CN105023058B - Power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion - Google Patents
Power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion Download PDFInfo
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Abstract
Power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion：According to distribution system incoming line parameter, load level and network topology connection relation, system operation voltage level and branch current limitation, distributed generation resource on-position, type and capacity and parameter, intelligent Sofe Switch on-position and capacity and parameter, running optimizatin period internal loading and distributed generation resource operation characteristic prediction curve, system reference voltage and reference power initial value；Establish the timing optimization model of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation；According to the canonical form of cone optimization in the timing optimization model of power distribution network interconnection switch and intelligent Sofe Switch synthetic operation object function and nonlinear restriction carry out Based On The Conic Model conversion；Object function is linearized respectively, nonlinear restriction is converted into linear restriction, second order cone constraint or rotating cone constraint, and obtained mathematical model is solved using CPLEX solvers.The invention avoids cumbersome iteration and a large amount of test.
Description
Technical field
The present invention relates to a kind of timing optimization methods of distribution network operation.Considering switch motion simultaneously more particularly to a kind of
Power distribution network intelligence Sofe Switch running optimizatin method.
Background technology
Highest attention to the energy and environment so that the development of power distribution network is faced with new pressure and challenge, these pressure with
It challenges while being also the opportunity for pushing conventional electrical distribution net to develop to intelligent distribution network.In intelligent distribution network, controllable device is increasingly
Increase, network structure and the method for operation are more flexible changeable, and advanced Distribution Automation Technology, advanced Information and Communication Technology are able to
Extensive use, distributed generation resource, energy storage, Demand-side resource etc. begin participating in the operation and optimization of power distribution network.Intelligent distribution network
Development and the extensive use of new energy power generation technology are pushing the deep reform of adapted power mode and running fluidization air flow.
Intelligent Sofe Switch (Soft Normally Open Point, SNOP) device is exactly to derive in the above context
A kind of novel intelligent power distribution equipment of the traditional interconnection switch of substitution.Compared with switching manipulation, the power control of SNOP is safer,
Reliably, it might even be possible to realize real-time optimization, randomness and fluctuation that distributed generation resource and load are brought can be successfully managed.But
It is that the realization of SNOP is based primarily upon full-control type power electronic device, and the cost of these devices itself is higher, in a short time in power distribution network
Interconnection switch can not possibly be replaced completely by SNOP.This running optimizatin for allowing for power distribution network needs overall thinking contact to open
It closes and SNOP and the case where deposit, optimal operation model will be one and need while solve discrete magnitude (on off state) and continuous quantity
The mixed integer nonlinear programming problem of (SNOP transimission powers).
In intelligent distribution network, the distributed generation resource accessed extensively exacerbates the uncertainty of system operation, it is contemplated that opens
The factors such as loss and dash current are closed, interconnection switch can not possibly frequently be cut-off, and traditional network reconfiguration is difficult to accomplish power distribution network
Real-time adjustment, and SNOP can then change transimission power in real time, adjust operating status, to cope with band after distributed generation resource access
Come the problems such as a series of voltage is out-of-limit, circuit overload, it is therefore desirable to from the angle of time series to power distribution network optimization problem into
Row modeling, and need to consider interconnection switch and the coordination optimization problem of SNOP.After considering its temporal aspect, distribution network operation
In optimization process, making rational planning for for switch motion just becomes a urgent problem to be solved.Meanwhile considering switch motion expense
SNOP operation timing optimization problem can with when discontinuity surface number increase solve dimension sharply increase, become mix on a large scale it is whole
Number nonlinear programming problem, causes its solution to become more difficult or even infeasible.
For solving this kind of extensive mixed integer nonlinear programming problem, it is also difficult to find at present a kind of quick, effective
Method for solving.The solution of the problem has been proposed at present and has developed a variety of optimization methods, mainly have including：1) traditional
Mathematics Optimization Method, including analytic method, successive elimination method etc.；2) heuritic approach, including Sensitivity Analysis Method, specially
Family's system etc.；3) randomized optimization process, including genetic algorithm, particle cluster algorithm etc..
Although the above method or technology have certain application, also all there is clearly disadvantageous, as traditional mathematics are excellent
Although change method can theoretically carry out global optimizing, it is inevitably present " dimension calamity " problem in practical application, calculates
Explosive surge is often presented in time；Heuritic approach requires, there are one polynomial time, to calculate in terms of time complexity
Speed is fast, but obtained optimal solution either lacks optimality in mathematical meaning or only locally optimal solution；Although random excellent
The last solution that change method is searched is unrelated with initial solution, but the power distribution network of different scales is needed to reset its control ginseng
Number, population quantity, iterations etc., to ensure to find globally optimal solution with larger probability.Heuristic and random device
It is suitable for solving integer programming problem, but for considering that the power distribution network timing optimization of interconnection switch and SNOP synthetic operations is asked more
Topic, mathematics is substantially extensive mixed integer nonlinear programming problem, so traditional mathematics optimization method, heuritic approach pair
In on such issues that solve, speed or precision cannot mostly be met the requirements simultaneously.Therefore, it is necessary to one kind accurately, rapid solving is above-mentioned
The model and algorithm of optimization problem.
Invention content
The technical problem to be solved by the invention is to provide one kind can consider switch motion expense it is equivalent loss,
The power distribution networks running wastage such as SNOP running wastages and via net loss, while determining that rational switch motion sequential and SNOP are run
Consider the power distribution network intelligence Sofe Switch running optimizatin method of switch motion.
The technical solution adopted in the present invention is：Power distribution network intelligence Sofe Switch operation that is a kind of while considering switch motion is excellent
Change method, includes the following steps：
1) according to selected distribution system, incoming line parameter, load level and network topology connection relation, system operation
Voltage level and branch current limitation, distributed generation resource on-position, type and capacity and parameter, intelligent Sofe Switch on-position
With capacity and parameter, running optimizatin period internal loading and distributed generation resource operation characteristic prediction curve and system reference voltage
With reference power initial value；
2) the distribution system structure and parameter provided according to step 1), while considering the switch of via net loss, network reconfiguration
The running wastage of the equivalent loss of action expense and intelligent Sofe Switch, establishes power distribution network interconnection switch and intelligent Sofe Switch synthetic operation
Timing optimization model, including：Selection root node is balance nodes, sets the minimum object function of distribution system running wastage,
Network topology constraint, system load flow constraint, system operation constraint, intelligent Sofe Switch operation constraint are considered respectively；
3) according to the canonical form of cone optimization to the power distribution network interconnection switch and intelligence Sofe Switch collaboration fortune described in step 2)
Object function and nonlinear restriction in capable timing optimization model carry out Based On The Conic Model conversion；
4) conversion Jing Guo step 3), respectively linearizes object function, and nonlinear restriction is converted into linear restriction, second order
Cone constraint or rotating cone constraint, obtained mathematical model is solved using CPLEX solvers；
5) solving result of step 4), including on off state, intelligent Sofe Switch optimal transmission performance number, network trend are exported
And target function value as a result.
The minimum object function of distribution system running wastage described in step 2), is expressed as
Min f=ES, loss+EL, loss+ESNOP, loss
In formula, the equivalent loss E of switch motion expenseS, loss, via net loss EL, lossWith the running wastage of intelligent Sofe Switch
ESNOP, lossIt is indicated respectively with following formula
In formula, CSFor switch motion expense equivalent conversion coefficient；Δ t is the period interval that optimization calculates；NTIt is calculated for optimization
When hop count, NNFor the node total number in system, NSNOPTo access the number of intelligent Sofe Switch in system；Ω (i) is node i
The set of adjacent node；αij(t) on off state for being t period branches ij；rijFor the resistance of branch ij, Iij(t) it is the t periods
Point i flows to the current amplitude of node j；PM, 1(t) and PM, 2(t) it is the active of two transverters of m-th of intelligent Sofe Switch of t periods
Output power, AM, 1And AM, 2For the active loss coefficient of two transverters of m-th of intelligent Sofe Switch.
Network topology constraint representation described in step 2) is
αij(t)=βij(t)+βji(t)
αij(t) { 0,1 } ∈
βij(t) { 0,1 } ∈
In formula, NSFor the source node number in system；βij(t) indicate that t periods node i and node j relationships, node j are section
It is 1 when the parent node of point i, is otherwise 0.
System load flow constraint representation described in step 2) is
Pi(t)=PDG, i(t)+PSNOP, i(t)-PLOAD, i(t)
Qi(t)=QDG, i(t)+QSNOP, i(t)-QLOAD, i(t)
In formula, Φ (i) is using node i as the branch headend node set of endpoint node, and Ψ (i) is using node i as headend node
Branch set of end nodes；Ui(t) it is the voltage magnitude of t period node is, xijFor the reactance of branch ij；Pij(t) it is the t periods
Node i flows to the active power of node j, Qij(t) reactive power of node j is flowed to for t period node is；Ω (i) is node i
The set of adjacent node；Pi(t) it is the sum of the active power injected in t period node is, PDG, i(t)、PSNOP, i(t)、PLOAD, i(t)
The active power of distributed generation resource injection, the active power of SNOP transmission, load consume active respectively in t period node is
Power, Qi(t) it is the sum of the active power injected in t period node is, QDG, i(t)、QSNOP, i(t)、QLOAD, i(t) when being respectively t
The reactive power of reactive power, load consumption that the reactive power of Duan Jiediani upper distributed generation resource injections, SNOP are sent out；M is one
A maximum.
Intelligent Sofe Switch described in step 2) runs constraint representation
PM, 1(t)+PM, 2(t)+AM, 1|PM, 1(t)|+AM, 2|PM, 2(t) |=0
-QM, 1, max≤QM, 1(t)≤QM, 1, max
-QM, 2, max≤QM, 2(t)≤QM, 2, max
In formula, QM, 1(t) and QM, 2(t) it is the reactive power of two transverters output of m-th of intelligent Sofe Switch of t periods；
SM, 1, max、SM, 2, max、QM, 1, max、QM, 2, maxIt the access capacity of respectively m-th intelligent two transverter of Sofe Switch and can be output
The reactive power upper limit.
The power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion of the present invention, based on solution
Discontinuity surface interconnection switch and SNOP and the distribution network operation timing optimization deposited is asked when considering multiple under the premise of switch motion expense
Topic, mathematics essence is extensive mixed integer nonlinear programming problem (MINLP), and current existing method is difficult to quick standard
Really solve.Basic principle of the present invention according to cone optimization algorithm, bores the object function and constraints of Optimized model
Conversion, converts former problem to MIXED INTEGER Second-order cone programming problem (MISOCP), greatly reduces solution difficulty, is easy to use
Solution tool is solved.Cone optimization method of the present invention can carry out network reconfiguration and SNOP running optimizatin problems
Unify legislation so that the problem of complicated mixed integer nonlinear programming, which solves, to be achieved, and cumbersome iteration and big is avoided
The test of amount has in calculating speed and is significantly promoted.Also, because of graceful geometry and special place possessed by cone
Reason mode can ensure the optimality of the solution of institute's Solve problems, apply it in the timing optimization problem of SNOP operations,
Optimal system operation scheme can be quickly obtained.
Description of the drawings
Fig. 1 is the present invention while considering the flow chart of the power distribution network intelligence Sofe Switch running optimizatin method of switch motion；
Fig. 2 is 33 node examples of IEEE and distributed generation resource, the on-positions SNOP figure；
Fig. 3 is distributed generation resource and load operation Predicting Performance Characteristics curve；
Fig. 4 be switch motion expense conversion coefficient be 5kWh/ when it is each when discontinuity surface switch motion situation；
Fig. 5 a are that different switch motion expense conversion coefficients correspond to SNOP transmitting active power situations of change；
Fig. 5 b are that different switch motion expense conversion coefficients correspond to SNOP and send out reactive power situation of change；
Fig. 6 is different 18 voltage change situations of switch motion expense conversion coefficient corresponding node.
Specific implementation mode
With reference to embodiment and attached drawing to the power distribution network intelligence Sofe Switch that is a kind of while considering switch motion of the present invention
Running optimizatin method is described in detail.
As shown in Figure 1, the power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion of the present invention,
Include the following steps：
1) according to selected distribution system, incoming line parameter, load level and network topology connection relation, system operation
Voltage level and branch current limitation, distributed generation resource on-position, type and capacity and parameter, intelligent Sofe Switch (SNOP) connect
Enter position and capacity and parameter, running optimizatin period internal loading and distributed generation resource operation characteristic prediction curve and system base
The initial values such as quasi- voltage and reference power；
2) the distribution system structure and parameter provided according to step 1), while considering the switch of via net loss, network reconfiguration
The running wastage of the equivalent loss of action expense and intelligent Sofe Switch, establishes power distribution network interconnection switch and intelligent Sofe Switch synthetic operation
Timing optimization model, including：Selection root node is balance nodes, sets the minimum object function of distribution system running wastage,
Network topology constraint, system load flow constraint, system operation constraint, intelligent Sofe Switch operation constraint are considered respectively；Wherein：
(1) the minimum object function of distribution system running wastage described in, is expressed as
Min f=ES, loss+EL, loss+ESNOP, loss (1)
In formula, the equivalent loss E of switch motion expenseS, loss, via net loss EL, lossWith the running wastage of intelligent Sofe Switch
ESNOP, lossIt is indicated respectively with following formula
In formula, CSFor switch motion expense equivalent conversion coefficient；Δ t is the period interval that optimization calculates；NTIt is calculated for optimization
When hop count, NNFor the node total number in system, NSNOPTo access the number of intelligent Sofe Switch in system；Ω (i) is node i
The set of adjacent node；αij(t) on off state for being t period branches ij；rijFor the resistance of branch ij, Iij(t) it is the t periods
Point i flows to the current amplitude of node j；PM, 1(t) and PM, 2(t) it is the active of two transverters of m-th of intelligent Sofe Switch of t periods
Output power, AM, 1And AM, 2For the active loss coefficient of two transverters of m-th of intelligent Sofe Switch.
(2) the network topology constraint representation described in is
αij(t)=βij(t)+βji(t) (5)
αij(t) { 0,1 } (8) ∈
βij(t) { 0,1 } (9) ∈
In formula, NSFor the source node number in system；βij(t) indicate that t periods node i and node j relationships, node j are section
It is 1 when the parent node of point i, is otherwise 0.
(3) the system load flow constraint representation described in is
Pi(t)=PDG, i(t)+PSNOP, i(t)-PLOAD, i(t) (12)
Qi(t)=QDG, i(t)+QSNOP, i(t)-QLOAD, i(t) (13)
In formula, Φ (i) is using node i as the branch headend node set of endpoint node, and Ψ (i) is using node i as headend node
Branch set of end nodes；Ui(t) it is the voltage magnitude of t period node is, xijFor the reactance of branch ij；Pij(t) it is the t periods
Node i flows to the active power of node j, Qij(t) reactive power of node j is flowed to for t period node is；Ω (i) is node i
The set of adjacent node；Pi(t) it is the sum of the active power injected in t period node is, PDG, i(t)、PSNOP, i(t)、PLOAD, i(t)
The active power of distributed generation resource injection, the active power of SNOP transmission, load consume active respectively in t period node is
Power, Qi(t) it is the sum of the active power injected in t period node is, QDG, i(t)、QSNOP, i(t)、QLOAD, i(t) when being respectively t
The idle work(of reactive power, load consumption that the reactive power of Duan Jiediani upper distributed generation resource injections, intelligent Sofe Switch are sent out
Rate；M is a maximum.
(4) the system operation constraint representation described in is
-Mαij(t)≤Pii(t)≤Mαij(t) (19)
-Mαij(t)≤Qij(t)≤Mαij(t) (20)
In formula, UI, minAnd UI, maxThe respectively minimum allowable voltage value and maximum allowable voltage of node i；IIj, maxFor this
The maximum allowed current value of branch.
(5) the intelligent Sofe Switch described in runs constraint representation
PM, 1(t)+PM, 2(t)+AM, 1|PM, 1(t)|+AM, 2|PM, 2(t) |=0 (22)
-QM, 1, max≤QM, 1(t)≤QM, 1, max (25)
-QM, 2, max≤QM, 2(t)≤QM, 2, max (26)
In formula, QM, 1(t) and QM, 2(t) it is the reactive power of two transverters output of m-th of intelligent Sofe Switch of t periods；
SM, 1, max、SM, 2, max、QM, 1, max、QM, 2, maxIt the access capacity of respectively m-th intelligent two transverter of Sofe Switch and can be output
The reactive power upper limit.
3) according to the canonical form of cone optimization to the power distribution network interconnection switch and intelligence Sofe Switch collaboration fortune described in step 2)
Object function and nonlinear restriction in capable timing optimization model carry out Based On The Conic Model conversion, and specific method for transformation is as follows：
M0(t)=| αii(t)-αij(t-1) |=max { αij(t)-αij(t-1), αij(t-1)-αij(t) }, and increase constraint
M0(t)≥0 (27)
M0(t)≥αij(t)-αij(t-1) (28)
M0(t)≥αij(t-1)-αij(t) (29)
(2) object function via net lossWith constraints (10),
(11), contain quadratic term in (14)~(18) and (21)WithUsing U2, i(t) and I2, ij(t) quadratic term is replacedWithIt is linearized.
(3) running wastage of object function intelligence Sofe Switch
Intelligent Sofe Switch operation constraints PM, 1(t)+PM, 2(t)+AM, 1|PM, 1(t)|+AM, 2|PM, 2(t) | in=0 (22)
Contain absolute value term | PM, 1(t) | and | PM, 2(t) |, introduce auxiliary variable M1(t)=| PM, 1(t) |=max { PM, 1(t) ,-PM, 1
And M (t) }2(t)=| pM, 2(t) |=max { PM, 2(t) ,-PM, 2(t) }, and increase constraint
M1(t)≥0 (29)
M2(t)≥0 (30)
M1(t)≥PM, 1(t) (31)
M1(t)≥-PM, 1(t) (32)
M2(t)≥PM, 2(t) (33)
M2(t)≥-PM, 2(t) (34)
(4) system load flow constrainsFor the nonlinear restriction after above-mentioned steps are replaced, by it
It is loose to be constrained for second order cone
||[2Pij(t) 2Qij(t) I2, ij(t)-U2, i(t)]T||2≤I2, ij(t)-U2, i(t) (35)
For nonlinear restriction, it is converted into rotating cone constraint
4) conversion Jing Guo step 3), respectively linearizes object function, and nonlinear restriction is converted into linear restriction, second order
Cone constraint or rotating cone constraint, obtained mathematical model is solved using CPLEX solvers；
5) solving result of step 4), including on off state, intelligent Sofe Switch optimal transmission performance number, network trend are exported
And target function value as a result.
While on off state, intelligent Sofe Switch optimal transmission power and trend being realized the present invention is based on cone optimization algorithm
It solves.Establish the timing optimization problem of the power distribution network interconnection switch for considering switch motion expense and intelligent Sofe Switch synthetic operation
Mathematical model, not only from it is single when discontinuity surface consider switch motion and the operation constraint of intelligent Sofe Switch, but also consider phase
When adjacent between discontinuity surface switch change continuity and sequential relationship.
Specific example is given below：
For the present embodiment, the impedance value of circuit element in 33 node systems of IEEE as shown in Figure 2 is inputted first, is born
Active power, the reactive power of lotus element, network topology connection relation, detail parameters are shown in Tables 1 and 2；Then 5 typhoons electricity is set
The on-position of unit be node 10,16,17,30,33, access capacity be respectively 500kVA, 300kVA, 200kVA, 200kVA,
300kVA, the on-positions of 3 photovoltaic systems are node 7,13,27, and access capacity is respectively 500kVA, 300kVA, 400kVA,
Power factor is 1.0；One group of SNOP access power distribution network is set again, replaces interconnection switch TS1, the capacity of two transverters equal
For 500kVA, it is 200kVar that reactive power, which exports the upper limit,；Then, as unit of day, with 1 hour for time interval, using negative
Lotus prediction technique simulates the day operation curve of load and wind-powered electricity generation, photovoltaic, as shown in Figure 3；The finally benchmark electricity of setting system
Pressure is 12.66kV, reference power 1MVA, and maximum M takes 9999.
1 IEEE33 nodes example load on-position of table and power
2 IEEE33 node example line parameter circuit values of table
Discontinuity surface distich network switch and intelligent Sofe Switch and when the power distribution network deposited carries out when the present embodiment with 1 hour was one
Sequence optimizes, and optimum results are shown in Table 3, and the switch motion situation of scheme 2 is as shown in Figure 4.
The different switch fare paths optimum results of table 3
It is Intel (R) Xeon (R) CPU E5-1620 to execute the computer hardware environment that optimization calculates, and dominant frequency is
3.70GHz inside saves as 32GB；Software environment is 7 operating systems of Windows.
Prioritization scheme considers different switch motion expense conversion coefficients, and to interconnection switch and intelligent Sofe Switch and that deposits match
Operation of power networks carries out timing optimization, and considers the loss generated during intelligent Sofe Switch transimission power, intelligent Sofe Switch transmission
Active power and go out the reactive power sent out in node 22 and see Fig. 5.With the increase of switch motion expense conversion coefficient, switch
Action frequency significantly reduces, and helps to improve the service life of switch.On the other hand, network reconfiguration and intelligent Sofe Switch are run excellent
The operation voltage level of system can be improved to a certain extent by changing, as shown in fig. 6, further improving power quality, improving confession
Electric reliability.
The mathematics essence of power distribution network interconnection switch and the timing optimization problem of intelligent Sofe Switch synthetic operation is extensive mixed
Integral nonlinear program-ming problem is closed, current existing optimization method can not solve this problem mostly.One kind proposed by the present invention
The power distribution network intelligence Sofe Switch running optimizatin method for considering switch motion simultaneously, can fast and accurately solve problems, and
It can guarantee the optimality of solution.For scheme two, at the same using a kind of hybrid solution method based on simulated annealing and cone optimization into
Row solves, and is compared to the optimality and calculated performance of solution, and comparison result is shown in Table 4.
The different method for solving calculated performances of table 4 compare
| / | The method of the present invention | Method for mixing and optimizing |
| Target function value (member) | 463.08 | 485.31 |
| Solve the time (s) | 27.12 | 1429.68 |
Claims (5)
1. power distribution network intelligence Sofe Switch running optimizatin method that is a kind of while considering switch motion, which is characterized in that including as follows
Step：
1) according to selected distribution system, incoming line parameter, load level and network topology connection relation, system operation voltage
The limitation of horizontal and branch current, distributed generation resource on-position, type and capacity and parameter, intelligent Sofe Switch on-position and appearance
Amount and parameter, running optimizatin period internal loading and distributed generation resource operation characteristic prediction curve and system reference voltage and base
Quasi- power initial value；
2) the distribution system structure and parameter provided according to step 1), while considering the switch motion of via net loss, network reconfiguration
The running wastage of the equivalent loss of expense and intelligent Sofe Switch, establish power distribution network interconnection switch and intelligent Sofe Switch synthetic operation when
Sequence Optimized model, including：Selection root node is balance nodes, sets the minimum object function of distribution system running wastage, respectively
Consider network topology constraint, system load flow constraint, system operation constraint, intelligent Sofe Switch operation constraint；
3) according to the canonical form of cone optimization to described in step 2) power distribution network interconnection switch and intelligent Sofe Switch synthetic operation
Object function and nonlinear restriction in timing optimization model carry out Based On The Conic Model conversion；
4) conversion Jing Guo step 3), respectively linearizes object function, and nonlinear restriction is converted into linear restriction, second order cone about
Beam or rotating cone constraint, obtained mathematical model is solved using CPLEX solvers；
5) solving result of step 4), including on off state, intelligent Sofe Switch optimal transmission performance number, network power flow solutions are exported
And target function value.
2. power distribution network intelligence Sofe Switch running optimizatin method that is according to claim 1 a kind of while considering switch motion,
It is characterized in that, the minimum object function of distribution system running wastage described in step 2), is expressed as
Min f=ES, loss+EL, loss+ESNOP, loss
In formula, the equivalent loss E of switch motion expenseS, loss, via net loss EL, lossWith the running wastage E of intelligent Sofe SwitchSNOP, loss
It is indicated respectively with following formula
In formula, CSFor switch motion expense equivalent conversion coefficient；Δ t is the period interval that optimization calculates；NTFor optimization calculate when
Hop count, NNFor the node total number in system, NSNOPTo access the number of intelligent Sofe Switch in system；Ω (i) is the adjacent of node i
The set of node；αij(t) on off state for being t period branches ij；rijFor the resistance of branch ij, Iij(t) it is t period node i streams
To the current amplitude of node j；PM, 1(t) and PM, 2(t) it is the active output of two transverters of m-th of intelligent Sofe Switch of t periods
Power, AM, 1And AM, 2For the active loss coefficient of two transverters of m-th of intelligent Sofe Switch.
3. power distribution network intelligence Sofe Switch running optimizatin method that is according to claim 1 a kind of while considering switch motion,
It is characterized in that, the network topology constraint representation described in step 2) is
αij(t)=βij(t)+βji(t)
αij(t) { 0,1 } ∈
βij(t) { 0,1 } ∈
In formula, αij(t) on off state for being t period branches ij；Ω (i) is the set of the adjacent node of node i；NNFor in system
Node total number；NSFor the source node number in system；βij(t) indicate that t periods node i and node j relationships, node j are node i
Parent node when be 1, be otherwise 0.
4. power distribution network intelligence Sofe Switch running optimizatin method that is according to claim 1 a kind of while considering switch motion,
It is characterized in that, the system load flow constraint representation described in step 2) is
Pi(t)=PDG, i(t)+PSNOP, i(t)-PLOAD, i(t)
Qi(t)=QDG, i(t)+QSNOP, i(t)-QLOAD, i(t)
In formula, Φ (i) is using node i as the branch headend node set of endpoint node, and Ψ (i) is using node i as the branch of headend node
Road set of end nodes；Ui(t) it is the voltage magnitude of t period node is, xijFor the reactance of branch ij；Pij(t) it is t period node is
Flow to the active power of node j, Qij(t) reactive power of node j is flowed to for t period node is；Ω (i) is the adjacent segments of node i
The set of point；Pi(t) it is the sum of the active power injected in t period node is, PDG, i(t)、PSNOP, i(t)、PLOAD, i(t) it is respectively
The active power of distributed generation resource injects in t period node is active power, SNOP transmission, the active power of load consumption, Qi
(t) it is the sum of the active power injected in t period node is, QDG, i(t)、QSNOP, i(t)、QLOAD, i(t) it is respectively t period node is
The reactive power of reactive power, load consumption that the reactive power of upper distributed generation resource injection, SNOP are sent out；M is one very big
Value；rijFor the resistance of branch ij；Iij(t) current amplitude of node j is flowed to for t period node is；αij(t) it is t period branches ij
On off state.
5. power distribution network intelligence Sofe Switch running optimizatin method that is according to claim 1 a kind of while considering switch motion,
It is characterized in that, the intelligent Sofe Switch operation constraint representation described in step 2) is
PM, 1(t)+PM, 2(t)+AM, 1|PM, 1(t)|+AM, 2|PM, 2(t) |=0
-QM, 1, max≤QM, 1(t)≤QM, 1, max
-QM, 2, max≤QM, 2(t)≤QM, 2, max
In formula, QM, 1(t) and QM, 2(t) it is the reactive power of two transverters output of m-th of intelligent Sofe Switch of t periods；
SM, 1, max、SM, 2, max、QM, 1, max、QM, 2, maxIt the access capacity of respectively m-th intelligent two transverter of Sofe Switch and can be output
The reactive power upper limit；PM, 1(t) and PM, 2(t) it is the active output work of two transverters of m-th of intelligent Sofe Switch of t periods
Rate；AM, 1And AM, 2For the active loss coefficient of two transverters of m-th of intelligent Sofe Switch.
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