CN106712031B - Active distribution network is sequential-ADAPTIVE ROBUST Optimal Scheduling and dispatching method - Google Patents
Active distribution network is sequential-ADAPTIVE ROBUST Optimal Scheduling and dispatching method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The invention discloses a kind of meter and probabilistic active distribution network it is sequential-robust Optimal Scheduling and dispatching method, wherein Scheduling Framework includes the sequential Optimized model in upper layer and lower layer's robust adaptive model；Dispatching method based on the Scheduling Framework, comprising: for traditional voltage adjusting device in active distribution network, carry out hour grade Optimized Operation, introduce sequential optimum theory, construct upper layer Optimized model, reduce the uncertain influence to decision；Based on upper layer dispatch command, the lower layer's optimization carried out in hour constructs active-idle Coordination and Optimization Model of robust adaptive using robust adaptive optimization method, realizes active control and real-time optimization to power distribution network；The present invention is directed to the active distribution network that extensive renewable energy accesses, and by calling all controllable resources to provide system support, sufficiently reduces the probabilistic adverse effect of renewable energy power output, realizes the reliable of power distribution network, safety, economy, efficient operation.
Description
Technical field
The present invention relates to the Scheduling Framework of active distribution network and dispatching method, more particularly to a kind of meter and probabilistic
The sequential robust Optimal Scheduling of active distribution network and dispatching method.
Background technique
Positive Renewable Energy Development generation technology, be China readjust the energy structure, Economic Development Mode Conversion and realization
The strategic choice of sustainable development, Distributed Power Generation (DG) have become the coke of China's energy sustainable development key technology
Point.The large-scale grid connection of distributed generation resource brings wide influence and huge challenge to the operation of power distribution network.
When existing active distribution network regulating strategy specifically includes that single time section optimization scheduling, dispatches a few days ago and is more
Between scale coordination cooperate layering scheduling.Single time section optimization scheduling does not fully consider traditional adjusting device and distributed generation resource
Difference, it is insufficient to distribution actual motion directive significance；Predicted value of the scheduling dependent on distributed generation resource a few days ago, and distributed electrical
Often there is certain deviation in source actual value and predicted value, and predicted time is longer, and deviation is bigger, therefore operation plan is past a few days ago
It is not high toward precision；The layering scheduling of Multiple Time Scales cooperation is still the passive adjusting after power distribution network security constraint is out-of-limit, no
It is real active control and real-time optimization, and its problem that be easy that there are calculation times more, at high cost.
Therefore, that there are scheduling time scales is too low, to distributed generation resource Uncertainty Management scarce capacity etc. for the prior art
Problem cannot ensure the reliable of power distribution network, safety, economy, efficient operation.
Summary of the invention
Goal of the invention: it to overcome the shortcomings of the prior art, provides a kind of using double-deck stereo Scheduling Framework, hour
Meter and probabilistic active distribution network of the grade Optimized Operation in conjunction with Optimized Operation in hour be sequential-robust Optimal Scheduling
And dispatching method.
Technical solution: a kind of meter and probabilistic active distribution network it is sequential-robust Optimal Scheduling, including upper layer is excellent
Change model and lower layer's Optimized model；
Upper layer Optimized model optimizes traditional adjusting device, and lower suboptimization model carries out distributed generation resource excellent in real time
Change, lower layer's Optimized model receives the dispatch command of upper layer Optimized model, carries out lower layer's optimization in hour, and lower layer's Optimized model will
Information feeds back to upper layer Optimized model.
Wherein, the upper layer Optimized model be sequential Optimized model, lower layer's Optimized model be robust adaptive it is active-
Idle Coordination and Optimization Model；
The sequential Optimized model are as follows:
Wherein, u indicates that control variable, x indicate state variable, and L (u, x)=0 indicates equality constraint, and G (u, x)≤0 is indicated
Inequality constraints, PlossIndicate system losses；
Active-idle Coordination and Optimization Model of the robust adaptive are as follows:
Wherein,For the objective function under undisturbed state,To there is state of disturbance
Under objective function, g (P, Q)=0 indicate equality constraint, h (P, Q)≤0 indicate inequality constraints；
The affine expression formula of maximum active power output of distributed generation resource are as follows:
Wherein, pmaxFor distributed generation resource maximum active power output,For the predicted value of distributed generation resource maximum active power output,For maximum perturbation amount, ε is distributed generation resource power output Discontinuous Factors, and Ω is uncertain collection；
The auto-adaptive function of the optimal active power output of distributed generation resource, is shown below:
P=p0+p(ε) (4)
P (ε)=pα1ε+pα2εTε+… (5)
Wherein, p is the optimal active power output of distributed generation resource, p0For in the case of undisturbed distributed generation resource it is optimal it is active go out
Power, p (ε) are auto-adaptive function of the distributed generation resource active power output with shock wave, pα1It is the one of distributed generation resource active power output
Rank disturbance quantity, pα2For the second order disturbance quantity of distributed generation resource active power output, εTIt is the transposition of ε；Wherein formula (5) is multinomial
Function, the specific number of item number are related to the expression of Optimized model；
The auto-adaptive function of distributed generation resource OPTIMAL REACTIVE POWER power output, is shown below:
Q=q0+q(ε) (6)
Q (ε)=qα1ε+qα2εTε+… (7)
Wherein, Q is distributed generation resource OPTIMAL REACTIVE POWER power output, q0OPTIMAL REACTIVE POWER for distributed generation resource in the case of undisturbed goes out
Power, q (ε) are auto-adaptive function of the idle power output of distributed generation resource with shock wave, qα1It is the one of the idle power output of distributed generation resource
Rank disturbance quantity, qα2For the second order disturbance quantity of the idle power output of distributed generation resource；Wherein formula (7) is polynomial function, item number tool
Body number is related to the expression of Optimized model.
A kind of dispatching method based on above-mentioned Scheduling Framework, comprising the following steps:
(1) upper layer Optimized model is constructed
For traditional voltage adjusting device in active distribution network, hour grade Optimized Operation is carried out, sequential optimum theory is introduced, into
The sequential rolling optimization of row, constructs upper layer Optimized model；
(2) lower layer's Optimized model is constructed
Based on upper layer dispatch command, lower layer's optimization in hour is carried out, gives full play to the system support energy of distributed generation resource
Uncertain variables are processed into the form in section by power, using robust adaptive optimization method, construct robust adaptive it is active-nothing
Function Coordination and Optimization Model, i.e. lower layer's Optimized model.
Wherein, the step (1) further comprises:
(a) operation plan is formulated on upper layer
When operation plan is formulated on upper layer, while considering traditional adjusting device and distributed generation resource, but main actions tradition from
Adjusting device is dissipated, the regulation and control instruction of distributed generation resource is mainly derived from lower layer's optimization；
(b) operation plan is obtained
Upper layer optimization, for a dispatching cycle, is started to optimize decision, obtains operation plan each dispatching cycle with T；
(c) operation plan is updated
If current time is more than 1 hour apart from the conventional discrete device action time, every T1Time is once rolled
Optimization updates operation plan；
If current time apart from the conventional discrete device action time less than 1 hour, every T2Time is once rolled
Optimization updates operation plan；
T before conventional discrete device action3Time, then a suboptimization is carried out, update operation plan.
The mathematical model of upper layer optimization are as follows:
Wherein, u indicates that control variable, x indicate state variable, and L (u, x)=0 indicates equality constraint, and G (u, x)≤0 is indicated
Inequality constraints, PlossIndicate system losses.
Wherein, upper layer optimization dispatching cycle T value range be 1h~for 24 hours, T1Value range be 20min~1h,
T2Value range be 10min~20min, T3Value range be 3min~10min.
In addition, lower layer's optimization only regulates and controls the active power output and idle power output of distributed generation resource, distributed generation resource is carried out real
Shi Youhua；Dispatch command based on upper layer, lower layer's optimization use active-idle coordination optimizing method of robust adaptive, will not be really
Determining variable processing is range format, constructs auto-adaptive function, robust adaptive Optimized model is constructed, to seek distributed generation resource
Self adaptive control rule；The time granularity of optimization is T4, i.e., every T4Time carries out an Optimized Operation, obtains the T4Time
Interior operational order, T4Value range is 30s~5min；
In active distribution network, the maximum active power output of distributed generation resource is uncertain variables, the following institute of section expression formula
Show:
Wherein, PmaxIndicate distributed generation resource maximum active power output,Indicate the lower limit of section power output,Indicate section
The upper limit of power output；
The affine expression formula of maximum active power output of distributed generation resource are as follows:
Wherein, pmaxFor distributed generation resource maximum active power output,For the predicted value of distributed generation resource maximum active power output,For maximum perturbation amount, ε is distributed generation resource power output Discontinuous Factors, and Ω is uncertain collection；
The auto-adaptive function of the optimal active power output of distributed generation resource, is shown below:
P=p0+p(ε) (11)
P (ε)=pα1ε+pα2εTε+… (12)
Wherein, p is the optimal active power output of distributed generation resource, p0For in the case of undisturbed distributed generation resource it is optimal it is active go out
Power, p (ε) are auto-adaptive function of the distributed generation resource active power output with shock wave, pα1It is the one of distributed generation resource active power output
Rank disturbance quantity, pα2For the second order disturbance quantity of distributed generation resource active power output, εTIt is the transposition of ε；Wherein formula (12) is multinomial
Function, the specific number of item number are related to the expression of Optimized model；
The auto-adaptive function of distributed generation resource OPTIMAL REACTIVE POWER power output, is shown below:
Q=q0+q(ε) (13)
Q (ε)=qα1ε+qα2εTε+… (14)
Wherein, Q is distributed generation resource OPTIMAL REACTIVE POWER power output, q0OPTIMAL REACTIVE POWER for distributed generation resource in the case of undisturbed goes out
Power, q (ε) are auto-adaptive function of the idle power output of distributed generation resource with shock wave, qα1It is the one of the idle power output of distributed generation resource
Rank disturbance quantity, qα2For the second order disturbance quantity of the idle power output of distributed generation resource；Wherein formula (14) is polynomial function, item number tool
Body number is related to the expression of Optimized model；
Active-idle Coordination and Optimization Model of active distribution network robust adaptive, as follows:
Wherein,For the objective function under undisturbed state,To there is state of disturbance
Under objective function, g (P, Q)=0 indicate equality constraint, h (P, Q)≤0 indicate inequality constraints.
The utility model has the advantages that compared with prior art, the invention has the following advantages that sufficiently having invoked the biography in active distribution network
System adjusting device and distributed generation resource, and double-deck stereo Scheduling Framework is devised according to its feature；In the Optimized Operation of upper layer, draw
Enter sequential optimum theory, it is ensured that under condition of uncertainty, the accuracy and validity of Optimal Decision-making, and compared to more times
Scale coordination optimization, reduces operation times, reduces costs；In lower layer's Optimized Operation, uses ADAPTIVE ROBUST and optimize
Method realizes Optimal Decision-making adaptive change with disturbance quantity variation, it is ensured that the economy and safety of power distribution network operation；
The present invention is suitable for the grid-connected active distribution network Optimized Operation of a large amount of distributed generation resources.
Detailed description of the invention
Fig. 1 is Scheduling Framework schematic diagram of the present invention.
Fig. 2 is dispatching method isoboles of the present invention.
Specific embodiment
Further detailed description is done to the present invention with reference to the accompanying drawing.
Regulating and controlling voltage resource is numerous in active distribution network, is typically divided into traditional voltage adjusting device and grid-connected renewable energy
Source.Traditional voltage adjusting device is mostly discrete device, and dispatching cycle is long, and response speed is slow, and often restricted to action frequency, with
Shunt capacitor group, load adjustable transformer are representative；Renewable energy is mostly continuous device, can quick response, to movement
There is no limit for number, but has the characteristics that randomness and uncertainty, and power supply is representative in a distributed manner.
Renewable energy it is a large amount of it is grid-connected bring a large amount of uncertainty, active distribution network optimization operation in fill
Divide and consider probabilistic influence, while renewable energy can provide active and reactive power support to power distribution network simultaneously, should sufficiently adjust
All maneuverable resources are moved, the effect of renewable energy is given full play to, realize active control and real-time optimization to power distribution network.
It the probabilistic influence of power output to eliminate renewable energy and gives full play to its supporting role to system, proposes sequential-Shandong
The adaptive dual-layer optimization adjustment and control system of stick.
Sequential optimum theory is the Study on Decision-making Method for Optimization for uncertain dynamic system, and task is a scheduling week
In phase, based on Optimized Operation plan and device action state before this, is constrained according to the regular hour, operation plan is continued to repair
Just, it is therefore an objective to realize the optimal control to uncertain system.
As shown in Figure 1, a kind of meter and probabilistic active distribution network it is sequential-robust Optimal Scheduling, including upper layer
Sequential optimization and lower layer robust adaptive optimization；Wherein, the sequential optimization in upper layer is mainly for the tradition in active distribution network
Voltage adjusting device, carries out hour grade Optimized Operation, and the optimization of lower layer's robust adaptive carries out in hour mainly for distributed generation resource
Real-time optimization；In addition, sequential optimize to lower layer's robust adaptive in upper layer optimizes the instruction of transmission upper layer, the optimization of lower layer's robust adaptive
Information of lower layer is fed back into the sequential optimization in upper layer；The sequential optimization in upper layer is divided into decision phase 1, decision phase 2 and decision phase 3,
Wherein, the time interval of decision phase 1 to decision phase 2 is greater than the time interval of decision phase 2 to decision phase 3；Lower layer Shandong
Stick adaptive optimization is divided into 3 periods, carries out a robust adaptive optimization every 5min.
A kind of dispatching method based on above-mentioned scheduling system, comprising the following specific steps
(1) for traditional voltage adjusting device in active distribution network, hour grade Optimized Operation is carried out, introduces sequential optimization reason
By carrying out sequential rolling optimization, construct upper layer Optimized model；
(2) it is based on upper layer dispatch command, carries out lower layer's optimization in hour, gives full play to the system support of distributed generation resource
Uncertain variables are processed into the form in section by ability, using robust adaptive optimization method, construct robust adaptive it is active-
Idle Coordination and Optimization Model.
When operation plan is formulated on upper layer, while considering traditional adjusting device and distributed generation resource, but main actions tradition from
Adjusting device is dissipated, the regulation and control instruction of distributed generation resource is mainly derived from lower layer's optimization.
As shown in Fig. 2, in step (1), comprehensively considering the low of scheduling decision for the dispatching method isoboles from 0 point to 4 point
Conservative degree reduces cost and reduces operation times, and upper layer optimization, for a dispatching cycle, was opened each dispatching cycle with 4 hours
Beginning optimizes decision, obtains operation plan；If current time is more than 1 hour apart from the conventional discrete device action time,
The rolling optimization of progress in 30 minutes updates operation plan；If current time is small less than 1 apart from the conventional discrete device action time
When, then the rolling optimization of progress in 15 minutes, updates operation plan；5 minutes before conventional discrete device action, then carry out primary
Optimization updates operation plan.
Upper layer optimization is with the minimum optimization aim of system losses, the node injecting power of the minimum as each node of system losses
Minimum, expression formula is as follows:
Wherein, i is node serial number, and N is node number, piFor the node active injection power of node i, PlossFor system network
Damage.
The trend Constraints of Equilibrium of system, is shown below:
S=diag [V] [Y]*·[V]* (17)
Wherein, S is node injecting power vector, and V is node voltage vector, and Y is node admittance matrix.
Voltage magnitude constraint, is shown below:
Wherein, ViFor arbitrary node i voltage magnitude,For the lower limit of arbitrary node i voltage magnitude,Arbitrarily to save
The upper limit of point i voltage magnitude.
Shunt capacitor group (CB) constraint is shown below:
Wherein, Qi,CBFor the idle power output of CB, Qi,CB,stepFor the reactive power that every group capacitor provides, Ni,CBFor CB's
It organizes number and it is nonnegative integer,For the upper limit of capacitor group number.
On-load regulator transformer (OLTC) constraint is as follows:
Wherein,For the gear of OLTC,For the upper limit of OLTC gear,For OLTC gear
Lower limit.
Distributed generation resource constraint is as follows:
Wherein,WithThe respectively active power output of distributed generation resource and idle power output；WithRespectively
For the lower and upper limit of distributed generation resource active power output；WithThe respectively lower limit of the idle power output of distributed generation resource
And the upper limit；For distributed generation resource capacity.
Upper layer optimized mathematical model reduced form is as follows:
Wherein, u is control variable, including load transformer (OLTC) gear, shunt capacitor (CB) gear and distribution
The active and reactive power output of formula power supply；X is state variable, including node voltage amplitude, capacity of trunk etc.；L (u, x)=0 be etc.
Formula constraint, G (u, x)≤0 are inequality constraints, PlossFor system losses.
In step (2), comprehensively considers the accuracy of prediction and reduce calculation times, lower layer's optimization is with 5 minutes for the time
Granularity, i.e., the Optimized Operation of progress in every 5 minutes, obtains the operational order in this 5 minutes.
Lower layer's optimization only regulates and controls the active power output and idle power output of distributed generation resource, carries out to distributed generation resource excellent in real time
Change.Dispatch command based on upper layer, lower layer's optimization uses active-idle coordination optimizing method of robust adaptive, by uncertain change
Amount processing be range format, construct auto-adaptive function, construct robust adaptive Optimized model, to seek distributed generation resource from
Suitable solution rule, the time granularity of optimization are 5 minutes.
In active distribution network, the maximum active power output of distributed generation resource is uncertain variables, the following institute of section expression formula
Show:
Wherein,The respectively lower and upper limit of section power output.
The range format is expressed as affine form, the i.e. affine expression formula of maximum active power output of distributed generation resource, it is as follows
It is shown:
Wherein, pmaxFor distributed generation resource maximum active power output,For the predicted value of distributed generation resource maximum active power output,For maximum perturbation amount, ε is distributed generation resource power output Discontinuous Factors, and Ω is uncertain collection.
In the case, simple linear adaption rule cannot be fitted the optimized operation point of active distribution network well,
More accurate auto-adaptive function need to be used, so that global optimum's operating point that optimization instruction is more nearly under every kind of possible operating condition.
The auto-adaptive function of the optimal active power output of distributed generation resource is a polynomial function, is expressed as follows shown in formula:
P=p0+p(ε) (25)
P (ε)=pα1ε+pα2εTε+… (26)
Wherein, p is the optimal active power output of distributed generation resource, p0For in the case of undisturbed distributed generation resource it is optimal it is active go out
Power, p (ε) are auto-adaptive function of the distributed generation resource active power output with shock wave, pα1It is the one of distributed generation resource active power output
Rank disturbance quantity, pα2For the second order disturbance quantity of distributed generation resource active power output, εTIt is the transposition of ε；
The auto-adaptive function of distributed generation resource OPTIMAL REACTIVE POWER power output is similarly a nonlinear polynomial function, expresses
Formula is as follows:
Q=q0+q(ε) (27)
Q (ε)=qα1ε+qα2εTε+… (28)
Wherein, Q is distributed generation resource OPTIMAL REACTIVE POWER power output, q0OPTIMAL REACTIVE POWER for distributed generation resource in the case of undisturbed goes out
Power, q (ε) are auto-adaptive function of the idle power output of distributed generation resource with shock wave, qα1It is the one of the idle power output of distributed generation resource
Rank disturbance quantity, qα2For the second order disturbance quantity of the idle power output of distributed generation resource；
The thought of ADAPTIVE ROBUST optimization is being sought changing and the decision of adaptive change with disturbance variable, i.e., it is solved
It is a decision rule, rather than a decision value.It is adaptive based on nonlinear distributed generation resource active power output and idle power output
Function is answered, active-idle Coordination and Optimization Model of active distribution network ADAPTIVE ROBUST is constructed.The following institute of mathematical model of lower layer's control
Show:
Wherein,For the objective function under undisturbed state,To there is state of disturbance
Under objective function, g (P, Q)=0 indicate equality constraint, h (P, Q)≤0 indicate inequality constraints.
Claims (2)
1. active distribution network is sequential-ADAPTIVE ROBUST Optimal Scheduling, which is characterized in that including upper layer Optimized model and lower layer
Optimized model；
Upper layer Optimized model optimizes traditional adjusting device, and lower layer's Optimized model carries out real-time optimization to distributed generation resource,
Lower layer's Optimized model receives the dispatch command of upper layer Optimized model, carries out lower layer's optimization in hour, and lower layer's Optimized model will be believed
Breath feeds back to upper layer Optimized model；
The upper layer Optimized model is sequential Optimized model, and lower layer's Optimized model is active-idle coordination of robust adaptive
Optimized model；
The sequential Optimized model are as follows:
Min f (u, x)=min Ploss
L (u, x)=0
G(u,x)≤0
Wherein, u indicates that control variable, x indicate state variable, and L (u, x)=0 indicates equality constraint, and the expression of G (u, x)≤0 differs
Formula constraint, PlossIndicate system losses；
Active-idle Coordination and Optimization Model of the robust adaptive are as follows:
G (P, Q)=0
h(P,Q)≤0
P=p0+ p (ε) p (ε)=pα1ε+pα2εTε+…
Q=q0+ q (ε) q (ε)=qα1ε+qα2εTε+…
ε∈Ω
Wherein,For the objective function under undisturbed state,To have under state of disturbance
Objective function, g (P, Q)=0 indicate equality constraint, and h (P, Q)≤0 indicates inequality constraints；
The affine expression formula of maximum active power output of distributed generation resource are as follows:
Wherein,p maxFor distributed generation resource maximum active power output,For the predicted value of distributed generation resource maximum active power output,
For maximum perturbation amount, ε is distributed generation resource power output Discontinuous Factors, and Ω is uncertain collection；
The auto-adaptive function of the optimal active power output of distributed generation resource, is shown below:
P=p0+p(ε)
P (ε)=pα1ε+pα2εTε+…
Wherein, p is the optimal active power output of distributed generation resource, p0For the optimal active power output of distributed generation resource in the case of undisturbed,
p(ε)It is distributed generation resource active power output with the auto-adaptive function of shock wave, pα1Single order for distributed generation resource active power output is disturbed
Momentum, pα2For the second order disturbance quantity of distributed generation resource active power output, εTIt is the transposition of ε；
The auto-adaptive function of distributed generation resource OPTIMAL REACTIVE POWER power output, is shown below:
Q=q0+q(ε)
Q (ε)=qα1ε+qα2εTε+…
Wherein, Q is distributed generation resource OPTIMAL REACTIVE POWER power output, q0For the OPTIMAL REACTIVE POWER power output of distributed generation resource in the case of undisturbed, q
(ε) is auto-adaptive function of the idle power output of distributed generation resource with shock wave, qα1Single order for the idle power output of distributed generation resource is disturbed
Momentum, qα2For the second order disturbance quantity of the idle power output of distributed generation resource.
2. the dispatching method based on scheduling system described in claim 1, which comprises the following steps:
(1) upper layer Optimized model is constructed
For traditional voltage adjusting device in active distribution network, hour grade Optimized Operation is carried out, sequential optimum theory is introduced, carries out sequence
Rolling optimization is passed through, upper layer Optimized model is constructed；
(2) lower layer's Optimized model is constructed
Based on upper layer dispatch command, lower layer's optimization in hour is carried out, the system enabling capabilities of distributed generation resource are given full play to, it will
Uncertain variables are processed into the form in section, using robust adaptive optimization method, construct active-idle coordination of robust adaptive
Optimized model, i.e. lower layer's Optimized model；
Step (1) specifically includes:
(11) operation plan is formulated on upper layer
When operation plan is formulated on upper layer, while considering traditional adjusting device and distributed generation resource, but main actions conventional discrete tune
Equipment is controlled, the regulation and control instruction of distributed generation resource is mainly derived from lower layer's optimization；
(12) operation plan is obtained
Upper layer optimization, for a dispatching cycle, is started to optimize decision, obtains operation plan each dispatching cycle with T；
(13) operation plan is updated
If current time is more than 1 hour apart from the conventional discrete device action time, every T1Time carries out a rolling optimization,
Update operation plan；
If current time apart from the conventional discrete device action time less than 1 hour, every T2Time carries out a rolling optimization,
Update operation plan；
T before conventional discrete device action3Time, then a suboptimization is carried out, update operation plan；
The mathematical model of upper layer optimization are as follows:
Min f (u, x)=min Ploss
L(u,X)=0
G(u,x)≤0
Wherein, u indicates that control variable, x indicate state variable, and L (u, x)=0 indicates equality constraint, and the expression of G (u, x)≤0 differs
Formula constraint, PlossIndicate system losses；
Upper layer optimization dispatching cycle T value range be 1h~for 24 hours, T1Value range be 20min~1h, T2Value model
It encloses for 10min~20min, T3Value range be 3min~10min；
Lower layer's optimization only regulates and controls the active power output and idle power output of distributed generation resource, carries out real-time optimization to distributed generation resource；Base
Dispatch command in upper layer, lower layer's optimization use active-idle coordination optimizing method of robust adaptive, uncertain variables are handled
For range format, auto-adaptive function is constructed, robust adaptive Optimized model is constructed, to seek the self-adaptive controlled of distributed generation resource
System rule；
The time granularity of optimization is T4, i.e., every T4Time carries out an Optimized Operation, obtains the T4Operational order in time, T4
Value range is 30s~5min；
In active distribution network, the maximum active power output of distributed generation resource is uncertain variables, and section expression formula is as follows:
Wherein, PmaxIndicate distributed generation resource maximum active power output,Pmax Indicate the lower limit of section power output,Indicate section power output
The upper limit；
The affine expression formula of maximum active power output of distributed generation resource are as follows:
Wherein, pmaxFor distributed generation resource maximum active power output,For the predicted value of distributed generation resource maximum active power output,
For maximum perturbation amount, ε is distributed generation resource power output Discontinuous Factors, and Ω is uncertain collection；
The auto-adaptive function of the optimal active power output of distributed generation resource, is shown below:
P=p0+p(ε)
P (ε)=pα1ε+pα2εTε+…
Wherein, p is the optimal active power output of distributed generation resource, p0For the optimal active power output of distributed generation resource in the case of undisturbed,
p(ε)It is distributed generation resource active power output with the auto-adaptive function of shock wave, pα1Single order for distributed generation resource active power output is disturbed
Momentum, pα2For the second order disturbance quantity of distributed generation resource active power output, εTIt is the transposition of ε；
The auto-adaptive function of distributed generation resource OPTIMAL REACTIVE POWER power output, is shown below:
Q=q0+q(ε)
Q (ε)=qα1ε+qα2εTε+…
Wherein, Q is distributed generation resource OPTIMAL REACTIVE POWER power output, q0For the OPTIMAL REACTIVE POWER power output of distributed generation resource in the case of undisturbed, q
(ε) is auto-adaptive function of the idle power output of distributed generation resource with shock wave, qα1Single order for the idle power output of distributed generation resource is disturbed
Momentum, qα2For the second order disturbance quantity of the idle power output of distributed generation resource；
Active-idle Coordination and Optimization Model of active distribution network robust adaptive, as follows:
G (P, Q)=0
h(P,Q)≤0
P=p0+ p (ε) p (ε)=pα1ε+pα2εTε+…
Q=q0+ q (ε) q (ε)=qα1ε+qα2εT ε+…
ε∈Ω
Wherein,For the objective function under undisturbed state,To have under state of disturbance
Objective function, g (P, Q)=0 indicate equality constraint, and h (P, Q)≤0 indicates inequality constraints.
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