CN109347096B - Optimization management method for power quality of active power distribution network - Google Patents

Optimization management method for power quality of active power distribution network Download PDF

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CN109347096B
CN109347096B CN201811269080.5A CN201811269080A CN109347096B CN 109347096 B CN109347096 B CN 109347096B CN 201811269080 A CN201811269080 A CN 201811269080A CN 109347096 B CN109347096 B CN 109347096B
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value
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CN109347096A (en
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张爱祥
宋士瞻
王传勇
周荣奎
张健
康文文
王坤
李森
杨凤文
代二刚
周晓倩
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Shanghai Jiaotong University
Zaozhuang Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Zaozhuang Power Supply Co of State Grid Shandong Electric Power 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
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses an optimal management method for power quality of a dynamic power distribution network, which relates to the field of optimal management of power grids, and comprises the following steps: step 1, establishing an optimization management model of the electric energy quality of an active power distribution network in a prediction time domain, wherein the optimization management model takes minimization of network power loss in the prediction time domain as an optimization target and takes conditions related to the electric energy quality and safe operation of a system as constraint conditions; step 2, solving the optimized management model by adopting a cuckoo algorithm to obtain an optimal management model result; and 3, on the basis of the optimal management model result, reducing the influence caused by the prediction uncertainty problem by adopting model prediction control with a feedback characteristic in a matching way. The optimization management method for the power quality of the active power distribution network can effectively reduce power loss caused by harmonic waves, the obtained scheduling plan also considers the power quality factor, and the influence of uncertain factors is reduced by adopting model prediction control.

Description

Optimization management method for electric energy quality of active power distribution network
Technical Field
The invention relates to the field of power grid optimization management, in particular to an optimization management method for the power quality of an active power distribution network.
Background
Distributed Generation (DG) and Energy Storage (ES) are gaining increasing attention in many countries as important components of smart grids. The navigat research institute predicts that DG installed capacity in 2023 will exceed 165GW compared to DG installed capacity in 87.3GW in 2014. Furthermore, by 2025, the ES market in china will reach $ 87 billion and 31GWh, which is three times that of the ES market in 2015. DG and ES may provide benefits to the power system, for example, DG may reduce power losses and thereby reduce the corresponding equipment investment. The ES may integrate a renewable type DG such as a wind turbine into the power system. However, since DG and ES are connected to the distribution network through inverters, they will also have some negative effects including harmonic pollution.
Harmonic regulation has a significant impact on the operation and planning of power distribution networks for the following reasons: 1) in general, harmonic injection is positively correlated with the fundamental power of the harmonic resource. A large fundamental output implies a large harmonic injection of harmonic resources. Thus, when the harmonic constraints are close to their limits, the output of both the inverter-based DG and ES with poor harmonic spectra will be limited; 2) if the capacitor is not configured correctly, the small harmonic injection will be amplified due to the resonance between the capacitor and the inductive elements in the power distribution network. This means that even small harmonic injection of ES and DG can cause severe Power Quality (PQ) problems. Therefore, it is necessary to consider the harmonic pollution problem caused by DG in the operation and planning of the distribution network.
Voltage imbalance level is another important PQ indicator for power distribution networks. Voltage imbalances may cause negative effects, such as more power loss. Therefore, in order to provide a good power supply to the end user, it is important to ensure that the voltage levels are within a reasonable range, while limiting the voltage imbalance levels of the distribution network.
Currently, the existing research mainly focuses on optimal addressing capacity of the DG considering harmonic constraints, optimal operation of the distribution network considering voltage imbalance levels, determination of maximum permeability level of the DG based on an inverter in the distribution network, estimation of DG configuration capacity in the distribution network, and reactive power control of the distribution network considering wind energy uncertainty. The existing literature relates to an optimized operation method of a power distribution network considering voltage unbalance level and harmonic pollution, and related literature also considers less uncertain problems of processing renewable energy sources.
In view of the importance of the power quality problem, it is necessary to research an active power distribution network optimization management method considering the power quality problem.
Disclosure of Invention
In view of the above drawbacks of the prior art, the technical problem to be solved by the present invention is how to perform optimal management on the power quality of an active power distribution network when considering both the harmonic pollution and the voltage imbalance level in the active power distribution network and the uncertainty problem caused by the renewable energy being connected to the active power distribution network.
In order to achieve the above object, the present invention provides an optimized management method for power quality of an active power distribution network, including the following steps:
step 1, establishing an optimization management model of the electric energy quality of an active power distribution network in a prediction time domain, wherein the optimization management model takes minimization of network power loss in the prediction time domain as an optimization target, takes conditions related to the electric energy quality and safe system operation as constraint conditions, and the constraint conditions comprise electric energy quality constraint and system safety constraint, wherein the electric energy quality constraint comprises integral voltage harmonic distortion constraint, single harmonic distortion constraint and voltage unbalance factor constraint; the system safety constraint comprises a voltage effective value constraint and a current effective value constraint;
step 2, solving the optimized management model by adopting a cuckoo algorithm to obtain an optimal management model result;
and 3, on the basis of the optimal management model result, reducing the influence caused by the prediction uncertainty problem by adopting model prediction control with a feedback characteristic in a matching way, wherein the prediction uncertainty problem is the uncertainty caused when renewable energy is accessed into the active power distribution network.
Further, the objective function of the optimization management model is as follows:
Figure BDA0001845578930000021
wherein the content of the first and second substances,
Figure BDA0001845578930000022
is the net active loss associated with the fundamental frequency at time t,
Figure BDA0001845578930000023
is the active power loss associated with the harmonic frequency h at time t; Ω is the set of relevant harmonic frequencies; t is a prediction time domain related to model prediction control, where the scheduling interval may be 1h or 0.5h, that is, the optimal management model may execute once with the minimum active power loss in the prediction time domain T as an objective function of 1h or 0.5h, but only issue a scheduling plan at the next time, and repeat the process until a boundary point of the next time comes.
Further, the overall voltage harmonic distortion constraint can be used to reflect the overall harmonic level of node i, as follows:
Figure BDA0001845578930000024
wherein THD is an abbreviation for Total voltage harmonic distortion, referring to the overall voltage harmonic distortion constraint; vi p,t,
Figure BDA0001845578930000025
Fundamental frequency and harmonic frequency h voltage value, THD of p phase at time t node ii,maxIs the upper limit of THD;
the individual harmonic distortion constraint is an index that evaluates the harmonic level of each relevant frequency, and the individual harmonic distortion constraint for each node i satisfies the following constraint:
Figure BDA0001845578930000031
wherein IHD is an abbreviation of Indvidual Harmonic Distortion Constraints and refers to Individual Harmonic Distortion Constraints;
Figure BDA0001845578930000032
is the upper limit of the individual voltage harmonic distortion of the harmonic frequency h;
the voltage imbalance factor constraint may be used to reflect the voltage imbalance level, and to ensure a relatively good voltage imbalance level, the following constraints should be satisfied:
Figure BDA0001845578930000033
wherein VUF is an abbreviation of Voltage Unbalance Factor Constraints and refers to Voltage imbalance Factor Constraints; vi 1,t,Vi 2,tPositive sequence voltage values and negative sequence voltage values of a node i at the moment t respectively; UM% is the upper limit in the form of percentage VUF; since the definition of the voltage imbalance factor is for a sine wave, only the voltage at the fundamental frequency is used to calculate VUF.
Further, the voltage effective value constraint is:
Figure BDA0001845578930000034
wherein the content of the first and second substances,
Figure BDA0001845578930000035
is the effective value of the voltage of the p phase of the node i at the moment t;
Figure BDA0001845578930000036
the upper limit and the lower limit of the voltage effective value are respectively;
the current effective value constraint is as follows:
Figure BDA0001845578930000037
wherein the content of the first and second substances,
Figure BDA0001845578930000038
the effective value of the current of the p-th phase of the ij branch at the moment t is shown;
Figure BDA0001845578930000039
the upper limit and the lower limit of the current effective value are respectively;
Figure BDA00018455789300000310
is the current value of the p-th phase fundamental frequency and harmonic frequency h of the branch ij at the time t.
Further, the constraints of the optimization management model also include battery constraints, which generally include energy value constraints and power value constraints;
the energy value constraint is:
Figure BDA00018455789300000311
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00018455789300000312
is the energy stored in the battery at the end of time t; ei,max,Ei,minAre respectively
Figure BDA00018455789300000313
Upper and lower limits of (P)i ES,tIs the active power of the accumulator at time t, epsilonin,εou tThe charging and discharging efficiency of the storage battery respectively; equation (7) describes the relationship between two continuous-time energy values and the limit that the energy value of the storage battery satisfies the upper and lower limits, respectively;
in order to ensure that the active power, reactive power and apparent power of the battery are within limits, the power value constraints should satisfy the following constraints:
Figure BDA0001845578930000041
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0001845578930000042
is the rated active power of the storage battery,
Figure BDA0001845578930000043
is the battery reactive power at time t,
Figure BDA0001845578930000044
are respectively
Figure BDA0001845578930000045
The upper and lower limit values of (a),
Figure BDA0001845578930000046
is the limit of the apparent power of the battery.
Further, the constraints of the optimization management model further include controllable distributed power supply constraints:
Figure BDA0001845578930000047
wherein, Pi DDG,tIs the active power of the controllable distributed power supply at time t,
Figure BDA0001845578930000048
are respectively Pi DDG,tThe upper and lower limit values of (c),
Figure BDA0001845578930000049
the upper and lower limit values of the gradient rate of the distributed power supply, wherein the controllable distributed power supply can be a micro gas turbine or a fuel cell;
Figure BDA00018455789300000410
is the power factor angle of the distributed power supply at time t,
Figure BDA00018455789300000411
is that
Figure BDA00018455789300000412
Upper and lower limit values of (d);
the constraints of the optimal management model further include capacitor constraints:
Figure BDA00018455789300000413
wherein the content of the first and second substances,
Figure BDA00018455789300000414
is the capacitor capacity available at node i at time t,
Figure BDA00018455789300000415
respectively, the limit of the capacity; for operation of the distribution network, since the number of available capacitors is discrete, the distribution network is provided with a plurality of capacitors
Figure BDA00018455789300000416
Is a discrete decision variable;
Figure BDA00018455789300000417
is the number of times the capacitor state is changed,
Figure BDA00018455789300000418
is the maximum number of transitions, the purpose of which is to ensure that the capacitor has a good life cycle.
Further, the constraints of the optimization management model also include balance constraints; the balance constraint is a power flow balance formula of the fundamental frequency and the harmonic frequency at each moment;
for power flow at the fundamental frequency, the node current injection equation can be written as follows:
Figure BDA00018455789300000419
Figure BDA00018455789300000420
V1 abc,t＝[Vi a,t Vi b,t Vi c,t]T (12)
wherein the content of the first and second substances,
Figure BDA00018455789300000421
is a sub-matrix of the fundamental frequency node admittance matrix at time t,
Figure BDA00018455789300000422
Vi abc,ta node injection current vector and a voltage vector of the fundamental frequency are respectively at a time t node i; the injected node current typically comes from loads, renewable power sources (wind turbines or photovoltaics), controllable distributed power sources (gas turbines or fuel cells), and batteries;
Figure BDA0001845578930000051
node injection current values V of A, B and C phases of the fundamental frequency at the time t node ii a,t,Vi b,t,Vi c ,tRespectively at time tnode iVoltage values of fundamental wave frequencies A, B and C phases;
similarly, the power flow equation for the harmonic frequency h can be expressed as:
Figure BDA0001845578930000052
Figure BDA0001845578930000053
Figure BDA0001845578930000054
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0001845578930000055
the node injection current values of A, B and C phases of the harmonic frequency h of the node i at the time t are respectively,
Figure BDA0001845578930000056
voltage values of A, B and C phases of i harmonic frequency h of a node t at the moment are respectively;
Figure BDA0001845578930000057
is the node admittance matrix of the harmonic frequency h at time t; harmonic components of distributed power sources and batteries are modeled as current sources with one or more branch circuits.
Further, the cuckoo algorithm is an algorithm obtained by improving a standard cuckoo algorithm;
the standard cuckoo algorithm continuously searches the optimal nest process by simulating cuckoos, and performs an idealized assumption:
firstly, only one egg can be produced by one cuckoo at a time, and only one egg can be produced in one bird nest;
secondly, the best nest in each generation is reserved to the next generation;
③ host birds with a certain probability PaFinding an intruder, the host bird will discard the entire birdBird nest and immediately searching a new nest;
the new nest is generated by means of Levy flight:
Figure BDA0001845578930000058
in the formula:
Figure BDA0001845578930000059
the position of the ith bird nest in the t iteration is represented, a step scale factor alpha is larger than 0 and is directly related to the severity of the problem, wherein alpha is generally selected to be L/100, and L is a characteristic range of the severity of the problem;
Figure BDA00018455789300000510
characterizing point-to-point multiplication, Levy (λ) is a value generated by a Levy random process, obeying a Levy distribution:
L(λ):u＝t-λ,(1＜λ＜3) (16)
the Levy flight consists of high-frequency short-distance flight and low-frequency long-distance flight;
when the step size scale factor alpha takes a larger value, the global search capability of the standard cuckoo algorithm can be enhanced, and a global optimal solution area can be quickly determined; when the step size scale factor alpha is a small value, the local searching capability of the standard cuckoo algorithm can be enhanced, and the convergence speed of the algorithm is improved;
the cuckoo algorithm sets the step size scale factor alpha as a variable, takes a larger value at the initial stage of iteration, accelerates the positioning of a globally optimal region, takes a smaller value at the later stage of iteration, and accelerates the convergence of the algorithm;
the step size scaling factor α is set to:
Figure BDA00018455789300000511
wherein alpha ismaxAnd alphaminIs the maximum and minimum of the step-size scale factor, NmaxThe number of the maximum iterations of the cuckoo algorithm is N, and the number of the current iterations of the cuckoo algorithm is N.
Further, the step 2 further comprises the following steps:
step 2.1, reading in system data, and deciding variables and upper and lower limits; collecting an energy value of a storage battery at the last optimization moment and an actual output value of a distributed power supply, and forming closed-loop feedback of model predictive control by using the energy value and the actual output value as known variables during optimization at the current moment; initializing parameters of the cuckoo algorithm;
step 2.2, initializing the iteration number k to be 0, randomly generating n nests in the feasible region, and setting the position as x (x)1,x2,...,xn)TEach bird nest contains m state variables;
2.3, calculating the fitness of each nest based on the power flow analysis of the fundamental frequency and the harmonic frequency; penalizing a fitness function if the power flow is not aggregated;
step 2.4, sorting the individual advantages and disadvantages, selecting the optimal nest position, and reserving the nest position to the next generation;
step 2.5, updating other nest positions by utilizing the Levy flight, combining with a parent generation, searching for the best and reserving to the next generation;
step 2.6, judging whether the bird is found by the host bird, if so, selecting a abandoned bird nest, and updating the position by utilizing the Levy flight;
step 2.7, selecting the current global optimal bird nest and judging whether the algorithm ending condition is met; if not, the step 2.3 is returned to.
Further, in the step 3, when each optimization time t comes, the last optimization time t-1, that is, the energy value of the storage battery and the actual output value of the distributed power source at the beginning of the optimization time t are collected, and the collected values are substituted into relevant constraints as known variables during optimization at the current optimization time t; the optimization program takes the minimum network loss in the predicted time domain T as an objective function, adopts the cuckoo algorithm for optimization, only issues a scheduling plan of the optimization time T, and temporarily repeats the optimization process when the next optimization time T +1 comes; the real storage battery energy value of the system and the actual output value of the distributed power supply are acquired during each optimization, so that effective closed-loop feedback is formed, and the uncertainty influence brought by the renewable energy is reduced.
The optimization management method for the power quality of the active power distribution network can effectively reduce power loss caused by harmonic waves, the obtained scheduling plan also considers the power quality factor, and the influence of uncertain factors is reduced by adopting model prediction control.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a schematic illustration of a Levy flight trajectory;
FIG. 2 is a schematic diagram of a calculation flow based on the improved cuckoo algorithm;
fig. 3 is a time domain diagram of model predictive control.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In order to ensure the power quality level of the power distribution network, the scheme establishes an optimal power distribution network operation model, takes the network power loss in a minimized prediction time domain as an optimization target, and takes conditions related to the power quality and the safe operation of a system as constraints, and the method is concretely as follows.
An optimization management method for the power quality of an active power distribution network comprises the following steps:
step 1, establishing an optimized management model of the electric energy quality of an active power distribution network in a prediction time domain, wherein the optimized management model takes the minimized network power loss in the prediction time domain as an optimized target, takes conditions related to the electric energy quality and the safe operation of a system as constraint conditions, and the constraint conditions are divided into electric energy quality constraint and system safety constraint, wherein the electric energy quality constraint comprises integral Voltage Harmonic Distortion constraint (THD), independent Harmonic Distortion constraint (IHD), and Voltage Unbalance Factor constraint (VUF); the system safety constraint comprises a voltage effective value constraint and a current effective value constraint;
step 2, solving the optimized management model by adopting a cuckoo algorithm to obtain an optimal management model result;
and 3, on the basis of the optimal management model result, reducing the influence caused by the prediction uncertainty problem by adopting model prediction control with a feedback characteristic in a matching way, wherein the prediction uncertainty problem is the uncertainty caused when the renewable energy is accessed into the active power distribution network.
The objective function of the optimization management model is as follows:
Figure BDA0001845578930000071
wherein the content of the first and second substances,
Figure BDA0001845578930000072
is the net active loss associated with the fundamental frequency at time t,
Figure BDA0001845578930000073
is the active power loss associated with the harmonic frequency h at time t; Ω is the set of relevant harmonic frequencies; t is a prediction time domain related to model prediction control, where the scheduling interval may be 1h or 0.5h, that is, the optimal management model may execute once with the minimum active power loss in the prediction time domain T as an objective function of 1h or 0.5h, but only issue a scheduling plan at the next time, and repeat the process until a boundary point of the next time comes.
The overall voltage harmonic distortion constraint can be used to reflect the overall harmonic level of node i, as follows:
Figure BDA0001845578930000074
wherein, THD is an abbreviation of Total voltage harmonic distortion, referring to the integral voltage harmonic distortion constraint; vi p,t,
Figure BDA0001845578930000075
Fundamental frequency and harmonic frequency h voltage value, THD of p phase at time t node ii,maxIs the upper limit of THD;
the individual harmonic distortion constraint is an index that evaluates the harmonic level of each relevant frequency, and the individual harmonic distortion constraint for each node i satisfies the following constraint:
Figure BDA0001845578930000081
wherein IHD is an abbreviation of Indvidual Harmonic Distortion Constraints and refers to Individual Harmonic Distortion Constraints;
Figure BDA0001845578930000082
is the upper limit of the individual voltage harmonic distortion of the harmonic frequency h;
the voltage imbalance factor constraint may be used to reflect the voltage imbalance level, and to ensure a relatively good voltage imbalance level, the following constraints should be satisfied:
Figure BDA0001845578930000083
wherein VUF is an abbreviation of Voltage Unbalance Factor Constraints and refers to Voltage imbalance Factor Constraints; vi 1,t,Vi 2,tPositive sequence voltage values and negative sequence voltage values of a node i at the moment t respectively; UM% is the upper limit in the form of percentage VUF; since the definition of the voltage imbalance factor is for a sine wave, only the voltage at the fundamental frequency is used to calculate VUF.
The voltage effective value constraint is as follows:
Figure BDA0001845578930000084
wherein the content of the first and second substances,
Figure BDA0001845578930000085
is the effective value of the voltage of the p phase of the i node at the moment t;
Figure BDA0001845578930000086
respectively are the upper limit and the lower limit of the effective value of the voltage;
the current effective value is constrained as:
Figure BDA0001845578930000087
wherein the content of the first and second substances,
Figure BDA0001845578930000088
the effective value of the current of the p-th phase of the ij branch at the moment t is shown;
Figure BDA0001845578930000089
the upper limit and the lower limit of the current effective value are respectively;
Figure BDA00018455789300000810
is the current value of the p-th phase fundamental frequency and harmonic frequency h of the branch ij at the time t.
The constraint conditions of the optimization management model also comprise storage battery constraints, and the storage battery constraints generally comprise energy value constraints and power value constraints;
the energy value constraint is:
Figure BDA00018455789300000811
wherein the content of the first and second substances,
Figure BDA00018455789300000812
is the energy stored in the battery at the end of time t; ei,max,Ei,minAre respectively
Figure BDA00018455789300000813
Upper and lower limits of (P)i ES,tIs the active power of the accumulator at time t, epsilonin,εout is the charge-discharge efficiency of the storage battery respectively; equation (7) describes the relationship between two continuous-time energy values and the limit that the energy value of the storage battery satisfies the upper and lower limits, respectively;
to ensure that the active power, reactive power and apparent power of the battery are within limits, the power value constraints should satisfy the following constraints:
Figure BDA0001845578930000091
wherein the content of the first and second substances,
Figure BDA0001845578930000092
is the rated active power of the storage battery,
Figure BDA0001845578930000093
is the battery reactive power at time t,
Figure BDA0001845578930000094
are respectively
Figure BDA0001845578930000095
The upper and lower limit values of (a),
Figure BDA0001845578930000096
is the limit of the apparent power of the battery.
The constraints of the optimization management model also include controllable distributed power supply constraints:
Figure BDA0001845578930000097
wherein, Pi DDG,tIs the active power of the controllable distributed power supply at time t,
Figure BDA0001845578930000098
are respectively Pi DDG,tThe upper and lower limit values of (c),
Figure BDA0001845578930000099
is the upper and lower limit values of the climbing rate of the distributed power supply, wherein the controllable distributed power supply can be a micro gas turbine or a fuel cell;
Figure BDA00018455789300000910
is the power factor angle of the distributed power supply at time t,
Figure BDA00018455789300000911
is that
Figure BDA00018455789300000912
Upper and lower limit values of (d);
the constraints for optimizing the management model also include capacitor constraints:
Figure BDA00018455789300000913
wherein the content of the first and second substances,
Figure BDA00018455789300000914
is the capacitor capacity available at node i at time t,
Figure BDA00018455789300000915
respectively, the limit of the capacity; for operation of the distribution network, since the number of available capacitors is discrete, the distribution network is provided with a plurality of capacitors
Figure BDA00018455789300000916
Is a discrete decision variable;
Figure BDA00018455789300000917
is the number of times the capacitor state is changed,
Figure BDA00018455789300000918
is the maximum number of transitions, the purpose of which is to ensure that the capacitor has a good life cycle.
The constraint conditions of the optimization management model also comprise balance constraints; the balance constraint is a power flow balance formula of fundamental frequency and harmonic frequency at each moment;
for power flow at the fundamental frequency, the node current injection equation can be written as follows:
Figure BDA00018455789300000919
Figure BDA00018455789300000920
V1 abc,t＝[Vi a,t Vi b,t Vi c,t]T (12)
wherein the content of the first and second substances,
Figure BDA00018455789300000921
is a sub-matrix of the frequency node admittance matrix at time t,
Figure BDA00018455789300000922
Vi abc,ta node injection current vector and a voltage vector of the i fundamental wave frequency of a node at the moment t respectively; the injected node current typically comes from the load, renewable power (wind turbine or photovoltaic), controllable distributed power (gas turbine or fuel cell), and battery;
Figure BDA0001845578930000101
node injection current values V of A, B and C phases of i fundamental wave frequency of node at time ti a,t,Vi b,t,Vi c,tThe voltage values of i fundamental wave frequencies A, B and C of a node t at the moment are respectively;
similarly, the power flow equation for harmonic frequency h can be expressed as:
Figure BDA0001845578930000102
Figure BDA0001845578930000103
Figure BDA0001845578930000104
wherein the content of the first and second substances,
Figure BDA0001845578930000105
the node injection current values of A, B and C phases of the harmonic frequency h of the node i at the time t are respectively,
Figure BDA0001845578930000106
voltage values of A, B and C phases of i harmonic frequency h of a node t at the moment are respectively;
Figure BDA0001845578930000107
is the node admittance matrix of the harmonic frequency h at time t; harmonic components of distributed power sources and batteries are modeled as current sources with one or more branch circuits.
The cuckoo algorithm in the step 2 is an algorithm obtained by improving a standard cuckoo algorithm;
the Cuckoo algorithm is also called Cuckoo Search algorithm (CS), is a newly proposed bionic intelligent algorithm, is proposed in 2009 by Xin-She Yang professor and Deb Suash, and finds an optimal solution of a problem by using a special parasitic brooding mode of Cuckoo and combining with a Levy flight process followed by bird flight.
Cuckoo search algorithms have been proposed based on the following biological principles: cuckoos typically lay eggs in nests of other birds, brooding by parasitic means. This behavior is likely to be discovered, and once the host bird finds the intruder, the bird will discard the cuckoo egg or directly discard the original nest. The cuckoo search algorithm continuously searches for the optimal nest by simulating the cuckoo, and performs an idealized assumption:
firstly, only one egg can be produced by one cuckoo at a time, and only one egg can be produced in one bird nest;
secondly, the best nest in each generation is reserved to the next generation;
③ host birds with a certain probability PaWhen finding the invader, the host bird abandons the whole nest and immediately searches a new nest;
the new nest is created by means of Levy flight:
Figure BDA0001845578930000108
in the formula:
Figure BDA0001845578930000109
the position of the ith bird nest in the t iteration is represented, a step scale factor alpha is larger than 0 and is directly related to the severity of the problem, wherein alpha is generally selected to be L/100, and L is a characteristic range of the severity of the problem;
Figure BDA00018455789300001010
characterizing point-to-point multiplication, Levy (λ) is a value generated by Levy random process, obeying Levy distribution:
L(λ):u＝t-λ,(1＜λ＜3) (16)
the Levy flight consists of a high frequency short-distance flight and a low frequency long-distance flight, and the flight path of the Levy flight is shown in fig. 1 after 1000 times of continuous flight. It can be seen that the flight process can suddenly generate long-distance flight at a short-distance flight gathering place, and the flight direction is not fixed, so that the local optimal solution is easy to jump out, and the global optimal solution can be searched in the whole feasible region more effectively.
In the standard cuckoo algorithm, the step-size scaling factor α is a fixed value and is directly related to the severity of the problem, and is generally selected to be L/100, where L is a characteristic range of the severity of the problem. When the step size scale factor alpha takes a larger value, the global search capability of the standard cuckoo algorithm can be enhanced, and the global optimal solution area can be quickly determined; when the step size scale factor alpha is a small value, the local search capability of the standard cuckoo algorithm can be enhanced, and the convergence speed of the algorithm is improved;
the cuckoo algorithm sets the step-size scale factor alpha as a variable, takes a large value at the initial stage of iteration, accelerates the positioning of a globally optimal region, takes a small value at the later stage of iteration, and accelerates the convergence of the algorithm;
by taking the improvement of the inertia coefficient in the improved particle swarm optimization as reference, the step-size scale factor α is set as:
Figure BDA0001845578930000111
wherein alpha ismaxAnd alphaminIs the maximum and minimum of the step-size scale factor, NmaxThe maximum iteration number of the cuckoo algorithm is shown, and N is the current iteration number of the cuckoo algorithm.
As shown in fig. 2, the specific calculation flow of the cuckoo algorithm in step 2 further includes the following steps:
step 2.1, reading in system data, and deciding variables and upper and lower limits; collecting an energy value of the storage battery and an actual output value of the distributed power supply at the last optimization time, and forming closed-loop feedback of model predictive control by using a known variable during current optimization; initializing parameters of a cuckoo algorithm;
step 2.2, initializing the iteration number k to be 0, randomly generating n nests in the feasible region, and setting the position as x (x)1,x2,...,xn)TEach bird nest contains m state variables;
step 2.3, calculating the fitness of each nest based on the power flow analysis of the fundamental frequency and the harmonic frequency; penalizing the fitness function if the power flow is not aggregated;
step 2.4, sorting the individual advantages and disadvantages, selecting the optimal nest position, and reserving the nest position to the next generation;
step 2.5, updating other nest positions by utilizing Levy flight, combining with a parent generation, searching for the best and reserving to the next generation;
step 2.6, judging whether the bird is found by the host bird, if so, selecting a abandoned bird nest, and updating the position by utilizing Levy flight;
step 2.7, selecting the current global optimal bird nest and judging whether the algorithm ending condition is met; if not, the step returns to the step 2.3.
As shown in fig. 3, in step 3, when each optimization time t comes, the energy value of the storage battery and the actual output force value of the distributed power supply at the end of the previous optimization time t-1, namely the beginning of the optimization time t, are collected and substituted into the relevant constraint as a known variable during the optimization at the current optimization time t; the optimization program takes the minimum network loss in the predicted time domain T as an objective function, adopts a cuckoo algorithm for optimization, only issues a scheduling plan of an optimization time T, and temporarily repeats the optimization process when the next optimization time T +1 comes; the real storage battery energy value of the system and the actual output value of the distributed power supply are acquired during each optimization, so that effective closed-loop feedback is formed, and the uncertainty influence brought by renewable energy is reduced.
In the present invention:
1. meanwhile, an optimal power distribution network operation model is established in consideration of harmonic pollution and voltage unbalance level;
2. an improved cuckoo search algorithm is provided for solving an optimal power distribution network operation model;
3. the optimal power distribution network operation model is matched with the model prediction control to form closed-loop feedback, so that the influence caused by the uncertainty of renewable energy sources is reduced.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concept. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (7)

1. An optimal management method for the power quality of an active power distribution network is characterized by comprising the following steps:
step 1, establishing an optimization management model of the electric energy quality of an active power distribution network in a prediction time domain, wherein the optimization management model takes minimization of network power loss in the prediction time domain as an optimization target, takes conditions related to the electric energy quality and safe system operation as constraint conditions, and the constraint conditions comprise electric energy quality constraint and system safety constraint, wherein the electric energy quality constraint comprises integral voltage harmonic distortion constraint, single harmonic distortion constraint and voltage unbalance factor constraint; the system safety constraint comprises a voltage effective value constraint and a current effective value constraint; the objective function of the optimization management model is as follows:
Figure FDA0003515874200000011
wherein the content of the first and second substances,
Figure FDA0003515874200000012
is the net active loss associated with the fundamental frequency at time t,
Figure FDA0003515874200000013
is the active power loss associated with the harmonic frequency h at time t; Ω is the set of relevant harmonic frequencies; t is a prediction time domain related to model prediction control, where the scheduling interval is 1h or 0.5h, that is, the optimal management model will take the minimum active power loss in the prediction time domain T as an objective function, execute once for 1h or 0.5h, but only issue a scheduling plan at the next moment, and repeat the process until a boundary point at the next moment comes;
step 2, solving the optimized management model by adopting a cuckoo algorithm to obtain an optimal management model result;
the cuckoo algorithm is an algorithm obtained by improving a standard cuckoo algorithm;
the standard cuckoo algorithm continuously searches the optimal nest process by simulating cuckoos, and performs an idealized assumption:
firstly, only one egg can be produced by one cuckoo at a time, and only one egg can be produced in one bird nest;
the best bird nest in each generation can be reserved to the next generation;
③ host birds with a certain probability PaWhen finding an invader, the host bird abandons the whole nest and immediately searches a new nest;
the new nest is generated by means of Levy flight:
Figure FDA0003515874200000014
in the formula:
Figure FDA0003515874200000015
the position of the ith bird nest in the t iteration is represented, a step scale factor alpha is larger than 0, the step scale factor alpha is directly related to the severity of the problem, alpha is selected to be L/100, and L is a characteristic range of the severity of the problem;
Figure FDA0003515874200000016
characterizing point-to-point multiplication, Levy (λ) is a value generated by Levy random process, obeying Levy distribution:
L(λ)～u＝t-λ,1＜λ＜3 (16)
the Levy flight consists of high-frequency short-distance flight and low-frequency long-distance flight;
when the step size scale factor alpha takes a larger value, the global search capability of the standard cuckoo algorithm can be enhanced, and a global optimal solution area can be quickly determined; when the step size scale factor alpha is a small value, the local searching capability of the standard cuckoo algorithm can be enhanced, and the convergence speed of the algorithm is improved;
the cuckoo algorithm sets the step size scale factor alpha as a variable, takes a larger value at the initial stage of iteration, accelerates the positioning of a globally optimal region, takes a smaller value at the later stage of iteration, and accelerates the convergence of the algorithm;
the step size scaling factor α is set to:
Figure FDA0003515874200000021
wherein alpha ismaxAnd alphaminFor the maximum and minimum values of the step-size scale factor, NmaxThe maximum iteration number of the cuckoo algorithm is obtained, and N is the current iteration number of the cuckoo algorithm;
the step 2 further comprises the following steps:
step 2.1, reading in system data, and deciding variables and upper and lower limits; collecting an energy value of a storage battery at the last optimization moment and an actual output value of a distributed power supply, and forming closed-loop feedback of model predictive control by using the energy value and the actual output value as known variables during optimization at the current moment; initializing parameters of the cuckoo algorithm;
step 2.2, initializing the iteration number k to 0, and randomly generating n in the feasible region0A bird nest in the position
Figure FDA0003515874200000022
Each bird nest contains m state variables;
2.3, calculating the fitness of each nest based on the power flow analysis of the fundamental frequency and the harmonic frequency; penalizing a fitness function if the power flow is not aggregated;
step 2.4, sorting the individual advantages and disadvantages, selecting the optimal nest position, and reserving the nest position to the next generation;
step 2.5, updating other nest positions by utilizing the Levy flight, combining with a parent generation, searching for the best and reserving to the next generation;
step 2.6, judging whether the bird is found by the host bird, if so, selecting a abandoned bird nest, and updating the position by utilizing the Levy flight;
step 2.7, selecting the current global optimal bird nest and judging whether the algorithm ending condition is met; if not, returning to the step 2.3;
and 3, on the basis of the optimal management model result, reducing the influence caused by the prediction uncertainty problem by adopting model prediction control with a feedback characteristic in a matching way, wherein the prediction uncertainty problem is the uncertainty caused when renewable energy is accessed into the active power distribution network.
2. The method for optimally managing the power quality of an active power distribution network as recited in claim 1 wherein said overall voltage harmonic distortion constraint can be used to reflect the overall harmonic level of node i as follows:
Figure FDA0003515874200000023
wherein THD is an abbreviation for Total voltage harmonic distortion, referring to the overall voltage harmonic distortion constraint; vi p,t,
Figure FDA0003515874200000024
Fundamental frequency and harmonic frequency h voltage value, THD of p phase at time t node ii,maxIs the upper limit of THD;
the individual harmonic distortion constraint is an index that evaluates the harmonic level of each relevant frequency, and the individual harmonic distortion constraint for each node i satisfies the following constraint:
Figure FDA0003515874200000031
wherein IHD is an abbreviation of Industrial Harmonic Distortion Constraints, which refers to single Harmonic Distortion Constraints;
Figure FDA0003515874200000032
is the upper limit of the individual voltage harmonic distortion for the harmonic frequency h;
the voltage imbalance factor constraint may be used to reflect the voltage imbalance level, and to ensure a relatively good voltage imbalance level, the following constraints should be satisfied:
Figure FDA0003515874200000033
wherein VUF is an abbreviation of Voltage Unbalance Factor Constraints and refers to Voltage imbalance Factor Constraints; vi 1,t,Vi 2,tPositive sequence voltage values and negative sequence voltage values of a node i at the moment t respectively; UM% is the upper limit in the form of percentage VUF; since the definition of the voltage imbalance factor is for a sine wave, only the voltage at the fundamental frequency is used to calculate VUF.
3. The method for optimizing and managing the power quality of the active power distribution network according to claim 1, wherein the voltage effective value constraint is as follows:
Figure FDA0003515874200000034
wherein the content of the first and second substances,
Figure FDA0003515874200000035
is the effective value of the voltage of the p phase of the node i at the moment t;
Figure FDA0003515874200000036
the upper limit and the lower limit of the voltage effective value are respectively;
the current effective value constraint is as follows:
Figure FDA0003515874200000037
wherein the content of the first and second substances,
Figure FDA0003515874200000038
is the p-th phase of branch ij at time tAn effective value of current of;
Figure FDA0003515874200000039
the upper limit and the lower limit of the current effective value are respectively;
Figure FDA00035158742000000310
is the current value of the p-th phase fundamental frequency and harmonic frequency h of the branch ij at the time t.
4. The method for optimizing and managing the power quality of the active power distribution network according to claim 1, wherein the constraints of the optimization management model further include battery constraints, and the battery constraints include energy value constraints and power value constraints;
the energy value constraint is:
Figure FDA0003515874200000041
wherein the content of the first and second substances,
Figure FDA0003515874200000042
is the energy stored in the battery at the end of time t; ei,max,Ei,minAre respectively
Figure FDA0003515874200000043
Upper and lower limits of (P)i ES,tIs the active power of the accumulator at time t, epsilonin,εoutThe charging and discharging efficiency of the storage battery respectively; equation (7) describes the relationship between two continuous-time energy values and the limit that the energy value of the storage battery satisfies the upper and lower limits, respectively;
in order to ensure that the active power, the reactive power and the apparent power of the battery are within limits, the power value constraints should satisfy the following constraints:
Figure FDA0003515874200000044
wherein the content of the first and second substances,
Figure FDA0003515874200000045
is the rated active power of the storage battery,
Figure FDA0003515874200000046
is the battery reactive power at time t,
Figure FDA0003515874200000047
are respectively
Figure FDA0003515874200000048
The upper and lower limit values of (c),
Figure FDA0003515874200000049
is the limit of the apparent power of the battery.
5. The method for optimizing and managing the power quality of the active power distribution network according to claim 1, wherein the constraints of the optimization management model further include controllable distributed power supply constraints:
Figure FDA00035158742000000410
wherein, Pi DDG,tIs the active power of the controllable distributed power supply at time t,
Figure FDA00035158742000000411
are respectively Pi DDG,tThe upper and lower limit values of (a),
Figure FDA00035158742000000412
the upper limit value and the lower limit value of the gradient rate of the distributed power supply, wherein the controllable distributed power supply is a micro gas turbine or a fuel cell;
Figure FDA00035158742000000413
is the power factor angle of the distributed power supply at time t,
Figure FDA00035158742000000414
is that
Figure FDA00035158742000000415
Upper and lower limit values of (d);
the constraints of the optimal management model further include capacitor constraints:
Figure FDA00035158742000000416
wherein the content of the first and second substances,
Figure FDA00035158742000000417
is the capacitor capacity available at node i at time t,
Figure FDA00035158742000000418
respectively, the limit of the capacity; for operation of the distribution network, since the number of available capacitors is discrete, the distribution network is provided with a plurality of capacitors
Figure FDA00035158742000000419
Is a discrete decision variable;
Figure FDA00035158742000000420
is the number of times the capacitor state is changed,
Figure FDA00035158742000000421
is the maximum number of transitions, the purpose of which is to ensure that the capacitor has a good life cycle.
6. The method for optimizing and managing the power quality of the active power distribution network according to claim 1, wherein the constraints of the optimization management model further include balance constraints; the balance constraint is a power flow balance formula of the fundamental frequency and the harmonic frequency at each moment;
for power flow at the fundamental frequency, the node current injection equation is written as follows:
Figure FDA0003515874200000051
Figure FDA0003515874200000052
V1 abc,t＝[Vi a,t Vi b,t Vi c,t]T (12)
wherein the content of the first and second substances,
Figure FDA0003515874200000053
is a sub-matrix of the fundamental frequency node admittance matrix at time t, Ii abc,t,Vi abc,tA node injection current vector and a voltage vector of the fundamental frequency are respectively at a time t node i; the injected node current comes from a load, a renewable power supply, a controllable distributed power supply and a storage battery; i isi a,t,Ii b,t,Ii c,tThe node injection current values V of A, B and C phases of the fundamental frequency at the time t node ii a,t,Vi b,t,Vi c,tVoltage values of the fundamental wave frequency A, B and C phases at a time t node i respectively;
similarly, the power flow equation for the harmonic frequency h is expressed as:
Figure FDA0003515874200000054
Figure FDA0003515874200000055
Figure FDA0003515874200000056
wherein the content of the first and second substances,
Figure FDA0003515874200000057
the node injection current values of A, B and C phases of the harmonic frequency h of the node i at the time t are respectively,
Figure FDA0003515874200000058
voltage values of A, B and C phases of i harmonic frequency h of a node t at the moment are respectively;
Figure FDA0003515874200000059
is the node admittance matrix of the harmonic frequency h at time t; harmonic components of distributed power sources and batteries are modeled as current sources with one or more branch circuits.
7. The method for optimizing and managing the power quality of the active power distribution network according to claim 1, wherein in the step 3, when each optimization time t comes, the energy value of the storage battery and the actual output value of the distributed power supply at the end of the last optimization time t-1, which is the initial time of the optimization time t, are collected and substituted into the relevant constraint as a known variable during the optimization at the current optimization time t; the optimization program takes the minimum network loss in the predicted time domain T as a target function, adopts the cuckoo algorithm for optimization, only issues the scheduling plan of the optimization time T, and temporarily repeats the optimization process when the next optimization time T +1 comes; and the real storage battery energy value of the system and the actual output value of the distributed power supply are acquired during each optimization, so that effective closed-loop feedback is formed, and the uncertainty influence brought by the renewable energy is reduced.
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* Cited by examiner, † Cited by third party
Title
Capacitor Bank Step State Optimization under Load Alteration in Smart Distribution Networks;Farhad Ahbab等;《 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)》;20170713;第1-5页 *
基于改进CS算法的电网损耗最小仿真研究;陈功贵等;《实验室研究与探索》;20170731;第36卷(第7期);第92-96页 *
考虑谐波因素的配电网无功补偿配置优化应用研究;肖林甫;《中国优秀硕士学位论文全文库》;20170315;第9-16、30-35页 *

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