CN108988393B - Micro-source time sequence optimization method for micro-grid black start - Google Patents

Micro-source time sequence optimization method for micro-grid black start Download PDF

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CN108988393B
CN108988393B CN201810954710.6A CN201810954710A CN108988393B CN 108988393 B CN108988393 B CN 108988393B CN 201810954710 A CN201810954710 A CN 201810954710A CN 108988393 B CN108988393 B CN 108988393B
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starting time
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阮志杰
方嵩
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Guangdong Power Grid Co Ltd
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
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Abstract

The invention relates to a micro-source time sequence optimization method for micro-grid black start. The method comprises the following steps: s1, establishing a DG timing sequence optimization model of the microgrid; s2, setting initial parameters of improved particle swarm optimizationCounting, including randomly generating E starting time sequence particles of DGs to be recovered, and taking the E starting time sequence particles as initial starting time sequence particle swarm; the parameter comprises a variation coefficient b1And b2(ii) a An improvement factor g; number of time series iterations Ec(ii) a S3, calling a DG time sequence optimization model aiming at each starting time sequence particle, solving the DG time sequence optimization model to obtain the starting time of the unit, calculating the generated energy of the system, defining the generated energy of the system as the fitness of the starting time sequence particles and evaluating the fitness; s4, updating the starting time sequence particles through an improved particle swarm algorithm; s5, repeating the steps S3 to S4 until the time sequence iteration number E of starting the time sequence particle swarm is reachedc(ii) a And S6, selecting the starting time sequence particles with the maximum fitness, namely the optimal DG starting time. The method effectively improves the precision of the result and provides an optimal starting time sequence for the black start of the microgrid.

Description

Micro-source time sequence optimization method for micro-grid black start
Technical Field
The invention relates to a microgrid black start method, in particular to a microgrid black start micro-source time sequence optimization method.
Background
Compared with the traditional large-scale unit, the Distributed Generation (DG) has the advantages of quick self-starting, quick response, flexible control mode and the like, and the microgrid consisting of a plurality of distributed generation, loads, energy storage and the like has better stability. At present, research and scheme formulation about black start are mainly aimed at a traditional large power grid, along with the fact that a large number of Distributed Generation (DG) access power distribution networks, particularly the application and development of a microgrid consisting of DG in the power distribution networks, attention is paid to black start research of DG auxiliary power grids, and selection of a micro-source time sequence in a DG auxiliary black start process is particularly important.
Disclosure of Invention
The invention provides a micro-source time sequence optimization method for micro-grid black start, which can quickly find out the optimal start time of the micro-grid black start, so as to overcome at least one defect in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a micro-grid black start micro-source time sequence optimization method comprises the following steps:
s1, establishing a DG timing sequence optimization model of the microgrid;
s2, setting initial parameters of an improved particle swarm algorithm, wherein the initial parameters comprise that E starting time sequence particles of DGs to be recovered are randomly generated and serve as initial starting time sequence particle swarm; the parameter comprises a variation coefficient b1And b2(ii) a An improvement factor g; number of time series iterations Ec；
S3, calling a DG time sequence optimization model aiming at each starting time sequence particle, solving the DG time sequence optimization model to obtain the starting time of the unit, calculating the generated energy of the system, defining the generated energy of the system as the fitness of the starting time sequence particles and evaluating the fitness;
s4, updating the starting time sequence particles through an improved particle swarm algorithm;
s5, repeating the steps S3 to S4 until the time sequence iteration number E of starting the time sequence particle swarm is reachedc；
And S6, selecting the starting time sequence particles with the maximum fitness, namely the optimal DG starting time.
Further, the DG timing optimization model in step S1 is:
Figure GDA0002454200760000021
in the formula, maxf1The method is an objective function 1 of a model and represents that the DG power generation amount is maximum in the black start stage of the microgrid; maxf2The objective function 2 of the model represents that the load importance degree near the DG is maximum; [0, T ]]Is the time interval studied; n is the number of DGs to be started; pi(t) is the active power output of the ith DG at the moment t; t is tnes,iThe moment when the starting power is obtained for the ith DG; t is tc,iTime required for the i-th DG to start up; pmax,iMaximum output power for ith DG; riThe climbing rate of the ith DG; pstart,iStarting power for the ith DG (black start DG this parameter is zero); a isiIs the starting state of the ith DG; ziThe load importance in the vicinity of the ith DG; omegalLoad weight for class i loads; zi.lIs the proportion of the class i load among all node loads connected to the ith DG.
Furthermore, in the DG timing sequence optimization process, the conditions of DG output constraint, node voltage constraint, line constraint and node power balance constraint should be met;
the DG output constraint is as follows:
Figure GDA0002454200760000022
Figure GDA0002454200760000023
in the formula, ΨGIs the set of DGs in the microgrid; piAnd QiRespectively the active power and the reactive power of the ith DG;
Figure GDA0002454200760000031
respectively setting the lower limit and the upper limit of the active output of the ith DG;
Figure GDA0002454200760000032
respectively setting the lower limit and the upper limit of the reactive power output of the ith DG;
the node voltage constraint is as follows:
Figure GDA0002454200760000033
in the formula, ΨNThe method comprises the steps of (1) collecting all nodes in a micro-grid; u shapeiThe voltage amplitude of the ith node;
Figure GDA0002454200760000034
Figure GDA0002454200760000035
the lower limit and the upper limit of the voltage amplitude of the ith node are respectively;
the line constraints are:
Figure GDA0002454200760000036
in the formula, ΨLThe method comprises the steps of (1) collecting all lines in a micro-grid; pLiIs the active power flowing through line i;
Figure GDA0002454200760000037
maximum active power allowed to flow for line i;
the node power balance constraint is as follows:
Figure GDA0002454200760000038
Figure GDA0002454200760000039
in the formula, Pinj,iAnd Qinj,iRespectively injecting active power and reactive power into the node i; u shapeiAnd UjThe voltages at node i and node j, respectively; gijAnd BijConductance and susceptance between node i and node j, respectively;ijis the voltage phase difference between node i and node j.
Further, the step of S3 includes comparing target for all i ∈ {1, 2.... multidot.E }, respectivelyiAnd PmaxiIf target is reachediGreater than PmaxiIf so, let Pmaxi＝targeti，
Figure GDA00024542007600000310
Then compares targetiAnd QmaxiIf target is reachediGreater than QmaxiThen Qmax needs to be updatediAnd it
Figure GDA00024542007600000311
Wherein targetiTo start the fitness of time series particle i, PmaxiFor the maximum power generation, Qmax, reached by each start sequence particle itselfiThe maximum amount of power generation experienced in the solution space for all startup time series particles in the startup time series particle swarm.
Further, the update formula of the start timing particle in step S4 is:
Figure GDA0002454200760000041
Figure GDA0002454200760000042
ti＝ti+i
in the formula (I), the compound is shown in the specification,ito start the timing particle tiA change factor; t is tiTo start the timing particle i position;
Figure GDA0002454200760000043
best position experienced for starting time sequence particle i position;
Figure GDA0002454200760000044
the best position of the current starting time sequence particle swarm in the solution space is obtained; s1、s2Is between [0,1]A random number in between; b1、b2Is a coefficient of variation; g is an improvement factor, and the convergence rate of the particle swarm algorithm is higher by searching different ranges.
Compared with the prior art, the beneficial effects are: the micro-source time sequence optimization method for the micro-grid black start has strong global detection and local search capabilities, effectively improves the accuracy of results, provides an optimal start time sequence for the micro-grid black start, and enables the micro-grid black start to be more stable and rapid.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
As shown in fig. 1, a method for optimizing a micro-source timing sequence of micro-grid black start includes the following steps:
step 1, establishing a DG timing optimization model of the microgrid:
Figure GDA0002454200760000051
in the formula, maxf1The method is an objective function 1 of a model and represents that the DG power generation amount is maximum in the black start stage of the microgrid; maxf2The objective function 2 of the model represents that the load importance degree near the DG is maximum; [0, T ]]Is the time interval studied; n is the number of DGs to be started; pi(t) is the active power output of the ith DG at the moment t; t is tnes,iThe moment when the starting power is obtained for the ith DG; t is tc,iTime required for the i-th DG to start up; pmax,iMaximum output power for ith DG; riThe climbing rate of the ith DG; pstart,iStarting power for the ith DG (black start DG this parameter is zero); a isiIs the starting state of the ith DG; ziThe load importance in the vicinity of the ith DG; omegalLoad weight for class i loads; zi.lIs the proportion of the class i load among all node loads connected to the ith DG.
In the DG time sequence optimization process, the conditions of DG output constraint, node voltage constraint, line constraint and node power balance constraint should be met;
in the DG recovery phase, the constraints to be considered are as follows:
DG output constraint:
Figure GDA0002454200760000052
Figure GDA0002454200760000053
in the formula, ΨGIs the set of DGs in the microgrid; piAnd QiAre respectively asActive and reactive power of the ith DG;
Figure GDA0002454200760000054
respectively setting the lower limit and the upper limit of the active output of the ith DG;
Figure GDA0002454200760000055
respectively setting the lower limit and the upper limit of the reactive power output of the ith DG;
node voltage constraint:
Figure GDA0002454200760000061
in the formula, ΨNThe method comprises the steps of (1) collecting all nodes in a micro-grid; u shapeiThe voltage amplitude of the ith node;
Figure GDA0002454200760000062
Figure GDA0002454200760000063
the lower limit and the upper limit of the voltage amplitude of the ith node are respectively;
line constraint:
Figure GDA0002454200760000064
in the formula, ΨLThe method comprises the steps of (1) collecting all lines in a micro-grid; pLiIs the active power flowing through line i;
Figure GDA0002454200760000065
maximum active power allowed to flow for line i;
node power balance constraint:
Figure GDA0002454200760000066
Figure GDA0002454200760000067
in the formula, Pinj,iAnd Qinj,iRespectively injecting active power and reactive power into the node i; u shapeiAnd UjThe voltages at node i and node j, respectively; gijAnd BijConductance and susceptance between node i and node j, respectively;ijis the voltage phase difference between node i and node j.
Step 2, setting initial parameters of the improved particle swarm algorithm, wherein the initial parameters comprise E starting time sequence particles of DGs to be recovered, and the E starting time sequence particles serve as initial starting time sequence particle swarm; the parameter comprises a variation coefficient b1And b2(ii) a An improvement factor g; number of time series iterations Ec；
Step 3, calling a DG time sequence optimization model for each starting time sequence particle, solving the DG time sequence optimization model to obtain the starting time of the unit, calculating the generated energy of the system, defining the generated energy of the system as the fitness of the starting time sequence particle, and evaluating;
specifically, target is compared for all i ∈ {1,2iAnd PmaxiIf target is reachediGreater than PmaxiIf so, let Pmaxi＝targeti，
Figure GDA0002454200760000068
Then compares targetiAnd QmaxiIf target is reachediGreater than QmaxiThen Qmax needs to be updatediAnd it
Figure GDA0002454200760000069
Wherein targetiTo start the fitness of time series particle i, PmaxiFor the maximum power generation, Qmax, reached by each start sequence particle itselfiThe maximum amount of power generation experienced in the solution space for all startup time series particles in the startup time series particle swarm.
Step 4, updating the starting time sequence particles through an improved particle swarm algorithm; the update formula of the start timing particle is as follows:
Figure GDA0002454200760000071
Figure GDA0002454200760000072
ti＝ti+i(3)
in the formula (I), the compound is shown in the specification,ito start the timing particle tiA change factor; t is tiTo start the timing particle i position;
Figure GDA0002454200760000073
best position experienced for starting time sequence particle i position;
Figure GDA0002454200760000074
the best position of the current starting time sequence particle swarm in the solution space is obtained; s1、s2Is between [0,1]A random number in between; b1、b2Is a coefficient of variation; g is an improvement factor, the convergence rate of the particle swarm algorithm is faster by searching different ranges, and the formula (3) represents the starting time sequence particle tiThe new coordinate position of (2). (2) The formula (3) determines the starting time sequence particle tiThe next step of movement position. FIG. 1 illustrates that the start-up sequence particle follows the equation (2), (3) from position tiTo tk+1The principle of movement.
Step 5, repeating the steps S3 to S4 until reaching the time sequence iteration number E of starting the time sequence particle swarmc(ii) a Wherein the time sequence iteration times E of starting the time sequence particle swarmcThe method is the termination condition of the optimization method of the micro-source time sequence of the micro-grid black start, and if the time sequence iteration times E of the starting time sequence particle swarm are reachedc"terminate the return; otherwise, the procedure returns to step 3.
And 6, selecting the starting time sequence particles with the maximum fitness (the generated energy of the system), namely the optimal DG starting time.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (4)

1. A micro-grid black start micro-source time sequence optimization method is characterized by comprising the following steps:
s1, establishing a DG timing sequence optimization model of the microgrid, wherein the DG timing sequence optimization model is as follows:
Figure FDA0002454200750000011
in the formula, maxf1The method is an objective function 1 of a model and represents that the DG power generation amount is maximum in the black start stage of the microgrid; maxf2The objective function 2 of the model represents that the load importance degree near the DG is maximum; [0, T ]]Is the time interval studied; n is the number of DGs to be started; pi(t) is the active power output of the ith DG at the moment t; t is tnes,iThe moment when the starting power is obtained for the ith DG; t is tc,iTime required for the i-th DG to start up; pmax,iMaximum output power for ith DG; riThe climbing rate of the ith DG; pstart,iStarting power of ith DG; a isiIs the starting state of the ith DG; ziThe load importance in the vicinity of the ith DG; omegalLoad weight for class i loads; zi.lThe specific weight of the load of the level I in all node loads connected with the ith DG;
s2, setting initial parameters of an improved particle swarm algorithm, wherein the initial parameters comprise that E starting time sequence particles of DGs to be recovered are randomly generated and serve as initial starting time sequence particle swarm; the parameter comprises a variation coefficient b1And b2(ii) a An improvement factor g; number of time series iterations Ec；
S3, calling a DG time sequence optimization model aiming at each starting time sequence particle, solving the DG time sequence optimization model to obtain the starting time of the unit, calculating the generated energy of the system, defining the generated energy of the system as the fitness of the starting time sequence particles and evaluating the fitness;
s4, updating the starting time sequence particles through an improved particle swarm algorithm;
s5, repeating the steps S3 to S4 until the time sequence iteration number E of starting the time sequence particle swarm is reachedc；
And S6, selecting the starting time sequence particles with the maximum fitness, namely the optimal DG starting time.
2. The method for optimizing the micro-source time sequence of the micro-grid black start according to claim 1, wherein in the DG time sequence optimization process, the conditions of DG output constraint, node voltage constraint, line constraint and node power balance constraint should be satisfied;
the DG output constraint is as follows:
Pi min≤Pi≤Pi max，i∈ΨG
Figure FDA0002454200750000021
in the formula, ΨGIs the set of DGs in the microgrid; piAnd QiRespectively the active power and the reactive power of the ith DG; pi min、Pi maxRespectively setting the lower limit and the upper limit of the active output of the ith DG;
Figure FDA0002454200750000022
respectively setting the lower limit and the upper limit of the reactive power output of the ith DG;
the node voltage constraint is as follows:
Figure FDA0002454200750000023
in the formula, ΨNThe method comprises the steps of (1) collecting all nodes in a micro-grid; u shapeiThe voltage amplitude of the ith node;
Figure FDA0002454200750000024
Figure FDA0002454200750000025
the lower limit and the upper limit of the voltage amplitude of the ith node are respectively;
the line constraints are:
Figure FDA0002454200750000026
in the formula, ΨLThe method comprises the steps of (1) collecting all lines in a micro-grid; pLiIs the active power flowing through line i;
Figure FDA0002454200750000027
maximum active power allowed to flow for line i;
the node power balance constraint is as follows:
Figure FDA0002454200750000028
Figure FDA0002454200750000029
in the formula, Pinj,iAnd Qinj,iRespectively injecting active power and reactive power into the node i; u shapeiAnd UjThe voltages at node i and node j, respectively; gijAnd BijConductance and susceptance between node i and node j, respectively;ijis the voltage phase difference between node i and node j.
3. The method for optimizing the micro-source timing of micro-grid black start according to claim 1, wherein the step S3 comprises comparing target for all i ∈ {1,2iAnd PmaxiIf target is reachediGreater than PmaxiIf so, let P maxi＝targeti，
Figure FDA00024542007500000210
Then compares targetiAnd Q maxiIf target is reachediGreater than Q maxiThen Q max needs to be updatediAnd it
Figure FDA00024542007500000211
Wherein targetiTo initiate fitness of time-series particle i, PmaxiFor the maximum power generation, Qmax, reached by each start sequence particle itselfiThe maximum amount of power generation experienced in the solution space for all startup time series particles in the startup time series particle swarm.
4. The method as claimed in claim 1, wherein the updating formula of the start timing particles in step S4 is as follows:
Figure FDA0002454200750000031
Figure FDA0002454200750000032
ti＝ti+i
in the formula (I), the compound is shown in the specification,ito start the timing particle tiA change factor; t is tiTo start the timing particle i position;
Figure FDA0002454200750000033
best position experienced for starting time sequence particle i position;
Figure FDA0002454200750000034
the best position of the current starting time sequence particle swarm in the solution space is obtained; s1、s2Is between [0,1]A random number in between; b1、b2Is a coefficient of variation; g is an improvement factor bySearching different ranges enables the particle swarm algorithm to converge faster.
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