grid-connected front and back of PV is

$\mathrm{\&Delta;Loss}=\frac{r{l}_1}{3V^2}(2P_L{P}_{\mathrm{PV}}+2Q_L{Q}_{\mathrm{PV}}-P_{\mathrm{PV}}^2{Q}_{\mathrm{PV}}^2)$

If the power-factor angle of load and PV is respectively Φ _land Φ _pVthen have

$\mathrm{\&Delta;Loss}=\frac{r{l}_1{P}_{\mathrm{PV}}}{3V^2}(2P_L+P_L\tan {\mathrm{\&Phi;}}_L\tan {\mathrm{\&Phi;}}_{\mathrm{PV}}-\frac{P_{\mathrm{PV}}}{{\cos }^2{\mathrm{\&Phi;}}_{\mathrm{PV}}})$

In step 2, the method for optimization is artificial fish-swarm algorithm.

In step 2, be optimized in conjunction with tabu search algorithm again after using artificial fish-swarm algorithm, form hybrid algorithm.

The step of described hybrid algorithm is as follows:

Step a, using the initial solution of the optimum state on artificial fish-swarm algorithm bulletin board as TABU search, empty taboo list;

Step b, judge whether target function meets, and is, terminate algorithm and export optimal solution, otherwise enter step c;

Step c, produce the neighborhood solution of current solution, and therefrom select the candidate solution meeting new forms of energy access constraints;

Steps d, judging whether candidate solution meets aspiration criterion, if met, with meeting the optimum value of aspiration criterion as new current solution, replacing the object entering taboo list the earliest simultaneously; If do not met, enter step e;

Step e, judge the taboo attribute of candidate solution corresponding objects, select the optimal solution fight of steps on the eastern side of the hall where the host stood to welcome the guests corresponding to non-taboo object to be new current solution, replace the object entering taboo list the earliest simultaneously;

Step f, go to step b.

Embodiment: the present embodiment adopts Matlab software programming and carries out simulation calculation to typical IEEE 30 node power distribution net system, and wherein the rated voltage of this distribution network system is 135kV.Calculate to simplify and don't lose correctness, distributed power source adopts the invariable power model of lagging power-factor 0.9, exerting oneself of distributed power source is considered as " negative load ".

As shown in Figure 3, each node parameter is as shown in table 1, and each branch data is as shown in table 2 for IEEE 30 node power distribution net system node network topology structure.

The each node parameter of table 1:IEEE30 node

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The each branch data of table 2:IEEE30 node

Start node	Terminal node	r	x	b
					1	2	0.02	0.06	0.03
1	3	0.05	0.19	0.02
					2	4	0.06	0.17	0.02
3	4	0.01	0.04	0
					2	5	0.05	0.2	0.02
2	6	0.06	0.18	0.02
					4	6	0.01	0.04	0
5	7	0.05	0.12	0.01
					6	7	0.03	0.08	0.01
6	8	0.01	0.04	0
					6	9	0	0.21	0
6	10	0	0.56	0
					9	11	0	0.21	0
9	10	0	0.11	0
					4	12	0	0.26	0
12	13	0	0.14	0
					12	14	0.12	0.26	0
12	15	0.07	0.13	0
					12	16	0.09	0.2	0
14	15	0.22	0.2	0
					16	17	0.08	0.19	0
15	18	0.11	0.22	0
					18	19	0.06	0.13	0
19	20	0.33	0.07	0
					10	20	0.09	0.21	0
10	17	0.03	0.08	0

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Start node	Terminal node	r	x	b
					10	21	0.03	0.07	0
10	22	0.07	0.15	0
					21	22	0.01	0.02	0
15	23	0.1	0.2	0
					22	24	0.12	0.18	0
23	24	0.13	0.27	0
					24	25	0.19	0.33	0
25	26	0.25	0.38	0
					25	27	0.11	0.21	0
28	27	0	0.4	0
					27	29	0.22	0.42	0
27	30	0.32	0.6	0
					29	30	0.24	0.45	0
8	28	0.06	0.2	0.02
					6	28	0.02	0.06	0.01

In the present embodiment, what hybrid algorithm needed to solve is the best on-position of distributed power source in IEEE30 node power distribution net system and capacity.

Provide one group of optimal solution in hybrid algorithm searching process below, the renewal of finder as shown in Figure 4; Wherein, (a|b) represents the distributed power source access position of power distribution network and capacity, is namely the distributed power source of bMW at node a access capacity, the active power loss after the access of below numeral distributed power source.

Table 3: network loss contrast before and after the net of distributed new access Shen

?	On-position	Access capacity (MW)	Network loss (MW)
				Before access	Nothing	Nothing	2.444
After access	10	43	1.5848

The active power loss of power distribution network reduces to 1.5848MW by original 2.444MW as can be seen from Table 3, the active power loss after distributed power source access power distribution network be only that original power distribution network does not access distributed power source active power loss 64.84%.Table 4 compared for the voltage condition before and after distributed power source access electrical network, and before distributed power source access power distribution network, node 8 is the minimum node of distribution network load node voltage amplitude, and its amplitude is 0.961; After distributed power source access power distribution network, although node 8 is still distribution network voltage amplitude minimum node, the voltage magnitude of this node then brings up to 0.966pu.

Table 4: Shen pressure contrast before and after the net of distributed new access Shen

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Associative list 3 and table 4 can be found out, simulation result while effectively reducing distribution network loss, also improves distribution network load node global voltage level after showing distributed power source access power distribution network.

Claims (4)

1. the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm, is characterized in that: comprise the following steps;

Step one, the random position of generation distributed new photovoltaic parallel in system and the initial population of capacity, initial population comprises more than one access point, is each access point Stochastic choice position and capacity;

Step 2, calculate each access point respectively whether the impact of the trend of electrical network is met the demands, judge whether to meet: if do not met, then this access point is optimized, and produce new position and capacity replaces this access point;

Step 3, judge whether each access point is the optimal solution meeting target function respectively: if then terminate; If not, then turn back to step one;

Wherein in step 2 take network loss as target function, and the network loss variable quantity of the grid-connected front and back of PV is

$\mathrm{\&Delta;Loss}=\frac{r{l}_1{P}_{\mathrm{PV}}}{3V^2}(2P_L+P_L\tan {\mathrm{\&Phi;}}_L\tan {\mathrm{\&Phi;}}_{\mathrm{PV}}-\frac{P_{\mathrm{PV}}}{{\cos }^2{\mathrm{\&Phi;}}_{\mathrm{PV}}})$

Wherein:

R is system line resistance per unit length, unit Ω/km;

L ₁be respectively the distance of system power supply to PV installation place, unit is km;

P _pVbe respectively the active power that PV exports, unit is respectively W;

V is circuit phase voltage;

P _lbe respectively the active power that load consumes, unit is respectively W;

Φ _l, Φ _pVbe respectively the power-factor angle of load and PV.

2. the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm according to claim 1, is characterized in that: in step 2, and the method for optimization is artificial fish-swarm algorithm.

3. the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm according to claim 2, is characterized in that: in step 2, is optimized after using artificial fish-swarm algorithm in conjunction with tabu search algorithm again, forms hybrid algorithm.

4. the application in new forms of energy connecting system based on artificial fish-swarm and tabu search algorithm according to claim 3, is characterized in that: the step of described hybrid algorithm is as follows:

Step a, using the initial solution of the optimum state on artificial fish-swarm algorithm bulletin board as TABU search, empty taboo list;

Step b, judge whether target function meets, and is, terminate algorithm and export optimal solution, otherwise enter step c;

Step c, produce the neighborhood solution of current solution, and therefrom select the candidate solution meeting new forms of energy access constraints;

Steps d, judging whether candidate solution meets aspiration criterion, if met, with meeting the optimum value of aspiration criterion as new current solution, replacing the object entering taboo list the earliest simultaneously; If do not met, enter step e;

Step e, judge the taboo attribute of candidate solution corresponding objects, select optimal solution that non-taboo object is corresponding as new current solution, replace the object entering taboo list the earliest simultaneously;

Step f, go to step b.