CN114825348A - Power distribution network double-layer optimization scheduling method considering wind and light uncertainty - Google Patents

Power distribution network double-layer optimization scheduling method considering wind and light uncertainty Download PDF

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CN114825348A
CN114825348A CN202210435130.2A CN202210435130A CN114825348A CN 114825348 A CN114825348 A CN 114825348A CN 202210435130 A CN202210435130 A CN 202210435130A CN 114825348 A CN114825348 A CN 114825348A
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周柯
金庆忍
丘晓茵
莫枝阅
宋益
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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Abstract

The invention discloses a power distribution network double-layer optimization scheduling method considering wind and light uncertainty, which relates to the technical field of power grid regulation and control.A probability modeling method is adopted, a double-layer optimization model is constructed by constructing a representative scene to approach the real-time uncertainty of wind power, photovoltaic output and load, the double-layer optimization model comprises an upper layer optimization model and a lower layer optimization model, the upper layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, and a network switching state is optimized by adopting a hybrid genetic algorithm based on backbone particle swarm to obtain an optimal network topology structure; the lower-layer optimization model is a distribution network operation optimization model, the optimal voltage quality is taken as a target, and an optimized wind-photovoltaic power generation system and capacitor bank reactive power output and energy storage active power output scheduling plan is obtained by solving mixed integer random programming; by solving the double-layer optimization model, the double-layer optimization model reaches a stable state, and an optimal scheduling plan and an optimal dynamic network frame topology of the power distribution network are obtained.

Description

Power distribution network double-layer optimization scheduling method considering wind and light uncertainty
Technical Field
The invention belongs to the technical field of power grid regulation and control, and particularly relates to a double-layer optimal scheduling method for a power distribution network, which takes wind and light uncertainty into consideration.
Background
With the promotion of environmental protection policies and new energy development policies, distributed power sources are increasingly integrated into modern power distribution networks, so that the traditional power system gradually changes to a novel power system. In the context of high permeability of renewable energy power generation, the intermittency of distributed power generation and the high impedance ratio of the distribution network may cause voltage fluctuations or even out of limits, which present greater technical challenges to voltage control and economic operation of the distribution network. The network reconstruction technology is an important means for supporting the optimized operation of the active power distribution network, and has important functions of improving voltage distribution, eliminating overload, reducing network loss, improving operation economy and the like.
Traditional voltage adjusting equipment such as a capacitor bank, a load tap changer and the like belong to mechanical equipment, and the response speed is low; power electronic reactive compensation equipment, such as SVC and STATCOM, has a relatively fast response speed, but is expensive, which prevents wide application. In an active power distribution network, a distributed power supply such as a photovoltaic power supply and a fan can provide flexible and quick reactive support through control under normal operation conditions. Therefore, the distributed power supply can play a greater role in voltage control and network reconfiguration of the active power distribution network.
And the traditional voltage regulation and optimal scheduling methods lack coordination, the optimal scheduling potential of the distributed power supply cannot be fully considered, and the randomness of power generation and load of the distributed power supply is ignored. Therefore, a power distribution network double-layer optimization scheduling method considering wind and light uncertainty is needed.
Disclosure of Invention
The invention aims to provide a power distribution network double-layer optimization scheduling method considering wind and light uncertainty, so that the defect that the traditional voltage regulation and optimization scheduling method is lack of coordination is overcome.
In order to achieve the purpose, the invention provides a power distribution network double-layer optimization scheduling method considering wind and light uncertainty, which comprises the following steps:
by adopting a probability modeling method, a representative scene is constructed to approach the real-time uncertainty of wind power, photovoltaic output and load to construct a double-layer optimization model, the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model, the upper-layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, and a hybrid genetic algorithm based on backbone particle swarm is adopted to optimize the network switching state to obtain an optimal network topology structure; the lower-layer optimization model is a distribution network operation optimization model, the optimal voltage quality is taken as a target, and an optimized wind-photovoltaic power generation system and capacitor bank reactive power output and energy storage active power output scheduling plan is obtained by solving mixed integer random programming;
and solving the double-layer optimization model to enable the double-layer optimization model to reach a stable state, so as to obtain an optimal scheduling plan and an optimal dynamic network frame topology of the power distribution network.
Preferably, the upper-layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, an optimal network topology is obtained by optimizing the network switch state, and the optimal network topology meets power flow constraint, node voltage constraint and radial topology constraint; and meanwhile, a typical scene is considered in load flow calculation, probability weighting is obtained to obtain network loss, and the optimized network topology structure is transmitted to a lower-layer model as input.
Preferably, the constraints to be considered by the lower layer optimization model are power flow constraints, distributed power supply constraints, capacitor bank constraints and node voltage constraints.
Preferably, the construction of the upper layer optimization model comprises the following steps:
establishing an upper layer objective function based on constraint by adopting a probability modeling method according to uncertain variables comprehensively considering wind and light output, thereby obtaining an initial distribution network topological structure;
and optimizing the network switch state in the initial distribution network topology structure by adopting a hybrid genetic algorithm based on backbone particle swarm to obtain an optimal network topology structure.
Preferably, a hybrid genetic algorithm based on backbone particle swarm is adopted to optimize the network switch state in the initial distribution network topology structure to obtain an optimal network topology structure, which specifically comprises the following steps:
initializing parameters;
solving the fitness value of each chromosome, comparing the fitness value of each chromosome with the fitness values of the historical optimal solution and the population global optimal solution, and updating the individual historical optimal solution and the population global optimal solution;
performing a selection operation;
according to the cross probability, performing cross operation to complete global search;
according to the mutation probability, a position updating mode of the backbone particle swarm is adopted as a mutation operator to execute mutation operation;
and judging whether a termination condition is met, if so, finishing the algorithm to obtain a population optimal solution, decoding the population optimal solution to obtain an optimal network topology structure, and if not, returning to solve each chromosome fitness value again.
Preferably, with the optimal voltage quality as a target, obtaining an optimized scheduling plan of the wind-photovoltaic power generation system, the capacitor bank reactive power output and the energy storage active power output by solving the mixed integer random programming, and specifically comprising:
solving an upper distribution network operation optimization model by adopting a mixed integer random planning method to obtain an optimal operation plan;
and transmitting the optimal operation plan to an upper-layer optimization model as the operation setting of the upper-layer optimization model.
Preferably, the optimal operation plan comprises wind power photovoltaic and the modulation value of the reactive power output and the energy storage active power output of the capacitor bank.
Preferably, the double-layer optimization model is solved by adopting a Nash equilibrium theory in a game theory, so that the double-layer optimization model reaches a stable state, and an optimal scheduling plan and an optimal dynamic grid frame topology of the power distribution network are obtained.
Preferably, the solving of the double-layer optimization model by using a nash equilibrium theory in a game theory specifically comprises the following steps: and judging whether the iteration process is converged or not based on Nash equilibrium conditions, and if so, outputting an optimal scheduling plan and an optimal dynamic network frame topology.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a power distribution network double-layer optimization scheduling method considering wind and light uncertainty, which adopts a probability modeling method to construct a double-layer optimization model by constructing a representative scene to approach the real-time uncertainty of wind power, photovoltaic output and load, wherein the double-layer optimization model comprises an upper layer optimization model and a lower layer optimization model, the upper layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, and a network switching state is optimized by adopting a hybrid genetic algorithm based on backbone particle swarm to obtain an optimal network topology structure; the lower-layer optimization model is a distribution network operation optimization model, the optimal voltage quality is taken as a target, and an optimized wind-photovoltaic power generation system and capacitor bank reactive power output and energy storage active power output scheduling plan is obtained by solving mixed integer random programming; and solving the double-layer optimization model to enable the double-layer optimization model to reach a stable state, so as to obtain an optimal scheduling plan and an optimal dynamic network frame topology of the power distribution network. The invention considers the global voltage and load characteristics, realizes the voltage control of the active power distribution network by adjusting the network topology structure, has high solving efficiency, can effectively improve the voltage deviation and reduce the voltage loss, and provides powerful algorithm support for the optimal operation of the active power distribution network under the consideration of wind and light uncertainty.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a double-layer optimized dispatching method for a power distribution network, which takes wind and solar uncertainty into account;
FIG. 2 is a schematic diagram of a two-layer optimization model structure of the power distribution network of the present invention;
fig. 3 is a schematic diagram of comparison results of the network loss of the active power distribution network before and after the implementation of the double-layer optimization scheduling method for the active power distribution network according to one embodiment of the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for double-layer optimal scheduling of a power distribution network considering wind and solar uncertainties provided by the present invention includes:
s1, constructing a double-layer optimization model by constructing a representative scene to approximate real-time uncertainty of wind power, photovoltaic output and load by adopting a probability modeling method, wherein the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model as shown in figure 2,
the upper-layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, the state of a tie switch is taken as an optimization variable, basic network power flow constraint and radial topology constraint are considered, the state of the network switch is optimized by adopting a hybrid genetic algorithm based on backbone particle swarm, a typical scene is considered in power flow calculation, probability weighting is obtained to obtain network loss, and the optimized network topology structure is transmitted to a lower-layer model to be used as input. Therefore, the construction of the upper layer optimization model comprises the following steps:
and S111, the upper-layer optimization model considers the influences of wind and light output, load fluctuation and reactive compensation devices on the network load flow and the structure of the active power distribution network, the minimum network loss is taken as an optimization target, and the voltage distribution of the system is improved through network reconstruction. Therefore, the probability modeling party is adopted to construct a distribution network reconstruction optimization model by comprehensively considering uncertain variables such as wind-solar output, load and the like,
the probability modeling method is used for approximating real-time uncertainty of wind power, photovoltaic output and load by constructing a representative scene. Compared with the traditional Monte Carlo sampling method which needs a large number of generated scenes and is simplified in a tedious process, the probability modeling method can directly generate a small number of typical scenes. The method includes the steps that the random changes of photovoltaic power, wind power generation and loads in an active power distribution network are considered, the wind power generation system adopts a probability density function obeying Weibull distribution to describe the power of wind power generation, the photovoltaic power generation system adopts a probability density function obeying Beta distribution to approximate the maximum power output of the photovoltaic power generation system, and the power distribution system loads adopt the probability density function obeying normal distribution to describe the fluctuation changes of the power distribution system. For the above uncertain variables, the variables can be divided into a plurality of equal-length intervals according to a probability density function describing the probability distribution of the uncertain variables. Therefore, in each interval, the weighted average value of the variable in the interval can be calculated by integrating the variable and the product term of the probability density function, and the probability of the variable value in the interval can be obtained by integrating the probability density function. The probability modeling method is characterized in that a probability distribution function of wind power, photovoltaic power generation output power and load is divided into E, F intervals and G intervals respectively and is defined as E, F states and G states, states of three random variables are mapped mutually to form E x F x G groups of typical scenes, the probability of occurrence of each group of typical scenes corresponds to the probability of simultaneous occurrence of combined states of the wind power, the photovoltaic power generation and the load, and the typical scenes can be applied to a double-layer optimization model to describe uncertainty of the variables. In addition, the energy storage can reduce the amount of abandoned wind and abandoned light of the distributed power supply in a charging and discharging energy mode, and the energy storage can relieve partial pressure of new energy consumption in a time period with high new energy permeability;
s112, establishing an upper layer objective function of the upper layer optimization model, namely an upper layer objective function of the network configuration reconstruction optimization model, wherein the expression is as follows:
Figure BDA0003612533110000051
in the above formula, the first and second carbon atoms are,
Figure BDA0003612533110000052
to meet an optimal target value at a given confidence level;
and calculating probability constraint of an upper layer objective function by considering uncertain variables such as wind-solar output and the like, wherein the expression of the probability constraint is as follows:
Figure BDA0003612533110000053
in the above formula, Pr represents the probability of event eligibility; beta is a given confidence level
And obtaining an initial distribution network topological structure according to the upper layer objective function and the probabilistic constraint.
S113, taking basic network flow constraint and radial topology constraint into consideration, optimizing a network switch state in an initial distribution network topology structure by adopting a hybrid genetic algorithm based on backbone particle swarm, and solving to obtain an optimal distribution network topology structure under the current operation condition and a corresponding optimal network loss value.
The method for optimizing the network switch state by adopting the hybrid genetic algorithm based on the backbone particle swarm to obtain the optimal network topology structure specifically comprises the following steps:
s121, initializing parameters, wherein the parameters comprise: cross probability Pc, variation probability Pm and population size; initializing a population, a historical optimal solution and a population global optimal solution;
s122, solving the fitness value of each chromosome, comparing the fitness value of each chromosome with the fitness values of the historical optimal solution and the population global optimal solution, and updating the individual historical optimal solution and the population global optimal solution;
s123, executing selection operation, and selecting part of individuals to be reserved by a wheel disc selection method;
s124, according to the cross probability, performing cross operation to complete global search;
s125, according to the mutation probability, adopting a position updating mode of the backbone particle swarm as a mutation operator to execute mutation operation;
specifically, example group position x in the standard particle swarm algorithm t+1 Update and velocity v t+1 As shown in formulas (3) to (4):
v t+1 ＝wv t +c 1 r 1 (pbest t -x t )+c 2 r 2 (gbest-x t ) (3)
x t+1 ＝x t +v t+1 (4)
in the above formula, w is the inertial weight, c 1 And c 2 Called acceleration operator, r 1 And r 2 Is at [0, 1 ]]Random numbers uniformly distributed thereon;
however, the evolution equation of the standard particle swarm algorithm can not ensure absolute convergence to the global optimal solution, each particle converges to the weighted average of the individual historical extreme value and the global extreme value, the efficiency and the precision of algorithm search can be improved by a cooperative probability search mode, and the complex parameter adjustment of the standard particle swarm algorithm is avoided, so that the position x of the backbone particle swarm is adopted t+1 The update equation is:
Figure BDA0003612533110000061
s126, judging whether a termination condition is met, if so, finishing the algorithm to obtain a population optimal solution, and decoding the population optimal solution to obtain an optimal network topology structure; if yes, returning to the step S122 to obtain the fitness value of each chromosome again until the termination condition is met;
and S127, the upper-layer optimization model transmits the optimal topological structure to the lower-layer optimization model to serve as a grid structure set in the lower-layer optimization model.
The lower-layer optimization model is a distribution network operation optimization model, the wind power, the photovoltaic power generation system and the capacitor bank reactive power output are optimized by solving mixed integer random programming by taking the optimal voltage quality as a target, and the energy storage active power output obtains a scheduling plan to obtain a scheduling plan; the constraints that the lower layer optimization model needs to consider further include: the method comprises the following steps of (1) power flow constraint, distributed power supply constraint, capacitor bank constraint and node voltage constraint;
the lower layer optimization model is used for solving the optimal network topology structure transmitted by the upper layer optimization model to obtain a scheduling decision value, and the method comprises the following steps:
s131, solving an upper-layer distribution network operation optimization model by adopting a mixed integer random planning method to obtain an optimal plan of reactive power output and energy storage active power output of the wind power and photovoltaic power generation system and the capacitor bank, wherein the optimal plan corresponds to the minimum voltage deviation value; the method specifically comprises the following steps:
establishing an optimization problem in a lower-layer optimization model as a mixed integer random programming model, wherein the optimization problem essentially belongs to the mixed integer random programming problem, and the optimization variables comprise the following parameters by taking the minimum voltage deviation as an optimization target: the optimization problem can be quickly solved by adopting a Cplex commercial solver, so that the modulation values of the wind power photovoltaic, the capacitor bank reactive power output and the energy storage active power output are obtained;
wherein the constraints of the mixed integer stochastic programming model comprise: branch flow constraints and equipment constraints,
the branch flow constraint comprises: node power balance and voltage constraints (as represented by equations (6) - (8)), node voltage constraints (as represented by equation (9)), branch power constraints (as represented by equation (10)), and radial network topology constraints (as represented by equation (11)).
Figure BDA0003612533110000071
Figure BDA0003612533110000072
Figure BDA0003612533110000073
Figure BDA0003612533110000074
Figure BDA0003612533110000075
Figure BDA0003612533110000081
In the above formula, P i,t Indicating that the line at node i injects real power at time t,
Figure BDA0003612533110000082
indicating that the energy storage at node i injects active power at time t,
Figure BDA0003612533110000083
indicating that the photovoltaic of node i at time t injects active power,
Figure BDA0003612533110000084
indicating that the fan at the point i at the moment t injects active power,
Figure BDA0003612533110000085
representing the active load, Q, of node i at time t i,t Indicating that the line at node i at time tnode injects reactive power,
Figure BDA0003612533110000086
the capacitor bank representing node i at time tnode injects reactive power,
Figure BDA0003612533110000087
represents the photovoltaic injected reactive power at the time tnode i,
Figure BDA0003612533110000088
indicating that the fan at node i at time t injects reactive power,
Figure BDA0003612533110000089
representing the reactive load, V, at node i at time t i,t Representing the node voltage, V, at node i at time t 0 Representing rated voltage, R i Denotes the resistance value, X, of the line i i Which represents the reactance value of the line i,
Figure BDA00036125331100000810
represents an upper limit value of the node voltage,Vrepresents a lower limit value of the node voltage,
Figure BDA00036125331100000811
represents an upper limit value of the transmission power of the line,Prepresents the lower limit of the line transmission power, g p,t Network architecture, G, representing time t p Indicating the allowed radial network configuration.
The device constraints include: distributed power output constraints (as represented by equations (12) - (13)), capacitor operation constraints (as represented by equations (14) - (15)), and energy storage operation constraints (as represented by equations (16) - (19)),
Figure BDA00036125331100000812
Figure BDA00036125331100000813
Figure BDA00036125331100000814
Figure BDA00036125331100000815
Figure BDA00036125331100000816
Figure BDA00036125331100000817
Figure BDA00036125331100000818
Figure BDA00036125331100000819
in the above formula, the first and second carbon atoms are,
Figure BDA00036125331100000820
represents the photovoltaic power generation capacity of the node i,
Figure BDA00036125331100000821
representing the wind power generation capacity of node i,
Figure BDA00036125331100000822
representing the unit reactive output of the capacitor bank,
Figure BDA00036125331100000823
representing the capacitor bank switching amount at time tnode i,
Figure BDA00036125331100000824
represents the upper limit of the switching amount of the capacitor bank,Trepresents the lower limit of the switching amount of the capacitor bank,
Figure BDA00036125331100000825
represents the energy storage capacity, mu, of node i at time t ch Charging target for representing stored energyZhi, mu dis Discharge mark, η, representing stored energy ch Representing the charging efficiency, η, of the stored energy dis The discharge efficiency of the stored energy is shown,Erepresents the lower limit of the electric quantity of the energy storage operation,
Figure BDA0003612533110000091
represents the upper limit of the electric quantity of the energy storage operation, ES Prepresents the lower limit of the output power for the energy storage operation,
Figure BDA0003612533110000092
representing the upper output power limit of the stored energy operation.
And S132, the lower-layer optimization model transmits the optimal operation plan (namely the dispatching values of the reactive power output and the energy storage active power output of the wind power photovoltaic and capacitor bank) to the upper-layer optimization model to serve as the dispatching values of the active power output and the reactive power output of the corresponding equipment set in the upper-layer optimization model. By fully utilizing the reactive output of the distributed power supply and the martial function compensation device, the network loss is reduced, the peak clipping and valley filling are performed under the energy management effect of energy storage, and the voltage deviation is improved.
S2, carrying out iterative solution on the double-layer optimization model by adopting a Nash equilibrium theory in a game theory, enabling the double-layer optimization model to reach a stable state, and obtaining an optimal scheduling plan and an optimal dynamic network frame topology of the power distribution network, wherein the iterative solution specifically comprises the following steps:
and judging whether the iteration process is converged or not based on Nash equilibrium conditions, and if so, outputting an optimal scheduling plan and an optimal dynamic network frame topology.
One embodiment of the power distribution network double-layer optimization scheduling method considering wind-solar uncertainty of the present invention is described in detail, so that those skilled in the art can understand the present invention more:
the method comprises the steps of carrying out example analysis based on an IEEE33 node power distribution system, accessing a capacitor bank, energy storage equipment, wind power generation and photovoltaic power generation on partial nodes, and considering randomness of a fan, photovoltaic power generation and load. Fig. 3 shows the comparison of the network loss of the active power distribution network before and after the application of the proposed double-layer optimization scheduling method of the active power distribution network, and it can be seen that the proposed model can effectively reduce the network loss. In a random scene, table 1 compares the optimization effects of the active power distribution network double-layer optimization scheduling and single-layer scheduling methods, where the average network loss is 147.9kW, the average voltage deviation is 0.0392pu, the disconnected line switch is 33/34/35/36/37, the average network loss is 118.8kW, the average voltage deviation is 0.0242pu, the disconnected line switch is 5/9/15/27/32, the average network loss is 72.7kW, the average voltage deviation is 0.0153pu, and the disconnected line switch is 7/9/14/28/36 in an initial scene.
Table 1 optimization effect of double-layer optimized scheduling and single-layer scheduling method for comparison of active power distribution network
Average loss of network Deviation of average voltage Disconnect switch
Initial scene 147.9 0.0392 33/34/35/36/37
Context after upper layer optimization 118.8 0.0242 5/9/15/27/32
Double-layer optimized scene 72.7 0.0153 7/9/14/28/36
The effect of reducing the loss and voltage deviation of the double-layer optimized scheduling compared to the single-layer optimized scheduling can be seen from table 1.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.

Claims (9)

1. A power distribution network double-layer optimization scheduling method considering wind and light uncertainty is characterized by comprising the following steps:
by adopting a probability modeling method, a representative scene is constructed to approach the real-time uncertainty of wind power, photovoltaic output and load to construct a double-layer optimization model, the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model, the upper-layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, and a hybrid genetic algorithm based on backbone particle swarm is adopted to optimize the network switching state to obtain an optimal network topology structure; the lower-layer optimization model is a distribution network operation optimization model, the optimal voltage quality is taken as a target, and an optimized wind-photovoltaic power generation system and capacitor bank reactive power output and energy storage active power output scheduling plan is obtained by solving mixed integer random programming;
and solving the double-layer optimization model to enable the double-layer optimization model to reach a stable state, so as to obtain an optimal scheduling plan and an optimal dynamic network frame topology of the power distribution network.
2. The power distribution network double-layer optimization scheduling method considering wind and light uncertainty according to claim 1, characterized in that the upper layer optimization model is a distribution network reconstruction optimization model, the minimum network loss is taken as an optimization target, an optimal network topology is obtained by optimizing a network switch state, and the optimal network topology meets power flow constraints, node voltage constraints and radial topology constraints; and meanwhile, a typical scene is considered in load flow calculation, probability weighting is obtained to obtain network loss, and the optimized network topology structure is transmitted to a lower-layer model as input.
3. The power distribution network double-layer optimization scheduling method considering wind and solar uncertainty according to claim 1, wherein the lower layer optimization model further needs to consider constraints including power flow constraints, distributed power supply constraints, capacitor bank constraints and node voltage constraints.
4. The power distribution network double-layer optimization scheduling method considering wind and solar uncertainty according to claim 1, wherein the construction of the upper layer optimization model comprises the following steps:
establishing an upper layer objective function based on constraint by adopting a probability modeling method according to uncertain variables comprehensively considering wind and light output, thereby obtaining an initial distribution network topological structure;
and optimizing the network switch state in the initial distribution network topology structure by adopting a hybrid genetic algorithm based on backbone particle swarm to obtain an optimal network topology structure.
5. The power distribution network double-layer optimization scheduling method considering wind and light uncertainty according to claim 4, wherein a hybrid genetic algorithm based on backbone particle swarm is adopted to optimize a network switch state in an initial distribution network topology structure to obtain an optimal network topology structure, and specifically comprises the following steps:
initializing parameters;
solving the fitness value of each chromosome, comparing the fitness value of each chromosome with the fitness values of the historical optimal solution and the population global optimal solution, and updating the individual historical optimal solution and the population global optimal solution;
performing a selection operation;
according to the cross probability, performing cross operation to complete global search;
according to the mutation probability, a position updating mode of the backbone particle swarm is adopted as a mutation operator to execute mutation operation;
and judging whether a termination condition is met, if so, finishing the algorithm to obtain a population optimal solution, decoding the population optimal solution to obtain an optimal network topology structure, and if not, returning to solve each chromosome fitness value again.
6. The power distribution network double-layer optimal scheduling method considering wind and photovoltaic uncertainty according to claim 1, wherein an optimized scheduling plan of wind and photovoltaic power generation systems and capacitor bank reactive power output and energy storage active power output is obtained by solving mixed integer stochastic programming with the aim of optimal voltage quality, and specifically comprises the following steps:
solving an upper distribution network operation optimization model by adopting a mixed integer random planning method to obtain an optimal operation plan;
and transmitting the optimal operation plan to an upper-layer optimization model as the operation setting of the upper-layer optimization model.
7. The power distribution network double-layer optimal scheduling method considering wind and solar uncertainty according to claim 6, wherein the optimal operation plan comprises scheduling values of wind power photovoltaic and capacitor bank reactive power output and energy storage active power output.
8. The power distribution network double-layer optimization scheduling method considering wind-solar uncertainty according to claim 1, wherein a Nash equilibrium theory in a game theory is adopted to solve the double-layer optimization model, so that the double-layer optimization model reaches a stable state, and an optimal scheduling plan and an optimal dynamic grid structure topology of the power distribution network are obtained.
9. The power distribution network double-layer optimization scheduling method considering wind-solar uncertainty according to claim 8, wherein solving the double-layer optimization model by using a Nash equilibrium theory in a game theory specifically comprises: and judging whether the iteration process is converged or not based on Nash equilibrium conditions, and if so, outputting an optimal scheduling plan and an optimal dynamic network frame topology.
CN202210435130.2A 2022-04-24 2022-04-24 Power distribution network double-layer optimization scheduling method considering wind and light uncertainty Pending CN114825348A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115378047A (en) * 2022-08-09 2022-11-22 国网江苏省电力有限公司淮安供电分公司 Power distribution system operation optimization method and system based on artificial bee colony and computer equipment

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115378047A (en) * 2022-08-09 2022-11-22 国网江苏省电力有限公司淮安供电分公司 Power distribution system operation optimization method and system based on artificial bee colony and computer equipment

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