CN105023058A - Power distribution network intelligent soft switch operation optimization method with simultaneous consideration of switch motion - Google Patents

Abstract

Provided is a power distribution network intelligent soft switch operation optimization method with simultaneous consideration of switch motion. According to a power distribution system, connection relation of circuit parameters, load level and network topology, system operation voltage level and branch current restriction, distributed power supply connection position, type, capacity and parameters, intelligent soft switch connection position, capacity and parameters, load and distributed power supply operation characteristic prediction curves in an operation optimization cycle, a system reference voltage and a reference power initial value are input; a sequential optimization model of cooperation operation of a power distribution network connection switch and an intelligent soft switch is established; conic model conversion of an object function and non-linear constraints in the sequential optimization model of cooperation operation of the power distribution network connection switch and the intelligent soft switch is carried out according to a conic optimization standard form; linearization of the object function is carried out, the non-linear constraints are converted into linear constraints, second order cone constraints or rotary cone constraints, and solving of the obtained mathematic model is carried out through a CPLEX solver. The provided method avoids tedious iteration and a lot of tests.

Description

A kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion

Technical field

The present invention relates to the timing optimization method that a kind of power distribution network runs.Particularly relate to a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion.

Background technology

Make the development of power distribution network be faced with new pressure and challenge to the energy and showing great attention to of environment, these pressure and challenge are also the opportunities that promotion conventional electrical distribution net develops to intelligent distribution network simultaneously.In intelligent distribution network, controllable device is increasing, network structure and the method for operation more flexible and changeable, senior Distribution Automation Technology, advanced ICT (information and communication technology) are able to widespread use, and distributed power source, energy storage, Demand-side resource etc. start the operation and the optimization that participate in power distribution network.The development of intelligent distribution network and the widespread use of generation of electricity by new energy technology are promoting the deep reform of adapted power mode and running fluidization air flow.

Intelligence Sofe Switch (Soft Normally Open Point, SNOP) device is exactly a kind of novel intelligent power distribution equipment of the replacement tradition interconnection switch derived under above-mentioned background.Compared with switching manipulation, the power of SNOP controls safer, reliable, even can realize real-time optimization, can successfully manage randomness and undulatory property that distributed power source and load bring.But the realization of SNOP is mainly based on full-control type power electronic device, and the cost of these devices itself is higher, and the interconnection switch in a short time in power distribution network can not be replaced by SNOP completely.This needs overall thinking interconnection switch and SNOP and the situation of depositing with regard to making the running optimizatin of power distribution network, and its optimal operation model will be a mixed integer nonlinear programming problem needing simultaneously to solve discrete magnitude (on off state) and continuous quantity (SNOP through-put power).

In intelligent distribution network, the distributed power source of extensive access exacerbates the uncertainty of system cloud gray model, consider the factor such as switching loss and dash current, interconnection switch can not frequently cut-off, traditional network reconfiguration is difficult to the real-time adjustment accomplishing power distribution network, SNOP then can change through-put power in real time, adjust operation state, out-of-limit to tackle the series of voltage brought after distributed power source accesses, the problems such as circuit overload, therefore need to carry out modeling from seasonal effect in time series angle to power distribution network optimization problem, and need the coordination optimization problem considering interconnection switch and SNOP.After considering its temporal aspect, in power distribution network running optimizatin process, making rational planning for of switch motion just becomes a problem demanding prompt solution.Meanwhile, consider timing optimization problem that the SNOP of switch motion expense runs can along with time increasing of discontinuity surface number solve dimension and sharply increase, become extensive mixed integer nonlinear programming problem, cause it to solve and become more difficult, even unfeasible.

For solving this kind of extensive mixed integer nonlinear programming problem, be also difficult at present find one method for solving fast and effectively.Solving of this problem is proposed and developed multiple optimization method at present, has mainly contained and comprise: 1) traditional mathematics optimization method, comprising analytical method, successive elimination method etc.; 2) heuritic approach, comprising Sensitivity Analysis Method, expert system etc.; 3) randomized optimization process, comprising genetic algorithm, particle cluster algorithm etc.

Although said method or technology have certain application, but also all there is obvious deficiency, as although traditional mathematics optimization method can carry out global optimizing in theory, inevitably there is " dimension calamity " problem when practical application, often present explosion type computing time and increase sharply; Heuritic approach requires a polynomial time in time complexity, and computing velocity is fast, but the optimum solution obtained or the optimality lacked in mathematical meaning or just locally optimal solution; Although the last solution that randomized optimization process is searched and initial solution have nothing to do, the power distribution network for different scales needs to reset its controling parameters, population quantity, iterations etc., thus ensures to find globally optimal solution with larger probability.Heuristic and random device is applicable to solve integer programming problem more; but for considering the power distribution network timing optimization problem of interconnection switch and SNOP synthetic operation; mathematics is extensive mixed integer nonlinear programming problem in essence; so traditional mathematics optimization method, heuritic approach are for solving in this kind of problem, speed or precision is many can not meet the demands simultaneously.Therefore, need a kind of accurately, the model and algorithl of the above-mentioned optimization problem of rapid solving.

Summary of the invention

Technical matters to be solved by this invention is, there is provided one can consider the power distribution network running wastages such as switch motion expense equivalence loss, SNOP running wastage and via net loss, determine the power distribution network intelligence Sofe Switch running optimizatin method considering switch motion while rational switch motion sequential and SNOP run.

The technical solution adopted in the present invention is: a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion, comprises the steps:

1) according to selected distribution system, incoming line parameter, load level and network topology annexation, system cloud gray model voltage levvl and branch current restriction, distributed power source on-position, type and capacity and parameter, intelligence Sofe Switch on-position and capacity and parameter, running optimizatin cycle internal loading and distributed power source operation characteristic prediction curve, and system reference voltage and reference power initial value;

2) according to step 1) the distribution system structure and parameters that provides, consider the switch motion expense equivalence loss of via net loss, network reconfiguration and the running wastage of intelligent Sofe Switch simultaneously, set up the timing optimization model of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation, comprise: choosing root node is balance node, setting distribution system running wastage is minimum is objective function, considers that network topology constraint, system load flow constraint, system cloud gray model constraint, intelligent Sofe Switch run constraint respectively;

3) according to cone optimize canonical form to step 2) described in power distribution network interconnection switch and intelligent Sofe Switch synthetic operation timing optimization model in objective function and non-linear constrain carry out Based On The Conic Model conversion;

4) through step 3) conversion, respectively by objective function linearization, non-linear constrain is converted into linear restriction, second order cone constraint or rotating cone constraint, adopts CPLEX solver to solve the mathematical model obtained;

5) export step 4) solving result, comprise on off state, intelligent Sofe Switch optimal transmission performance number, network power flow solutions and target function value.

Step 2) described in distribution system running wastage minimum be objective function, be expressed as

min f＝E _S，loss+E _L，loss+E _SNOP，loss

In formula, switch motion expense equivalence loss E _{s, loss}, via net loss E _{l, loss}with the running wastage E of intelligent Sofe Switch _{sNOP, loss}represent with following formula respectively

$E_{S,loss}=C_S{\Σ}_{t=1}^{N_T}{\Σ}_{i=1}^{N_N}{\Σ}_{j\∈\mathrm{\Ω}\left(i\right)}|{\mathrm{\α}}_{ij}\left(t\right)-{\mathrm{\α}}_{ij}(t-1)|$

$E_{L,loss}={\Σ}_{t=1}^{N_T}{\Σ}_{i=1}^{N_N}{\Σ}_{j\∈\mathrm{\Ω}\left(i\right)}{r}_{ij}{I}_{ij}^2\left(t\right)\mathrm{\Δ}t$

$E_{SNOP,loss}={\Σ}_{t=1}^{N_T}{\Σ}_{m=1}^{N_{SNOP}}\left(A_{m,1}\left|P_{m,1}\left(t\right)\right|+A_{m,2}\left|P_{m,2}\left(t\right)\right|\right)\mathrm{\Δ}t$

In formula, C _sfor switch motion expense equivalent conversion coefficient; Δ t optimizes the period interval calculated; N _tfor optimizing the time hop count calculated, N _nfor the node total number in system, N _sNOPfor accessing the number of intelligent Sofe Switch in system; The set of the adjacent node that Ω (i) is node i; α _ijt () is the on off state of t period branch road ij; r _ijfor the resistance of branch road ij, I _ijt () flows to the current amplitude of node j for t period node i; P _{m, 1}(t) and P _{m, 2}t () is the meritorious output power of two transverters of t period m intelligent Sofe Switch, A _{m, 1}and A _{m, 2}it is the active loss coefficient of two transverters of m intelligent Sofe Switch.

Step 2) described in network topology constraint representation be

α _ij(t)＝β _ij(t)+β _ji(t)

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=1,\∀i\∈N_N\backslash N_S$

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=0,\∀i\∈N_S$

α _ij(t)∈{0，1}

β _ij(t)∈{0，1}

In formula, N _sfor the source node number in system; β _ijt () represents t period node i and node j relation, be 1, otherwise be 0 when node j is the parent node of node i.

Step 2) described in system load flow constraint representation be

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(P_{ji}\left(t\right)-r_{ji}{I}_{ji}^2\left(t\right)\right)+P_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{P}_{ik}\left(t\right)$

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(Q_{ji}\left(t\right)-x_{ji}{I}_{ji}^2\left(t\right)\right)+Q_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{Q}_{ik}\left(t\right)$

P _i(t)＝P _DG，i(t)+P _SNOP，i(t)-P _LOAD，i(t)

Q _i(t)＝Q _DG，i(t)+Q _SNOP，i(t)-Q _LOAD，i(t)

$I_{ij}^2\left(t\right)=\frac{P_{ij}^2\left(t\right)+Q_{ij}^2\left(t\right)}{U_i^2\left(t\right)}$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\left(t\right)+x_{ij}{Q}_{ij}\left(t\right)\right)+\left(r_{ij}^2+x_{ij}^2\right)I_{ij}^2\left(t\right)+M\left(1-{\mathrm{\α}}_{ij}\left(t\right)\right)\≥0$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\left(t\right)+x_{ij}{Q}_{ij}\left(t\right)\right)+\left(r_{ij}^2+x_{ij}^2\right)I_{ij}^2\left(t\right)-M\left(1-{\mathrm{\α}}_{ij}\left(t\right)\right)\≤0$

In formula, Φ (i) take node i as the branch road headend node set of endpoint node, and Ψ (i) take node i as the branch road set of end nodes of headend node; U _it () is the voltage magnitude of t period node i, x _ijfor the reactance of branch road ij; P _ijt () flows to the active power of node j, Q for t period node i _ijt () flows to the reactive power of node j for t period node i; The set of the adjacent node that Ω (i) is node i; P _i(t) active power sum for t period node i is injected, P _{dG, i}(t), P _{sNOP, i}(t), P _{lOAD, i}t () is respectively active power, the active power of SNOP transmission, the active power of load consumption that in t period node i, distributed power source injects, Q _i(t) active power sum for t period node i is injected, Q _{dG, i}(t), Q _{sNOP, i}(t), Q _{lOAD, i}the reactive power that the reactive power that t () is respectively reactive power that distributed power source in t period node i injects, SNOP sends, load consume; M is a maximum value.

Step 2) described in intelligent Sofe Switch run constraint representation and be

P _m，1(t)+P _m，2(t)+A _m，1|P _m，1(t)|+A _m，2|P _m，2(t)|＝0

$\sqrt{P_{m,1}^2\left(t\right)+Q_{m,1}^2\left(t\right)}\≤S_{m,1,\max }$

$\sqrt{P_{m,2}^2\left(t\right)+Q_{m,2}^2\left(t\right)}\≤S_{m,2,\max }$

-Q _m，1，max≤Q _m，1(t)≤Q _m，1，max

-Q _m，2，max≤Q _m，2(t)≤Q _m，2，max

In formula, Q _{m, 1}(t) and Q _{m, 2}the reactive power that t two transverters that () is t period m intelligent Sofe Switch export; S _{m, 1, max}, S _{m, 2, max}, Q _{m, 1, max}, Q _{m, 2, max}the access capacity being respectively m intelligent Sofe Switch two transverters and the reactive power upper limit that can export.

A kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion of the present invention; based on solve consider switch motion expense prerequisite under multiple time discontinuity surface interconnection switch and SNOP the power distribution network deposited runs timing optimization problem; its mathematics essence is extensive mixed integer nonlinear programming problem (MINLP), and current existing method is all difficult to quick and precisely solve.The present invention is according to the ultimate principle of cone optimized algorithm, carry out boring transforming to the objective function of Optimized model and constraint condition, former problem is converted into MIXED INTEGER Second-order cone programming problem (MISOCP), greatly reduces and solve difficulty, the instrument that solves easy to use solves.Cone optimization method of the present invention can carry out Unify legislation to network reconfiguration and SNOP running optimizatin problem, the problem solving of complicated mixed integer nonlinear programming is achieved, avoid loaded down with trivial details iteration and a large amount of tests, computing velocity has and promotes significantly.Further, because bore the geometry of the grace had and special processing mode, the optimality of the solution of institute's Solve problems can be ensured, apply it in the timing optimization problem of SNOP operation, optimum system cloud gray model scheme can be obtained fast.

Accompanying drawing explanation

Fig. 1 is the process flow diagram that the present invention considers the power distribution network intelligence Sofe Switch running optimizatin method of switch motion simultaneously;

Fig. 2 is IEEE 33 node example and distributed power source, SNOP on-position figure;

Fig. 3 is distributed power source and load operation Predicting Performance Characteristics curve;

Fig. 4 is switch motion expense conversion coefficient each when being 5kWh/ time discontinuity surface switch motion situation;

Fig. 5 a is different switch motion expense conversion coefficient corresponding SNOP transmitting active power situation of change;

Fig. 5 b is that the corresponding SNOP of different switch motion expense conversion coefficient sends reactive power situation of change;

Fig. 6 is different switch motion expense conversion coefficient corresponding node 18 change in voltage situations.

Embodiment

A kind of consider that the power distribution network intelligence Sofe Switch running optimizatin method of switch motion is described in detail below in conjunction with embodiment and accompanying drawing to of the present invention simultaneously.

As shown in Figure 1, a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion of the present invention, comprises the steps:

1) according to selected distribution system, incoming line parameter, load level and network topology annexation, system cloud gray model voltage levvl and branch current restriction, distributed power source on-position, type and capacity and parameter, intelligence Sofe Switch (SNOP) on-position and capacity and parameter, running optimizatin cycle internal loading and distributed power source operation characteristic prediction curve, and the initial value such as system reference voltage and reference power;

2) according to step 1) the distribution system structure and parameters that provides, consider the switch motion expense equivalence loss of via net loss, network reconfiguration and the running wastage of intelligent Sofe Switch simultaneously, set up the timing optimization model of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation, comprise: choosing root node is balance node, setting distribution system running wastage is minimum is objective function, considers that network topology constraint, system load flow constraint, system cloud gray model constraint, intelligent Sofe Switch run constraint respectively; Wherein:

(1) the distribution system running wastage described in is minimum is objective function, is expressed as

min f＝E _S，loss+E _L，loss+E _SNOP，loss(1)

In formula, switch motion expense equivalence loss E _{s, loss}, via net loss E _{l, loss}with the running wastage E of intelligent Sofe Switch _{sNOP, loss}represent with following formula respectively

$E_{S,loss}=C_S{\Σ}_{t=1}^{N_T}{\Σ}_{i=1}^{N_N}{\Σ}_{j\∈\mathrm{\Ω}\left(i\right)}|{\mathrm{\α}}_{ij}\left(t\right)-{\mathrm{\α}}_{ij}(t-1)|---\left(2\right)$

$E_{L,loss}={\Σ}_{t=1}^{N_T}{\Σ}_{i=1}^{N_N}{\Σ}_{j\∈\mathrm{\Ω}\left(i\right)}{r}_{ij}{I}_{ij}^2\left(t\right)\mathrm{\Δ}t---\left(3\right)$

$E_{SNOP,loss}={\Σ}_{t=1}^{N_T}{\Σ}_{m=1}^{N_{SNOP}}\left(A_{m,1}\left|P_{m,1}\left(t\right)\right|+A_{m,2}\left|P_{m,2}\left(t\right)\right|\right)\mathrm{\Δ}t---\left(4\right)$

In formula, C _sfor switch motion expense equivalent conversion coefficient; Δ t optimizes the period interval calculated; N _tfor optimizing the time hop count calculated, N _nfor the node total number in system, N _sNOPfor accessing the number of intelligent Sofe Switch in system; The set of the adjacent node that Ω (i) is node i; α _ijt () is the on off state of t period branch road ij; r _ijfor the resistance of branch road ij, I _ijt () flows to the current amplitude of node j for t period node i; P _{m, 1}(t) and P _{m, 2}t () is the meritorious output power of two transverters of t period m intelligent Sofe Switch, A _{m, 1}and A _{m, 2}it is the active loss coefficient of two transverters of m intelligent Sofe Switch.

(2) the network topology constraint representation described in is

α _ij(t)＝β _ij(t)+β _ji(t) (5)

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=1,\∀i\∈N_N\backslash N_S---\left(6\right)$

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=0,\∀i\∈N_S---\left(7\right)$

α _ij(t)∈{0，1} (8)

β _ij(t)∈{0，1} (9)

In formula, N _sfor the source node number in system; β _ijt () represents t period node i and node j relation, be 1, otherwise be 0 when node j is the parent node of node i.

(3) the system load flow constraint representation described in is

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(P_{ji}\left(t\right)-r_{ji}{I}_{ji}^2\left(t\right)\right)+P_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{P}_{ik}\left(t\right)---\left(10\right)$

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(Q_{ji}\left(t\right)-x_{ji}{I}_{ji}^2\left(t\right)\right)+Q_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{Q}_{ik}\left(t\right)---\left(11\right)$

P _i(t)＝P _DG，i(t)+P _SNOP，i(t)-P _LOAD，i(t) (12)

Q _i(t)＝Q _DG，i(t)+Q _SNOP，i(t)-Q _LOAD，i(t) (13)

$I_{ij}^2\left(t\right)=\frac{P_{ij}^2\left(t\right)+Q_{ij}^2\left(t\right)}{U_i^2\left(t\right)}---\left(14\right)$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\left(t\right)+x_{ij}{Q}_{ij}\left(t\right)\right)+\left(r_{ij}^2+x_{ij}^2\right)I_{ij}^2\left(t\right)+M\left(1-{\mathrm{\α}}_{ij}\left(t\right)\right)\≥0---\left(15\right)$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\left(t\right)+x_{ij}{Q}_{ij}\left(t\right)\right)+\left(r_{ij}^2+x_{ij}^2\right)I_{ij}^2\left(t\right)-M(1-{\mathrm{\α}}_{ij}(t\left)\right)\≤0---\left(16\right)$

In formula, Φ (i) take node i as the branch road headend node set of endpoint node, and Ψ (i) take node i as the branch road set of end nodes of headend node; U _it () is the voltage magnitude of t period node i, x _ijfor the reactance of branch road ij; P _ijt () flows to the active power of node j, Q for t period node i _ijt () flows to the reactive power of node j for t period node i; The set of the adjacent node that Ω (i) is node i; P _i(t) active power sum for t period node i is injected, P _{dG, i}(t), P _{sNOP, i}(t), P _{lOAD, i}t () is respectively active power, the active power of SNOP transmission, the active power of load consumption that in t period node i, distributed power source injects, Q _i(t) active power sum for t period node i is injected, Q _{dG, i}(t), Q _{sNOP, i}(t), Q _{lOAD, i}the reactive power that the reactive power that t () is respectively the reactive power that in t period node i, distributed power source injects, intelligent Sofe Switch sends, load consume; M is a maximum value.

(4) the system cloud gray model constraint representation described in is

$U_{i,\min }^2\≤U_i^2\left(t\right)\≤U_{i,\max }^2---\left(17\right)$

$I_{ij}^2\left(t\right)\≤I_{ij,\max }^2---\left(18\right)$

-Mα _ij(t)≤P _ii(t)≤Mα _ij(t) (19)

-Mα _ij(t)≤Q _ij(t)≤Mα _ij(t) (20)

$0\≤I_{ij}^2\left(t\right)\≤{\mathrm{M\α}}_{ij}\left(t\right)---\left(21\right)$

In formula, U _{i, min}and U _{i, max}be respectively minimum permission magnitude of voltage and the maximum allowable voltage of node i; I _{ij, max}for the maximum allowed current value of this branch road.

(5) the intelligent Sofe Switch described in runs constraint representation

P _m，1(t)+P _m，2(t)+A _m，1|P _m，1(t)|+A _m，2|P _m，2(t)|＝0 (22)

$\sqrt{P_{m,1}^2\left(t\right)+Q_{m,1}^2\left(t\right)}\≤S_{m,1,\max }---\left(23\right)$

$\sqrt{P_{m,2}^2\left(t\right)+Q_{m,2}^2\left(t\right)}\≤S_{m,2,\max }---\left(24\right)$

-Q _m，1，max≤Q _m，1(t)≤Q _m，1，max(25)

-Q _m，2，max≤Q _m，2(t)≤Q _m，2，max(26)

In formula, Q _{m, 1}(t) and Q _{m, 2}the reactive power that t two transverters that () is t period m intelligent Sofe Switch export; S _{m, 1, max}, S _{m, 2, max}, Q _{m, 1, max}, Q _{m, 2, max}the access capacity being respectively m intelligent Sofe Switch two transverters and the reactive power upper limit that can export.

3) according to cone optimize canonical form to step 2) described in power distribution network interconnection switch and intelligent Sofe Switch synthetic operation timing optimization model in objective function and non-linear constrain carry out Based On The Conic Model conversion, concrete method for transformation is as follows:

(1) objective function switch motion expense equivalence loss $E_{S,loss}=C_S{\Σ}_{t=1}^{N_T}{\Σ}_{i=1}^{N_N}{\Σ}_{j\∈\mathrm{\Ω}\left(i\right)}|{\mathrm{\α}}_{ij}\left(t\right)-{\mathrm{\α}}_{ij}(t-1)|---\left(2\right)$ In containing absolute value item | α _ij(t)-α _ij(t-1) |, introduce auxiliary variable

M ₀(t)=| α _ii(t)-α _ij(t-1) |=max{ α _ij(t)-α _ij(t-1), α _ij(t-1)-α _ij(t) }, and increase constraint

M ₀(t)≥0 (27)

M ₀(t)≥α _ij(t)-α _ij(t-1) (28)

M ₀(t)≥α _ij(t-1)-α _ij(t) (29)

(2) objective function via net loss with in constraint condition (10), (11), (14) ~ (18) and (21) containing quadratic term with adopt U _{2, i}(t) and I _{2, ij}t () replaces quadratic term with by its linearization.

(3) running wastage of objective function intelligence Sofe Switch

$E_{SNOP,loss}={\Σ}_{t=1}^{N_T}{\Σ}_{m=1}^{N_{SNOP}}\left(A_{m,1}\left|P_{m,1}\left(t\right)\right|+A_{m,2}\left|P_{m,2}\left(t\right)\right|\right)\mathrm{\Δ}t---\left(4\right)$ With

Intelligence Sofe Switch runs constraint condition P _{m, 1}(t)+P _{m, 2}(t)+A _{m, 1}| P _{m, 1}(t) |+A _{m, 2}| P _{m, 2}(t) | containing absolute value item in=0 (22) | P _{m, 1}(t) | with | P _{m, 2}(t) |, introduce auxiliary variable M ₁(t)=| P _{m, 1}(t) |=max{P _{m, 1}(t) ,-P _{m, 1}(t) } and M ₂(t)=| p _{m, 2}(t) |=max{P _{m, 2}(t) ,-P _{m, 2}(t) }, and increase constraint

M ₁(t)≥0 (29)

M ₂(t)≥0 (30)

M ₁(t)≥P _m，1(t) (31)

M ₁(t)≥-P _m，1(t) (32)

M ₂(t)≥P _m，2(t) (33)

M ₂(t)≥-P _m，2(t) (34)

(4) system load flow constraint for through above-mentioned steps replace after non-linear constrain, by its loose for second order cone retrain

||[2P _ij(t) 2Q _ij(t) I _2，ij(t)-U _2，i(t)] ^T|| ₂≤I _2，ij(t)-U _2，i(t) (35)

(5) intelligent Sofe Switch runs about intrafascicular $\sqrt{P_{m,1}^2\left(t\right)+Q_{m,1}^2\left(t\right)}\≤S_{m,1,\max }---\left(23\right)$ With

for non-linear constrain, be converted into rotating cone constraint

$P_{m,1}^2\left(t\right)+Q_{m,1}^2\left(t\right)\≤2\frac{s_{m,1,\max }}{\sqrt{2}}\frac{s_{m,1,\max }}{\sqrt{2}}---\left(36\right)$

$P_{m,2}^2\left(t\right)+Q_{m,2}^2\left(t\right)\≤2\frac{s_{m,2,\max }}{\sqrt{2}}\frac{s_{m,2,\max }}{\sqrt{2}}---\left(37\right)$

4) through step 3) conversion, respectively by objective function linearization, non-linear constrain is converted into linear restriction, second order cone constraint or rotating cone constraint, adopts CPLEX solver to solve the mathematical model obtained;

5) export step 4) solving result, comprise on off state, intelligent Sofe Switch optimal transmission performance number, network power flow solutions and target function value.

The present invention is based on while cone optimized algorithm achieves on off state, intelligent Sofe Switch optimal transmission power and trend and solve.Establish the mathematical model considering the power distribution network interconnection switch of switch motion expense and the timing optimization problem of intelligent Sofe Switch synthetic operation, not only consider switch motion and intelligent Sofe Switch operation constraint from discontinuity surface time single, and consider continuity and the sequential relationship of switch change between adjacent time section.

Provide instantiation below:

For the present embodiment, first input the resistance value of circuit element in IEEE 33 node system as shown in Figure 2, the active power of load cell, reactive power, network topology annexation, detail parameters is in table 1 and table 2; Then the on-position setting 5 typhoon group of motors is node 10,16,17,30,33, access capacity is respectively 500kVA, 300kVA, 200kVA, 200kVA, 300kVA, the on-position of 3 photovoltaic systems is node 7,13,27, access capacity is respectively 500kVA, 300kVA, 400kVA, and power factor is 1.0; Again set one group of SNOP and access power distribution network, replace interconnection switch TS1, the capacity of two transverters is 500kVA, and reactive power exports the upper limit and is 200kVar; Then, in units of sky, with 1 hour for the time interval, utilize load forecasting method to simulate the day operation curve of load and wind-powered electricity generation, photovoltaic, as shown in Figure 3; The reference voltage finally arranging system is 12.66kV, reference power is 1MVA, and maximum value M gets 9999.

Table 1 IEEE33 node example load on-position and power

Table 2 IEEE33 node example line parameter circuit value

Discontinuity surface distich network switch and intelligent Sofe Switch when the present embodiment was one with 1 hour the power distribution network deposited carries out timing optimization, optimum results is in table 3, and the switch motion situation of scheme 2 as shown in Figure 4.

Table 3 different switch fare paths optimum results

Performing the computer hardware environment optimizing calculating is Intel (R) Xeon (R) CPU E5-1620, and dominant frequency is 3.70GHz, inside saves as 32GB; Software environment is Windows 7 operating system.

Prioritization scheme considers different switch motion expense conversion coefficients, to interconnection switch and intelligent Sofe Switch and the power distribution network deposited run and carry out timing optimization, and consider the loss that produces in intelligent Sofe Switch through-put power process, the active power of intelligent Sofe Switch transmission is shown in Fig. 5 with the reactive power going out to send at node 22.Along with the increase of switch motion expense conversion coefficient, switch motion number of times obviously reduces, and contributes to the serviceable life of improving switch.On the other hand, network reconfiguration and intelligent Sofe Switch running optimizatin can improve the operation voltage level of system to a certain extent, as shown in Figure 6, improve the quality of power supply further, improve power supply reliability.

The mathematics essence of the timing optimization problem of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation is extensive mixed integer nonlinear programming problem, and current existing optimization method cannot solve this problem mostly.A kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion that the present invention proposes, can solve problems fast and accurately, and can ensure the optimality of solution.For scheme two, adopt a kind of hybrid solution method optimized based on simulated annealing and cone to solve simultaneously, and compare the optimality of separating and calculated performance, comparative result is in table 4.

The different method for solving calculated performance of table 4 compares

/	The inventive method	Method for mixing and optimizing
			Target function value (unit)	463.08	485.31
Solve the time (s)	27.12	1429.68

Claims (5)

1. consider a power distribution network intelligence Sofe Switch running optimizatin method for switch motion simultaneously, it is characterized in that, comprise the steps:

1) according to selected distribution system, incoming line parameter, load level and network topology annexation, system cloud gray model voltage levvl and branch current restriction, distributed power source on-position, type and capacity and parameter, intelligence Sofe Switch on-position and capacity and parameter, running optimizatin cycle internal loading and distributed power source operation characteristic prediction curve, and system reference voltage and reference power initial value;

2) according to step 1) the distribution system structure and parameters that provides, consider the switch motion expense equivalence loss of via net loss, network reconfiguration and the running wastage of intelligent Sofe Switch simultaneously, set up the timing optimization model of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation, comprise: choosing root node is balance node, setting distribution system running wastage is minimum is objective function, considers that network topology constraint, system load flow constraint, system cloud gray model constraint, intelligent Sofe Switch run constraint respectively;

3) according to cone optimize canonical form to step 2) described in power distribution network interconnection switch and intelligent Sofe Switch synthetic operation timing optimization model in objective function and non-linear constrain carry out Based On The Conic Model conversion;

4) through step 3) conversion, respectively by objective function linearization, non-linear constrain is converted into linear restriction, second order cone constraint or rotating cone constraint, adopts CPLEX solver to solve the mathematical model obtained;

5) export step 4) solving result, comprise on off state, intelligent Sofe Switch optimal transmission performance number, network power flow solutions and target function value.

2. a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion according to claim 1, is characterized in that, step 2) described in distribution system running wastage minimum be objective function, be expressed as

min f＝E _S，loss+E _L，loss+E _SNOP，loss

In formula, switch motion expense equivalence loss E _{s, loss}, via net loss E _{l, loss}with the running wastage E of intelligent Sofe Switch _{sNOP, loss}represent with following formula respectively

$E_{S,loss}=C_S{\mathrm{\Σ}}_{t=1}^{N_T}{\mathrm{\Σ}}_{i=1}^{N_N}{\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}|{\mathrm{\α}}_{ij}\left(t\right)-{\mathrm{\α}}_{ij}(t-1)|$

$E_{L,loss}={\mathrm{\Σ}}_{t=1}^{N_T}{\mathrm{\Σ}}_{i=1}^{N_N}{\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{r}_{i,j}{I}_{ij}^2\left(t\right)\mathrm{\Δ}t$

$E_{SNOP,loss}={\mathrm{\Σ}}_{t=1}^{N_T}{\mathrm{\Σ}}_{m=1}^{N_{SNOP}}\left(A_{m,1}\right|P_{m,1}\left(t\right)|+A_{m.2}|P_{m,2}\left(t\right)\left|\right)\mathrm{\Δ}t$

In formula, C _sfor switch motion expense equivalent conversion coefficient; Δ t optimizes the period interval calculated; N _tfor optimizing the time hop count calculated, N _nfor the node total number in system, N _sNOPfor accessing the number of intelligent Sofe Switch in system; The set of the adjacent node that Ω (i) is node i; α _ijt () is the on off state of t period branch road ij; r _ijfor the resistance of branch road ij, I _ijt () flows to the current amplitude of node j for t period node i; P _{m, 1}(t) and P _{m, 2}t () is the meritorious output power of two transverters of t period m intelligent Sofe Switch, A _{m, 1}and A _{m, 2}it is the active loss coefficient of two transverters of m intelligent Sofe Switch.

3. a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion according to claim 1, is characterized in that, step 2) described in network topology constraint representation be

α _ij(t)＝β _ij(t)+β _ji(t)

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=1,\∀i\∈N_N\backslash N_S$

${\mathrm{\Σ}}_{j\∈\mathrm{\Ω}\left(i\right)}{\mathrm{\β}}_{ij}\left(t\right)=0,\∀i\∈N_S$

α _ij(t)∈{0，1}

β _ij(t)∈{0，1}

In formula, N _sfor the source node number in system; β _ijt () represents t period node i and node j relation, be 1, otherwise be 0 when node j is the parent node of node i.

4. a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion according to claim 1, is characterized in that, step 2) described in system load flow constraint representation be

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(P_{ji}\right(t)-r_{ji}{I}_{ji}^2\left(t\right))+P_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{P}_{ik}\left(t\right)$

${\mathrm{\Σ}}_{j\∈\mathrm{\Φ}\left(i\right)}\left(Q_{ji}\right(t)-x_{.ii}{I}_{ji}^2\left(t\right))+Q_i\left(t\right)={\mathrm{\Σ}}_{k\∈\mathrm{\Ψ}\left(i\right)}{Q}_{ik}\left(t\right)$

P _i(t)＝P _DG，i(t)+P _SNOP，i(t)-P _LOAD，i(t)

Q _i(t)＝Q _DG，i(t)+Q _SNOP，i(t)-Q _LOAD，i(t)

$I_{ij}^2\left(t\right)=\frac{P_{ij}^2\left(t\right)+Q_{ij}^2\left(t\right)}{U_i^2\left(t\right)}$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\right(t)+x_{ij}{Q}_{ij}\left(t\right))+(r_{ij}^2+x_{ij}^2)I_{ij}^2\left(t\right)+M(1-{\mathrm{\α}}_{ij}(t))\≥0$

$U_i^2\left(t\right)-U_j^2\left(t\right)-2\left(r_{ij}{P}_{ij}\right(t)+x_{ij}{Q}_{ij}\left(t\right))+(r_{ij}^2+x_{ij}^2)I_{ij}^2\left(t\right)-M(1-{\mathrm{\α}}_{ij}(t))\≤0$

In formula, Φ (i) take node i as the branch road headend node set of endpoint node, and Ψ (i) take node i as the branch road set of end nodes of headend node; U _it () is the voltage magnitude of t period node i, x _ijfor the reactance of branch road ij; P _ijt () flows to the active power of node j, Q for t period node i _ijt () flows to the reactive power of node j for t period node i; The set of the adjacent node that Ω (i) is node i; P _i(t) active power sum for t period node i is injected, P _{dG, i}(t), P _{sNOP, i}(t), P _{lOAD, i}t () is respectively active power, the active power of SNOP transmission, the active power of load consumption that in t period node i, distributed power source injects, Q _i(t) active power sum for t period node i is injected, Q _{dG, i}(t), Q _{sNOP, i}(t), Q _{lOAD, i}the reactive power that the reactive power that t () is respectively reactive power that distributed power source in t period node i injects, SNOP sends, load consume; M is a maximum value.

5. a kind of power distribution network intelligence Sofe Switch running optimizatin method simultaneously considering switch motion according to claim 1, is characterized in that, step 2) described in intelligent Sofe Switch run constraint representation and be

P _m，1(t)+P _m，2(t)+A _m，1|P _m，1(t)|+A _m，2|P _m，2(t)|＝0

$\sqrt{P_{m,1}^2\left(t\right)+Q_{m,1}^2\left(t\right)}\≤S_{m,1,\max }$

$\sqrt{P_{m,2}^2\left(t\right)+Q_{m,2}^2\left(t\right)}\≤S_{m,2,\max }$

-Q _m，1，max≤Q _m，1(t)≤Q _m，1，max

-Q _m，2，max≤Q _m，2(t)≤Q _m，2，max

In formula, Q _{m, 1}(t) and Q _{m, 2}the reactive power that t two transverters that () is t period m intelligent Sofe Switch export; S _{m, 1, max}, S _{m, 2, max}, Q _{m, 1, max}, Q _{m, 2, max}the access capacity being respectively m intelligent Sofe Switch two transverters and the reactive power upper limit that can export.