CN112396304A - Division strategy research method for regional autonomous power grid in active power distribution network - Google Patents

Division strategy research method for regional autonomous power grid in active power distribution network Download PDF

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CN112396304A
CN112396304A CN202011239905.6A CN202011239905A CN112396304A CN 112396304 A CN112396304 A CN 112396304A CN 202011239905 A CN202011239905 A CN 202011239905A CN 112396304 A CN112396304 A CN 112396304A
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杨梓俊
荆江平
胡伟
陈康
吴奕
夏艺
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State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a division strategy research method of a regional autonomous power grid in an active power distribution network, which specifically comprises the following steps: inputting basic data and constructing a power output model of each element in the power distribution network system; constructing a minimized objective function for reducing interaction between regional autonomous power grids; according to the condition of successful island operation of the regional autonomous power grid, constructing an objective function which maximizes the successful operation capability; constructing an optimal division model of the regional autonomous power grid in the power distribution network according to the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grid; and a backtracking search optimization algorithm is provided as a solving algorithm for optimizing the division boundary of the regional autonomous power grid. The invention considers the randomness and uncertainty of wind, light and other output, formulates the optimization division strategy of the regional autonomous power grid in the power distribution network, determines the optimal virtual boundary of each regional autonomous power grid, and realizes the safe and reliable operation and flexible control of the power distribution network.

Description

Division strategy research method for regional autonomous power grid in active power distribution network
Technical Field
The invention relates to the field of optimal division of a power distribution network, in particular to a division strategy research method of a regional autonomous power distribution network in an active power distribution network.
Background
The sustainable development of the economic society cannot be separated from sufficient energy supply, and a power distribution network is used as an important part of power transmission of a power grid, so that new challenges in the aspects of safety, reliability, power quality and the like are faced. In recent years, new energy, distributed power generation, intelligent micro-grids and the like are rapidly developed, and the technology of regional autonomous grids is applied to improve the operation reliability and flexibility of a power distribution network in an economic and efficient mode, so that the technology is regarded as one of ideal solutions.
The regional autonomous power grid is a small power grid comprising multiple energy sources and loads, is a special component of the power distribution network, can be quickly isolated and independently operated when a large power grid connected with the regional autonomous power grid is in an accident, and has the important characteristic of being beneficial to enhancing the reliable operation capacity of the power distribution network.
At present, the design, control and operation of regional autonomous power grids still have challenges, so the invention provides a division strategy research method of regional autonomous power grids in an active power distribution network, discusses an optimal design method for dividing the active power distribution network into regional autonomous power grids, and provides a comprehensive target which considers the necessary conditions that each regional autonomous power grid has minimum interaction with other regional autonomous power grids during grid-connected operation and can successfully operate in an isolated island mode when needed, maximizes the self-sufficiency and flexibility of the divided regional autonomous power grids, and realizes the safe and reliable operation of the power distribution network.
Disclosure of Invention
The invention aims to provide a division strategy research method of regional autonomous power grids in an active power distribution network, which is characterized in that the necessary conditions that the interaction of each regional autonomous power grid with other regional autonomous power grids is minimum in grid-connected operation after division and the regional autonomous power grids successfully operate in an isolated island mode when needed are considered, an optimized division model is constructed by taking the self-sufficient operation capacity and the successful isolated island operation probability of the maximized regional autonomous power grids as objective functions, and the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grids are added to determine the optimal division of the regional autonomous power grids and improve the reliability and flexibility of a power distribution network system.
The purpose of the invention can be realized by the following technical scheme:
a division strategy research method for a regional autonomous power grid in an active power distribution network specifically comprises the following steps:
step (1.1): basic data of four seasons in one year are input, and a Photovoltaic (PV) and fan (WT) output probability model is constructed;
step (1.2): in order to ensure autonomous operation of the regional autonomous power grid in a grid-connected operation mode, a minimized objective function for reducing interaction between regional autonomous power grids is constructed;
step (1.3): according to the condition of successful island operation of the regional autonomous power grid, constructing an objective function which maximizes the successful operation capability;
step (1.4): constructing an optimal division model of the regional autonomous power grid in the power distribution network according to the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grid;
step (1.5): and a backtracking search optimization algorithm is provided as a solving algorithm for optimizing the division boundary of the regional autonomous power grid.
Further, in the step (1.1), an output model of each element in the power distribution network system is constructed, and the specific steps are as follows:
collecting basic data of four seasons in one year;
setting all power generation equipment in the power distribution system as controllable power sources with constant power, and constructing a probability model of each element in the power distribution system;
the output intermittency and randomness of the distributed Photovoltaic (PV) and the fan (WT) are strong, and the output probability model is specifically described as follows:
the distributed photovoltaic output probability distribution model can be obtained by combining a solar radiation probability distribution model and a photovoltaic cell integral model, the solar illumination intensity is regarded as Beta distribution, and the probability density function is as follows:
Figure BDA0002768091880000031
in the formula, rmaxAnd r is the maximum light intensity and the actual light intensity in the period of time, alpha and Beta are both Beta distribution shape parameters, and can be obtained by the predicted mean value and variance of illumination according to the function between the photovoltaic array output P and the illumination intensity r:
Figure BDA0002768091880000032
the probability density function of the active output of the distributed photovoltaic output can be derived as follows:
Figure BDA0002768091880000033
in the formula, PmaxGrid-connected maximum output for distributed power supplyActive power is output;
the distributed wind power output probability distribution model can be obtained by combining with the wind speed probability distribution model, the wind speed is regarded as Rayleigh distribution, and the probability density function is as follows:
Figure BDA0002768091880000041
in the formula, V is wind speed; sigma is a distribution parameter;
according to a function between the wind turbine output P and the wind speed V:
Figure BDA0002768091880000042
in the formula, vin,vr,vout,wrFor the cut-in wind speed, the rated wind speed, the cut-out wind speed and the rated power (output power at the rated wind speed), respectively, the probability density function of the output active power of the distributed wind turbine generator can be derived as follows:
Figure BDA0002768091880000043
in the formula, Delta is a Dirac Delta function introduced for solving the discontinuity of the wind power probability density function;
the reactive source device is considered to provide constant reactive power; the energy storage device is considered a generator during peak periods of electricity usage (i.e., discharge periods) and a load during off-peak periods (i.e., charge periods);
step three: dividing the probability density function into different states corresponding to different probabilities deltastate，δstateThe value of (c) can be obtained by evaluation.
Further, in the step (1.2), the objective function of autonomous operation of the regional autonomous grid in the grid-connected operation mode aims to reduce the interaction between the divided regional autonomous grids during the grid-connected operation mode to the maximum extent, and the objective function is minimized by the interaction between the regional autonomous grids, that is, the active (P) and reactive (Q) power flows in the lines connecting the regional autonomous grids, which is a key step toward autonomous operation, and the specific description is as follows:
MinF1＝K1×Pnorm+K2×Qnorm
(9)
0≤K1,K2≤1,K1+K2＝1
(10)
in the formula, Pnorm、QnormNormalized active power and reactive power on interconnection lines between regional autonomous power grids can be calculated through a probabilistic power flow algorithm, and a parameter K is set according to the requirements of the regional autonomous power grids1、K2；
Figure BDA0002768091880000051
In the formula, NstatesIs the total number of states in the selected time period;
Figure BDA0002768091880000061
and
Figure BDA0002768091880000062
respectively the active power and the reactive power on the feeder line between the two regional autonomous power grids in the state i; deltaiProbability of occurrence of state i; n is a radical ofmicrogridsFor the number of regional autonomous grids into which the distribution network is split, by Nmicrogrids-1 for normalization.
Further, in the step (1.3), whether the divided regional autonomous power grid can successfully operate in the island operation mode is judged, which mainly depends on whether there are enough active and reactive resources to support important loads in the regional autonomous power grid, and whether the frequency and voltage of each line in the regional autonomous power grid can be maintained within an acceptable deviation range. Another basic requirement is that more than 60% of the power output within the regional autonomous grid needs to come from controllable DGs, as follows:
PG≥PC
(13)
QG≥QC
(14)
Pbio≥0.6×PDGs
(15)
Figure BDA0002768091880000063
in the formula, PGAnd PCActive power, Q, generated and consumed in the regional autonomous grid, respectivelyGAnd QCReactive power, P, respectively, generated and consumed in the regional autonomous gridbioActive power, P, generated for all power supplies within a regional autonomous gridDGsActive power generated by all DGs in the regional autonomous power grid; viFor the line voltage values in the regional autonomous grid in the i-th state,
Figure BDA0002768091880000064
and
Figure BDA0002768091880000065
respectively is a voltage lower limit value and an upper limit value;
based on the method, an index IND for measuring whether each regional autonomous power grid can successfully operate is providedMGFor a power distribution network system containing a multi-region autonomous power grid, a target construction objective function F is constructed by weighting and maximizing successful operation indexes of all regional autonomous power grids2The following are specifically mentioned:
Figure BDA0002768091880000071
in the formula, INDMGiThe method comprises the following steps of (1) indicating a successful island operation index of a single regional autonomous power grid in the ith state; deltaiProbability of occurrence of state i; n is a radical ofstatesIs the total number of states in the selected time period; NoLjRepresenting the total number of load nodes in the jth regional autonomous power grid; n is a radical ofmicrogridsThe distribution network is split into the number of regional autonomous grids.
Further, in the step (1.4), considering that dividing the power distribution network into regional autonomous power grids contributes to improving the power supply reliability and flexibility of the power distribution network, constructing a regional autonomous power grid optimized division model in the power distribution network, realizing autonomous operation of each regional autonomous power grid in a grid-connected mode, and successfully entering an island operation mode when an accident occurs, the specific description is as follows:
the method aims at improving the autonomous operation capability and the island operation capability of the regional autonomous power grid to the maximum extent and combines an objective function F1And an objective function F2Constructing a final objective function F3The method comprises the following steps:
MinF3＝a×F1+b×(1-F2)
(20)
0≤a,b≤1,a+b＝1
(21)
in the formula, weight parameters a and b are set according to the requirements of the regional autonomous power grid;
the regional autonomous power grid voltage constraint, the line capacity constraint and the energy storage power constraint described by the model are specifically described as follows:
1) voltage constraint:
all line voltages in the regional autonomous grid should be kept within a specified range.
|Vmin|≤|Vk-i|≤|Vmax|,k≠1
(22)
In the formula, Vk-iThe voltage amplitude of the line k in the state i; vmin、VmaxRespectively is a voltage lower limit value and an upper limit value;
2) line capacity constraint:
the power flow of each line in the regional autonomous power grid is limited by the maximum capacity to ensure that overload does not occur:
|Iline|≤|Imax|
(23)
in the formula IlineIs the line current; i ismaxMaximum current allowed for the line;
3) energy Storage (ESU) power constraint:
for each regional autonomous power grid, the sum of active power generated by the energy storage unit in any state needs to be smaller than the total active load requirement:
Figure BDA0002768091880000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002768091880000082
total active power generated for stored energy;
Figure BDA0002768091880000083
and (4) the total active power consumed by the local autonomous power grid j under the state i.
Further, in the step (1.5), a backtracking search optimization algorithm is used as a solving algorithm for optimizing the division boundary of the regional autonomous power grid, and the control variable is a virtual secant between each node, and the method includes the following steps:
step one, initializing a population matrix P:
Pi,j～U(minj,maxj)
(25)
wherein i is 1,2,3, …, N; j ═ 1,2,3, …, D; n is the population number; d is a problem dimension; u is uniformly distributed; min and max are respectively the minimum value and the maximum value limit of the virtual secant variable;
step two, randomly generating a historical population matrix oldP of the virtual secant:
at the beginning of each iteration, the oldP matrix is randomly arranged, and the initial history population matrix oldP is defined as follows:
oldPi,j～U(minj,maxj)
(26)
step three, arranging the matrix oldP:
after determining oldP, the order of the individuals in oldP is randomly changed using the following equation:
oldP＝permuting(oldP)
(27)
wherein, the permatng function is a random transformation function;
step four, mutation crossing process:
the matrix oldP is used to evaluate the search steering matrix (oldP-P). From experience with the previous iterations, each iteration generates a new solution set population T, the initial form of the test population is generated according to the following equation:
T＝P+F×map×(oldP-P)
(28)
wherein the variable F controls the amplitude of the search steering matrix (oldP-P); map is a randomly generated binary integer matrix, the size of the matrix is (N multiplied by D), and the matrix map determines elements to be operated in T and is responsible for a crossing process;
step five, performing boundary check:
some individuals in the trial population obtained at the end of the mutation crossover process may overflow the allowed search space limit;
and step six, selecting the global minimum value according to the minimized target, namely obtaining the virtual boundary of each regional autonomous power grid, and finishing the optimized division.
The invention has the beneficial effects that:
1. the division strategy research method provided by the invention considers the necessary conditions that the interaction of each regional autonomous power grid with other regional autonomous power grids is minimum in grid-connected operation after division and the successful island operation is required, an optimized division model is constructed by taking the self-sufficient operation capability and the successful island operation probability of the maximized regional autonomous power grid as objective functions, the regional autonomous power grid voltage constraint, the line capacity constraint and the energy storage power constraint are added, the optimal partition of the regional autonomous power grid is determined, and the reliability and the flexibility of a power distribution network system are improved;
2. the division strategy research method has the characteristics of considering the influence of the regional autonomous power grid on the reliable operation capacity of the power distribution network and realizing the partition control of the power distribution network.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a partitioning strategy research method of a regional autonomous power grid in an active power distribution network according to the present invention;
FIG. 2 is a flowchart of a backtracking search optimization algorithm of the present invention;
FIG. 3 is a schematic diagram of a PG & E69 node power distribution system configuration in an embodiment of the present invention;
FIG. 4 is a schematic diagram of the installation location and capacity of a distributed power supply in an embodiment of the invention;
FIG. 5 is a schematic diagram of a regional autonomous power grid partitioning design optimized by a regional autonomous power grid partitioning strategy research method in an active power distribution network according to the present invention;
fig. 6 is a schematic diagram of a regional autonomous power grid division design obtained by optimization of the regional autonomous power grid division strategy research method in the active power distribution network.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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.
A division strategy research method of a regional autonomous power grid in an active power distribution network is disclosed, as shown in FIG. 1, and comprises the following steps:
step (1.1): basic data of four seasons in one year are input, and output probability models of Photovoltaic (PV), Wind Turbine (WT) and the like are constructed:
the method comprises the following specific steps of constructing a power output model of each element in a power distribution network system:
step one, collecting basic data of four seasons in one year.
And step two, assuming that all power generation equipment in the power distribution system are regarded as controllable power sources with constant power, and constructing a probability model of each element in the power distribution system.
The distributed photovoltaic and wind power output is intermittent and has strong randomness, and the output probability model is specifically described as follows:
the distributed photovoltaic output probability distribution model can be obtained by combining the solar radiation probability distribution model and the photovoltaic cell integral model.
The solar illumination intensity can be approximately regarded as Beta distribution, and the probability density function is as follows:
Figure BDA0002768091880000121
in the formula, rmaxAnd r is the maximum light intensity and the actual light intensity in the period of time, and both alpha and Beta are Beta distribution shape parameters which can be obtained by the predicted illumination mean value and variance.
According to a function between the photovoltaic array output P and the illumination intensity r:
Figure BDA0002768091880000122
the probability density function of the active output of the distributed photovoltaic output can be derived as follows:
Figure BDA0002768091880000123
in the formula, PmaxAnd outputting the active power for the maximum grid connection of the distributed power supply.
The distributed wind power output probability distribution model can be obtained by combining with the wind speed probability distribution model. Wind speed can be approximated as a Rayleigh distribution with a probability density function as follows:
Figure BDA0002768091880000124
in the formula, V is wind speed; σ is a distribution parameter.
According to a function between the wind turbine output P and the wind speed V:
Figure BDA0002768091880000131
in the formula, vin,vr,vout,wrCut-in wind speed, rated wind speed, cut-out wind speed, and rated power (output power at rated wind speed) are provided.
The probability density function of the output active power of the distributed wind turbine generator can be derived as follows:
Figure BDA0002768091880000132
in the formula, δ is a Dirac Delta function introduced to solve the discontinuity of the wind power probability density function.
The reactive source device is considered to provide constant reactive power; the energy storage device is considered a generator during peak periods of power usage (i.e., discharge periods) and a load during off-peak periods (i.e., charge periods).
Step three, dividing the probability density function into different states, wherein the different states correspond to different probabilities deltastate，δstateThe value of (c) can be obtained by evaluation.
Step (1.2): in order to ensure autonomous operation of the regional autonomous power grid in a grid-connected operation mode, a minimized objective function for reducing interaction between regional autonomous power grids is constructed: the objective function of autonomous operation of an area autonomous power grid in a grid-connected operation mode aims to reduce the mutual influence among divided area autonomous power grids during the grid-connected operation mode to the maximum extent, and the objective function is implemented by minimizing active (P) and reactive (Q) power flows in a line connecting the area autonomous power grids through the interaction among the area autonomous power grids, which is a key step taken towards the autonomous operation, and the specific description is as follows:
MinF1＝K1×Pnorm+K2×Qnorm
(9)
0≤K1,K2≤1,K1+K2＝1
(10)
in the formula, Pnorm、QnormThe normalized active power and the normalized reactive power on the interconnection lines between the regional autonomous power grids can be calculated through a probabilistic power flow algorithm. Setting parameter K according to regional autonomous power grid requirements1、K2。
Figure BDA0002768091880000141
In the formula, NstatesIs the total number of states in the selected time period;
Figure BDA0002768091880000142
and
Figure BDA0002768091880000143
respectively the active power and the reactive power on the feeder line between the two regional autonomous power grids in the state i; deltaiProbability of occurrence of state i; n is a radical ofmicrogridsFor the number of regional autonomous grids into which the distribution network is split, by Nmicrogrids-1 for normalization.
Step (1.3): according to the condition of successful island operation of the regional autonomous power grid, constructing an objective function which maximizes the successful operation capacity:
and judging whether the divided regional autonomous power grid can successfully operate in an island operation mode or not, wherein the judgment is mainly based on whether enough active and reactive resources are available for supporting important loads in the regional autonomous power grid and whether the frequency and the voltage of each line in the regional autonomous power grid can be maintained within an acceptable deviation range or not. Another basic requirement is that more than 60% of the power output within the regional autonomous grid needs to come from controllable DGs, as follows:
PG≥PC
(13)
QG≥QC
(14)
Pbio≥0.6×PDGs
(15)
Figure BDA0002768091880000151
in the formula, PGAnd PCActive power, Q, generated and consumed in the regional autonomous grid, respectivelyGAnd QCReactive power, P, respectively, generated and consumed in the regional autonomous gridbioActive power, P, generated for all power supplies within a regional autonomous gridDGsActive power generated by all DGs in the regional autonomous power grid; viFor the line voltage values in the regional autonomous grid in the i-th state,
Figure BDA0002768091880000152
and
Figure BDA0002768091880000153
respectively, a voltage lower limit value and an upper limit value.
Based on the method, an index IND for measuring whether each regional autonomous power grid can successfully operate is providedMGFor a power distribution network system containing a multi-region autonomous power grid, a target construction objective function F is constructed by weighting and maximizing successful operation indexes of all regional autonomous power grids2The following are specifically mentioned:
Figure BDA0002768091880000161
in the formula, INDMGiThe method comprises the following steps of (1) indicating a successful island operation index of a single regional autonomous power grid in the ith state; deltaiProbability of occurrence of state i; n is a radical ofstatesFor a selected period of timeThe total number of internal states; NoLjRepresenting the total number of load nodes in the jth regional autonomous power grid; n is a radical ofmicrogridsNumber of regional autonomous grids split for a power distribution network
Step (1.4): according to the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grid, constructing a regional autonomous power grid optimal division model in the power distribution network:
the method comprises the following steps of considering that the distribution network is divided into regional autonomous power networks, which is beneficial to improving the power supply reliability and flexibility of the distribution network, constructing a regional autonomous power network optimization division model in the distribution network, realizing autonomous operation of each regional autonomous power network in a grid-connected mode, and successfully entering an island operation mode when an accident occurs, wherein the specific description is as follows:
the method aims at improving the autonomous operation capability and the island operation capability of the regional autonomous power grid to the maximum extent and combines an objective function F1And an objective function F2Constructing a final objective function F3The method comprises the following steps:
MinF3＝a×F1+b×(1-F2) (20)
0≤a,b≤1,a+b＝1 (21)
in the formula, weight parameters a and b are set according to the requirements of the regional autonomous power grid.
The regional autonomous power grid voltage constraint, the line capacity constraint and the energy storage power constraint described by the model are specifically described as follows: 1) voltage constraint:
all line voltages in the regional autonomous grid should be kept within a specified range.
|Vmin|≤|Vk-i|≤|Vmax|,k≠1 (22)
In the formula, Vk-iThe voltage amplitude of the line k in the state i; vmin、VmaxRespectively, a voltage lower limit value and an upper limit value.
2) Line capacity constraint:
the power flow of each line in the regional autonomous grid is limited by the maximum capacity to ensure that no overload occurs.
|Iline|≤|Imax| (23)
In the formula IlineIs the line current; i ismaxThe maximum current allowed for the line.
3) Energy Storage (ESU) power constraint:
for each regional autonomous power grid, the sum of active power generated by the energy storage unit in any state needs to be smaller than the total active load demand.
Figure BDA0002768091880000171
In the formula (I), the compound is shown in the specification,
Figure BDA0002768091880000172
total active power generated for stored energy;
Figure BDA0002768091880000173
and (4) the total active power consumed by the local autonomous power grid j under the state i.
Step (1.5): a backtracking search optimization algorithm is provided as a solving algorithm for optimizing the division boundary of the regional autonomous power grid:
the backtracking search optimization algorithm is used as a solving algorithm for optimizing the division boundary of the regional autonomous power grid, and the control variable is a virtual secant between each node, and the backtracking search optimization algorithm comprises the following steps:
step one, initializing a population matrix P:
Pi,j～U(minj,maxj) (25)
wherein i is 1,2,3, …, N; j ═ 1,2,3, …, D; n is the population number; d is a problem dimension; u is uniformly distributed; min and max are respectively the minimum value and the maximum value limit of the virtual secant variable.
And step two, randomly generating a historical population matrix oldP of the virtual secant.
At the beginning of each iteration, the oldP matrix is randomly ordered. The initial historical population matrix oldP is defined as follows:
oldPi,j～U(minj,maxj) (26)
step three, the matrix oldP is arranged.
After determining oldP, the order of the individuals in oldP is randomly changed using the following equation:
oldP＝permuting(oldP) (27)
in the formula, the permatng function is a random transformation function.
Step four, mutation crossing process.
The matrix oldP is used to evaluate the search steering matrix (oldP-P). From experience with the previous iterations, a new solution set population T is generated for each iteration. Initial forms of the test population were generated according to the following formula:
T＝P+F×map×(oldP-P) (28)
wherein the variable F controls the amplitude of the search steering matrix (oldP-P); map is a randomly generated binary integer matrix with the size of (N × D), and the matrix map determines the elements to be operated in T, which is responsible for the crossover process.
And step five, carrying out boundary inspection.
Some individuals in the trial population obtained at the end of the mutation crossover process may spill the allowable search space limit.
And step six, selecting the global minimum value according to the minimized target, namely obtaining the virtual boundary of each regional autonomous power grid, and finishing the optimized division.
Example 1
The invention provides a division strategy research method of a regional autonomous power grid in an active power distribution network, which is a mixed integer nonlinear programming model, and provides a backtracking search optimization algorithm as a solving algorithm of a regional autonomous power grid virtual boundary, wherein a solving flow chart of the backtracking search optimization algorithm is shown in figure 2.
The method takes the necessary conditions that the interaction between each regional autonomous power grid and other regional autonomous power grids is minimum in grid-connected operation after division and the successful island operation is performed when needed into consideration, an optimized division model is constructed by taking the self-sufficient operation capability and the successful island operation probability of the regional autonomous power grid as objective functions to be maximized, the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grid are added, the optimal partition of the regional autonomous power grid is determined, and the reliability and the flexibility of a power distribution network system are improved.
In an embodiment of the present invention, the PG & E69 node power distribution system shown in fig. 3 is used. In the system, the energy production equipment comprises a gas turbine, a photovoltaic, a fan and the like, the energy storage and conversion equipment mainly comprises a storage battery, and the capacity and the access position of each equipment are shown in figure 4. The following explains the simulation results of the embodiment of the present invention.
The parameters in the objective function F3 are set to have values of a-b-0.5, the power distribution network is divided into a different number of regional autonomous power grids, and the optimal virtual boundary and each objective function value are shown in fig. 5. As the number of divided regions increases, the total objective function value increases. Fig. 6 specifically shows a partition boundary condition designed when the power distribution network is divided into 9 regional autonomous power grids.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (6)

1. A division strategy research method for a regional autonomous power grid in an active power distribution network is characterized by specifically comprising the following steps:
step (1.1): basic data of four seasons in one year are input, and a Photovoltaic (PV) and fan (WT) output probability model is constructed;
step (1.2): in order to ensure autonomous operation of the regional autonomous power grid in a grid-connected operation mode, a minimized objective function for reducing interaction between regional autonomous power grids is constructed;
step (1.3): according to the condition of successful island operation of the regional autonomous power grid, constructing an objective function which maximizes the successful operation capability;
step (1.4): constructing an optimal division model of the regional autonomous power grid in the power distribution network according to the voltage constraint, the line capacity constraint and the energy storage power constraint of the regional autonomous power grid;
step (1.5): and a backtracking search optimization algorithm is provided as a solving algorithm for optimizing the division boundary of the regional autonomous power grid.
2. The method for researching the partitioning strategy of the regional autonomous power grid in the active power distribution network according to claim 1, wherein in the step (1.1), an output model of each element in the power distribution network system is constructed, and the specific steps are as follows:
collecting basic data of four seasons in one year;
setting all power generation equipment in the power distribution system as controllable power sources with constant power, and constructing a probability model of each element in the power distribution system;
the output intermittency and randomness of the distributed Photovoltaic (PV) and the fan (WT) are strong, and the output probability model is specifically described as follows:
the distributed photovoltaic output probability distribution model can be obtained by combining a solar radiation probability distribution model and a photovoltaic cell integral model, the solar illumination intensity is regarded as Beta distribution, and the probability density function is as follows:
Figure FDA0002768091870000021
in the formula, rmaxAnd r is the maximum light intensity and the actual light intensity in the period, respectively, and both alpha and beta are Beta, the shape parameters of the distribution can be obtained by the predicted mean value and variance of the illumination, and according to the function between the photovoltaic array output P and the illumination intensity r:
Figure FDA0002768091870000022
the probability density function of the active output of the distributed photovoltaic output can be derived as follows:
Figure FDA0002768091870000023
in the formula, PmaxOutputting active power for the maximum grid connection of the distributed power supply;
the distributed wind power output probability distribution model can be obtained by combining with the wind speed probability distribution model, the wind speed is regarded as Rayleigh distribution, and the probability density function is as follows:
Figure FDA0002768091870000024
in the formula, V is wind speed; sigma is a distribution parameter;
according to a function between the wind turbine output P and the wind speed V:
Figure FDA0002768091870000025
Figure FDA0002768091870000026
Figure FDA0002768091870000027
in the formula, vin,vr,vout,wrFor the cut-in wind speed, the rated wind speed, the cut-out wind speed and the rated power (output power at the rated wind speed), respectively, the probability density function of the output active power of the distributed wind turbine generator can be derived as follows:
Figure FDA0002768091870000031
in the formula, Delta is a Dirac Delta function introduced for solving the discontinuity of the wind power probability density function;
the reactive source device is considered to provide constant reactive power; the energy storage device is considered a generator during peak periods of electricity usage (i.e., discharge periods) and a load during off-peak periods (i.e., charge periods);
step three: dividing the probability density function into different states corresponding to different probabilities deltastate，δstateThe value of (c) can be obtained by evaluation.
3. The method as claimed in claim 1, wherein in the step (1.2), the objective function of autonomous operation of the regional autonomous power grid in the grid-connected operation mode is designed to minimize the interaction between the divided regional autonomous power grids during the grid-connected operation mode, and the objective function is a key step toward autonomous operation, which is implemented by minimizing active (P) and reactive (Q) power flows in lines connecting the regional autonomous power grids through the interaction between the regional autonomous power grids, and is specifically described as follows:
MinF1＝K1×Pnorm+K2×Qnorm (9)
0≤K1,K2≤1,K1+K2＝1 (10)
in the formula, Pnorm、QnormNormalized active power and reactive power on interconnection lines between regional autonomous power grids can be calculated through a probabilistic power flow algorithm, and a parameter K is set according to the requirements of the regional autonomous power grids1、K2；
Figure FDA0002768091870000032
Figure FDA0002768091870000041
In the formula, NstatesIs the total number of states in the selected time period;
Figure FDA0002768091870000042
and
Figure FDA0002768091870000043
respectively the active power and the reactive power on the feeder line between the two regional autonomous power grids in the state i; deltaiProbability of occurrence of state i; n is a radical ofmicrogridsFor the number of regional autonomous grids into which the distribution network is split, by Nmicrogrids-1 for normalization.
4. The method according to claim 1, wherein in step (1.3), it is determined whether the partitioned regional autonomous power grid can be successfully operated in an islanding operation mode, mainly depending on whether there are enough active and reactive resources to support important loads in the regional autonomous power grid, and whether the frequency and voltage of each line in the regional autonomous power grid can be maintained within an acceptable deviation range. Another basic requirement is that more than 60% of the power output within the regional autonomous grid needs to come from controllable DGs, as follows:
PG≥PC (13)
QG≥QC (14)
Pbio≥0.6×PDGs (15)
Figure FDA0002768091870000044
in the formula, PGAnd PCActive power, Q, generated and consumed in the regional autonomous grid, respectivelyGAnd QCReactive power, P, respectively, generated and consumed in the regional autonomous gridbioActive power, P, generated for all power supplies within a regional autonomous gridDGsActive power generated by all DGs in the regional autonomous power grid; viFor the line voltage values in the regional autonomous grid in the i-th state,
Figure FDA0002768091870000045
and
Figure FDA0002768091870000046
respectively is a voltage lower limit value and an upper limit value;
based on the method, an index IND for measuring whether each regional autonomous power grid can successfully operate is providedMGFor a power distribution network system containing a multi-region autonomous power grid, a target construction objective function F is constructed by weighting and maximizing successful operation indexes of all regional autonomous power grids2The following are specifically mentioned:
Figure FDA0002768091870000047
Figure FDA0002768091870000051
Figure FDA0002768091870000052
in the formula, INDMGiThe method comprises the following steps of (1) indicating a successful island operation index of a single regional autonomous power grid in the ith state; deltaiProbability of occurrence of state i; n is a radical ofstatesIs the total number of states in the selected time period; NoLjDenotes the j (th)The total number of load nodes in the autonomous power grid of each area; n is a radical ofmicrogridsThe distribution network is split into the number of regional autonomous grids.
5. The division strategy research method of the regional autonomous power grid in the active power distribution network according to claim 1, wherein in the step (1.4), the division of the power distribution network into the regional autonomous power grid is considered to help to improve the power supply reliability and flexibility of the power distribution network, an optimized division model of the regional autonomous power grid in the power distribution network is constructed, autonomous operation of each regional autonomous power grid in a grid-connected mode is realized, and an island operation mode is successfully entered when an accident occurs, and the specific description is as follows:
the method aims at improving the autonomous operation capability and the island operation capability of the regional autonomous power grid to the maximum extent and combines an objective function F1And an objective function F2Constructing a final objective function F3The method comprises the following steps:
MinF3＝a×F1+b×(1-F2) (20)
0≤a,b≤1,a+b＝1 (21)
in the formula, weight parameters a and b are set according to the requirements of the regional autonomous power grid;
the regional autonomous power grid voltage constraint, the line capacity constraint and the energy storage power constraint described by the model are specifically described as follows:
1) voltage constraint:
all line voltages in the regional autonomous grid should be kept within a specified range.
|Vmin|≤|Vk-i|≤|Vmax|,k≠1 (22)
In the formula, Vk-iThe voltage amplitude of the line k in the state i; vmin、VmaxRespectively is a voltage lower limit value and an upper limit value;
2) line capacity constraint:
the power flow of each line in the regional autonomous power grid is limited by the maximum capacity to ensure that overload does not occur:
|Iline|≤|Imax| (23)
in the formula IlineIs the line current; i ismaxMaximum current allowed for the line;
3) energy Storage (ESU) power constraint:
for each regional autonomous power grid, the sum of active power generated by the energy storage unit in any state needs to be smaller than the total active load requirement:
Figure FDA0002768091870000061
in the formula (I), the compound is shown in the specification,
Figure FDA0002768091870000062
total active power generated for stored energy;
Figure FDA0002768091870000063
and (4) the total active power consumed by the local autonomous power grid j under the state i.
6. The method for researching the division strategy of the regional autonomous power grid in the active power distribution network according to claim 1, wherein in the step (1.5), a backtracking search optimization algorithm is used as a solving algorithm for optimizing the division boundary of the regional autonomous power grid, and a control variable is a virtual secant between each node, comprising the following steps:
step one, initializing a population matrix P:
Pi,j～U(minj,maxj) (25)
wherein i is 1,2,3, …, N; j ═ 1,2,3, …, D; n is the population number; d is a problem dimension; u is uniformly distributed; min and max are respectively the minimum value and the maximum value limit of the virtual secant variable;
step two, randomly generating a historical population matrix oldP of the virtual secant:
at the beginning of each iteration, the oldP matrix is randomly arranged, and the initial history population matrix oldP is defined as follows:
oldPi,j～U(minj,maxj) (26)
step three, arranging the matrix oldP:
after determining oldP, the order of the individuals in oldP is randomly changed using the following equation:
oldP＝permuting(oldP) (27)
wherein, the permatng function is a random transformation function;
step four, mutation crossing process:
the matrix oldP is used to evaluate the search steering matrix (oldP-P). From experience with the previous iterations, each iteration generates a new solution set population T, the initial form of the test population is generated according to the following equation:
T＝P+F×map×(oldP-P) (28)
wherein the variable F controls the amplitude of the search steering matrix (oldP-P); map is a randomly generated binary integer matrix, the size of the matrix is (N multiplied by D), and the matrix map determines elements to be operated in T and is responsible for a crossing process;
step five, performing boundary check:
some individuals in the trial population obtained at the end of the mutation crossover process may overflow the allowed search space limit;
and step six, selecting the global minimum value according to the minimized target, namely obtaining the virtual boundary of each regional autonomous power grid, and finishing the optimized division.
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