CN110570051B - Benders-Pso algorithm-based distributed energy storage robustness site selection system and method - Google Patents

Benders-Pso algorithm-based distributed energy storage robustness site selection system and method Download PDF

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CN110570051B
CN110570051B CN201910922509.4A CN201910922509A CN110570051B CN 110570051 B CN110570051 B CN 110570051B CN 201910922509 A CN201910922509 A CN 201910922509A CN 110570051 B CN110570051 B CN 110570051B
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杨珺
王奎文
孙秋野
刘鑫蕊
杨东升
王迎春
张化光
黄博南
刘振伟
会国涛
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Abstract

The invention discloses a Benders-Pso algorithm-based distributed energy storage robustness site selection system and method, and belongs to the technical field of intelligent power grids. The system comprises four modules of power distribution network information acquisition, modeling, optimization and analytic output, a system implementation method is also disclosed, the uncertainty of wind power is considered, the objective function and the constraint condition of a distributed energy storage robustness site selection system model are determined according to the worst wind power output, the distributed energy storage robustness site selection system model based on the Pso algorithm is optimized by adopting the Benders algorithm, the problem is divided into two problems of robustness optimization and distributed energy storage site selection, the problem of local convergence is avoided, the two problems can be optimized by ensuring the final result, and the performance of a power grid is improved.

Description

Distributed energy storage robustness addressing system and method based on Benders-Pso algorithm
Technical Field
The invention relates to the technical field of intelligent power grids, in particular to a Benders-Pso algorithm-based distributed energy storage robustness site selection system and method.
Background
The distributed power generation technology using renewable energy as primary energy adapts to the requirements of human in the 21 st century for developing low-carbon economy and realizing sustainable development, so that great attention is drawn in the global scope, and the access of a distributed power supply enables a power distribution system to be changed from a passive network to an active network. The uncertainty of the distributed power supply has a large influence on the stability of the power grid. Therefore, energy storage is configured to be a hot solution for absorbing wind power uncertainty.
At present, most methods for optimizing energy storage configuration are to use the existing load and wind power generation as a known item, optimize and solve the existing load and wind power generation after determining the numerical values of the existing load and wind power generation, but the consideration on wind power uncertainty is very little, so that the studied example is not applicable under the wind power limit working condition. The solving method is also difficult to solve the larger problem because of the local convergence problem of the algorithm. An algorithm with strong convergence performance is provided to solve the problems of large size and strong adaptability, which is an inevitable direction of research and development.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a distributed energy storage robustness address selection system and method based on a Benders-Pso algorithm.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a distributed energy storage robustness addressing system based on a Benders-Pso algorithm is shown in figure 1, and comprises: the power distribution network information acquisition module, the modeling module, the optimization module and the analysis output module.
The power distribution network information acquisition module is used for acquiring and transmitting network parameter information of the current power distribution network, and comprises:
(1) The distribution network information collector is used for collecting network initial information of the current distribution network, and the network initial information comprises distribution network node information, branch information, position information and output information of the distributed power supply;
(2) And the relay transmitter is used for transmitting the information acquired by the power distribution network information acquisition device to the modeling module.
The modeling module is used for constructing a planning model of the Benders-Pso algorithm, and comprises the following steps:
(1) The information preprocessing unit is used for receiving network initial information and preprocessing the initial information into a matrix form required by model construction;
(2) And the model construction unit is used for determining the adaptive value function and each constraint condition so as to construct a planning model of the Benders-Pso algorithm.
The optimization module is used for optimizing the network parameter information by adopting Benders-Pso to obtain an optimization result, namely an optimal energy storage position, and comprises the following steps:
(1) The Benders algorithm unit is used for dividing the optimization problem into a robustness optimization problem and a distributed energy storage site selection problem, and ensuring that the final result is obtained to optimize the two problems, and comprises the following steps: the Benders initialization subunit, the Benders transformation subunit and the Benders judgment subunit;
the Benders initializing subunit is used for dividing the problems in the distributed energy storage robustness addressing system into a main problem part and a sub problem part and decoupling the sub problems;
the Benders transformation subunit is used for generating and constraining the solved result of the subproblems to be added into the main problem;
and the Benders judging subunit is used for updating the variables and judging whether to carry out circulation or not.
The robustness optimization problem refers to that the worst wind power output is selected in the optimization process, so that the whole system works under the condition, and the system has the characteristic of safe working under any condition; the problem of energy storage site selection refers to that an energy storage unit is configured on a node in a power system.
(2) The Pso algorithm unit is used for calculating the optimal values of the robustness optimization problem and the distributed energy storage addressing problem, and comprises the following steps: a Pso initial setting subunit and a Pso individual optimization subunit;
and the Pso initial setting subunit is used for initializing the initial value of the individual in the Pso algorithm unit.
And the Pso individual optimization subunit updates the position information of the individuals in the population according to the optimization scheme so as to obtain the individuals with higher adaptive values and iterate.
By using the system, the invention also provides an addressing method of the distributed energy storage robustness addressing system based on the Benders-Pso algorithm, the flow of the method is shown in figure 2, and the method comprises the following steps:
step 1: acquiring and transmitting parameter information of a current power distribution network, wherein the parameter information comprises power distribution network node information, branch information, position information of a distributed power supply and output information of the distributed power supply;
and 2, step: determining a target function and constraint conditions of a distributed energy storage robustness addressing system model;
step 2.1: taking the received network parameter information in each time interval as a set, combining all the sets into a large data set, converting the data set into a matrix mode, wherein each column in the matrix is data of one piece of network parameter information at one moment, and each row in the matrix is the number of time intervals;
step 2.2: determining an adaptive value function and various constraint conditions;
step 2.2.1: the fitness function is set to:
Figure GDA0003911061980000021
wherein, C soc For energy storage construction costs, z for energy storage construction locations, f g As a function of the cost of the generator, P g,t Generating electric quantity for each generator in each time interval, G is a set of the generator sets, and T is a set of the time intervals, wherein
Figure GDA0003911061980000022
The meaning is that the minimum value of the output of the generator is maximized by selecting a proper wind power output value;
step 2.2.2: setting the line constraint conditions as follows:
Figure GDA0003911061980000031
B (i,j) (θ i,t -θ j,t )-P (i,j),t ＝0
wherein, P g,t The g output of the generator at the time t is P w,t The power of the fan w at the time t is P (i,j),t The power flowing from line i to line j at time t, P SOC,n,t The magnitude of the energy storage output force P on the node n at the time t L,n,t Is the magnitude of the load on node n at time t, B (i,j) For admittance from line i to line j, θ, corresponding to the DC power flow i,t Is the voltage phase angle of node i;
step 2.2.3: the constraint conditions of the set are set as follows:
Figure GDA0003911061980000032
Figure GDA0003911061980000033
Figure GDA0003911061980000034
Figure GDA0003911061980000035
Figure GDA0003911061980000036
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003911061980000037
a minimum value of power is issued for the generator g,
Figure GDA0003911061980000038
for the generator g to generate the maximum value of power,
Figure GDA0003911061980000039
predicting the output power alpha of the fan w at the moment t w,t Predicting the output deviation, l, for the fan w at time t (i,j) For the line connection state matrix, if the line i is connected to the line j, the corresponding value is 1, otherwise, it is 0,
Figure GDA00039110619800000310
and
Figure GDA00039110619800000311
for the upper and lower limits of the line transmission power,
Figure GDA00039110619800000312
and
Figure GDA00039110619800000313
the upward climbing limit value and the downward climbing limit value processed by the unit g in a period of time;
step 2.2.4: setting the energy storage constraint as:
P soc,min ≤P soc,n,t ≤P soc,max
0≤SOC n,t ≤SOC max
SOC n,t ＝SOC n,t-1 +ΔtP SOC,n,t
wherein, P soc,max And P soc,min Upper and lower limits of output, SOC, for stored energy at node n n,t For storing the charge at time t, SOC max The maximum value of the energy storage charge capacity.
And step 3: optimizing a distributed energy storage robustness addressing system model based on a Pso algorithm by adopting a Benders algorithm, dividing the problem into two problems of robustness optimization and distributed energy storage addressing, and obtaining an optimization result;
step 3.1: the robustness distributed energy storage addressing problem is divided into a main problem and a sub problem,
the main problem objective function is: min C soc z
The sub-problem objective function is:
Figure GDA0003911061980000041
the main and sub-problem constraints are:
Ax≥h-Ez * -Jw
a is an unknown variable corresponding coefficient matrix, x is an unknown variable matrix and comprises the generated energy of a motor in each time interval, the transmission power of a line in each time interval, the phase angle of each node in each time interval and the charged quantity of stored energy in each time interval, h is a constraint constant term matrix, E is an energy storage charged quantity maximum value matrix, J is a wind power output matrix in each time interval, w is a wind motor position matrix, z is an unknown variable corresponding coefficient matrix, and * is an energy storage position matrix;
step 3.2: decoupling the subproblem objective function by using a dual theorem, which is changed into:
max(h-Ez * -Jw) T τ
the constraints are noted as: a. The T τ≤b
Wherein tau is a variable after dual decoupling, b is C soc Transposing;
step 3.3: transmitting the sub-problem objective function and the constraint condition to a Pso algorithm, and solving the problem by using the Pso algorithm, wherein the flow is shown in FIG. 3;
step 3.3.1: initializing various variable values and adaptive value functions, setting the type as a discrete type if the problem is a main problem, and setting the type as a continuous type if the problem is a sub problem; performing random initialization distribution on all particles, setting the number of the particles as n, the current iteration number as k, the initial value of the iteration number as 1, and the maximum iteration number as k max ；
Step 3.3.2: according to the step 2.2.1, the fitness value of the current individual and the maximum fitness values of all the current individuals are obtained through comparison, and each particle is subjected to speed updating:
V i ’＝ωV i +c 1 ×rand×(q ibest -X i )+c 2 ×rand×(g best -X i )
X’ i ＝X i +V i
k＝k+1
wherein, V i Representing the speed, V, of the individual i before updating i ' is aUpdated velocity, ω is the inertial weight coefficient, c 1 And c 2 Are all acceleration factors, rand denotes a random number between (0, 1), q ibest Is the individual optimal position of the individual i, g best For the optimal position of the current group, X i Is the current individual position, X' i Is the updated individual position.
Step 3.3.3: repeating the step 3.3.2 for k = k max When the iteration is stopped;
step 3.3.4: and outputting the position of the particle with the highest fitness, wherein the value of the problem to be solved is the fitness value of the particle with the highest fitness.
Step 3.4: recording the objective function value of the subproblem obtained by solving the pso algorithm as eta;
step 3.5: adding eta and tau obtained by the sub-problem into the main problem, and changing the main objective function and the constraint into:
min cz+η
Figure GDA0003911061980000051
step 3.6: the changed main problem objective function and the changed constraint condition are transmitted to the Pso algorithm of the inner layer, the main problem objective function value takes a negative value as an individual fitness value, and the main problem is solved;
step 3.7: updating the solution z of the main problem to z * Marking the target function value as xi, judging the values of xi and eta, and if eta-xi =0, ending the optimization and outputting a result; if η - ξ ≠ 0, then steps 3.3-3.7 are executed for the next loop.
And 4, step 4: and analyzing and outputting the optimization result.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1. according to the invention, the worst wind power output is selected for optimization, so that energy storage configuration under the limit working condition of the whole line is realized, and the operation safety and stability under normal working conditions are ensured;
2. the optimization problem is divided into a robustness optimization problem and a distributed energy storage site selection problem by adopting a Benders algorithm, the two problems can be optimized by ensuring the final result, and the performance of a power grid is improved;
3. the invention integrates two complementary problems through the Benders algorithm, optimizes the Pso algorithm, finds global optimum, avoids the dilemma of local convergence, has better global convergence and inevitable convergence during iteration, and is suitable for solving the NP-Hard problem.
Drawings
FIG. 1 is a structural diagram of a distributed energy storage robustness addressing system based on a Benders-Pso algorithm of the invention;
FIG. 2 is a flow chart of a distributed energy storage robustness address selection method based on the Benders-Pso algorithm of the invention;
FIG. 3 is a flow chart of the Pso algorithm of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention.
As shown in fig. 1, the system configuration of the present embodiment is as follows.
A distributed energy storage robustness addressing system based on Benders-Pso algorithm comprises: the system comprises a power distribution network information acquisition module, a modeling module, an optimization module and an analysis output module.
The power distribution network information acquisition module is used for acquiring and transmitting network parameter information of the current power distribution network, and comprises the following components:
(1) The power distribution network information acquisition device is used for acquiring network initial information of the current power distribution network, wherein the network initial information comprises power distribution network node information, branch information, position information and output information of the distributed power supply;
(2) And the relay transmitter is used for transmitting the information acquired by the power distribution network information acquisition device to the modeling module.
The modeling module is used for constructing a planning model of the Benders-Pso algorithm, and comprises the following steps:
(1) The information preprocessing unit is used for receiving network initial information and preprocessing the initial information into a matrix form required by model construction;
(2) And the model construction unit is used for determining the adaptive value function and each constraint condition so as to construct a planning model of the Benders-Pso algorithm.
The optimization module is used for optimizing the network parameter information by adopting Benders-Pso to obtain an optimization result, namely an optimal energy storage position, and comprises the following steps:
(1) The Benders algorithm unit is used for dividing the optimization problem into a robustness optimization problem and a distributed energy storage site selection problem, and ensuring that the final result is obtained to optimize the two problems, and comprises the following steps: benders initialization subunit, benders transformation subunit and Benders judgment subunit;
the Benders initialization subunit is used for dividing the problem in the distributed energy storage robustness addressing system into a main problem part and a sub problem part and decoupling the sub problem part;
the Benders transformation subunit is used for generating and constraining the solved result of the subproblems to be added into the main problem;
and the Benders judging subunit is used for updating the variables and judging whether to carry out circulation or not.
The robustness optimization problem refers to that the worst wind power output is selected in the optimization process, so that the whole system works under the condition, and the system has the characteristic of safe work under any condition; the problem of energy storage site selection refers to that an energy storage unit is configured on a node in an electric power system.
(2) The Pso algorithm unit is used for calculating the optimal values of the robustness optimization problem and the distributed energy storage site selection problem, and comprises the following steps: a Pso initial setting subunit and a Pso individual optimization subunit;
and the Pso initial setting subunit is used for initializing the initial value of the individual in the Pso algorithm unit.
And the Pso individual optimization subunit updates the position information of the individuals in the population according to the optimization scheme so as to obtain the individuals with higher adaptive values and iterate.
With the system, the method flow of this embodiment is shown in fig. 2, and includes the following steps:
step 1: acquiring and transmitting parameter information of a current power distribution network, wherein the parameter information comprises power distribution network node information, branch information, position information of a distributed power supply and output information of the distributed power supply;
step 2: determining a target function and constraint conditions of a distributed energy storage robustness addressing system model;
step 2.1: taking the received network parameter information in each time interval as a set, combining all the sets into a large data set, converting the data set into a matrix mode, wherein each column in the matrix is data of one piece of network parameter information at one moment, and each row in the matrix is the number of time intervals;
step 2.2: determining an adaptive value function and various constraint conditions;
step 2.2.1: the fitness function is set to:
Figure GDA0003911061980000071
wherein, C soc For energy storage construction costs, z for energy storage construction locations, f g As a function of the cost of the generator, P g,t Generating electricity for each generator in each time period, G is a set of the generator sets, and T is a set of the time periods, wherein
Figure GDA0003911061980000072
The meaning is that a proper wind power output value is selected to enable the minimum value of the output of the generator to be maximum;
step 2.2.2: setting the line constraint conditions as follows:
Figure GDA0003911061980000073
B (i,j) (θ i,t -θ j,t )-P (i,j),t ＝0
wherein, P g,t G output of the generator at time t, P w,t The power of the fan w at the time t is P (i,j),t For time t by linei power flowing to line j, P SOC,n,t The magnitude of the energy storage output force P on the node n at the time t L,n,t Is the magnitude of the load on node n at time t, B (i,j) For admittance from line i to line j, θ, corresponding to the DC power flow i,t Is the voltage phase angle of node i;
step 2.2.3: the constraint conditions of the set are set as follows:
Figure GDA0003911061980000074
Figure GDA0003911061980000075
Figure GDA0003911061980000076
Figure GDA0003911061980000077
Figure GDA0003911061980000078
wherein the content of the first and second substances,
Figure GDA0003911061980000079
to generate a minimum value of power for the generator g,
Figure GDA00039110619800000710
for the generator g to generate the maximum value of power,
Figure GDA00039110619800000711
predicting the output power alpha of the fan w at the moment t w,t Predicting the output deviation l for the fan w at time t (i,j) For the line connection state matrix, if the line i is connected to the line j, the corresponding value is 1, otherwise, it is 0,
Figure GDA00039110619800000712
and
Figure GDA00039110619800000713
for the upper and lower limits of the transmission power of the line,
Figure GDA00039110619800000714
and
Figure GDA00039110619800000715
the upward climbing limit value and the downward climbing limit value processed by the unit g in a period of time;
step 2.2.4: setting the energy storage constraint as:
P soc,min ≤P soc,n,t ≤P soc,max
0≤SOC n,t ≤SOC max
SOC n,t ＝SOC n,t-1 +ΔtP SOC,n,t
wherein, P soc,max And P soc,min Upper and lower limits of output, SOC, for energy storage at node n n,t For storing the charge at time t, SOC max The maximum value of the energy storage charge.
And step 3: optimizing a distributed energy storage robustness addressing system model based on a Pso algorithm by adopting a Benders algorithm, dividing the problem into two problems of robustness optimization and distributed energy storage addressing, and obtaining an optimization result;
step 3.1: the robustness distributed energy storage addressing problem is divided into a main problem and a sub problem,
the main problem objective function is: min C soc z
The sub-problem objective function is:
Figure GDA0003911061980000081
the main and sub-problem constraints are:
Ax≥h-Ez * -Jw
wherein A is an unknown variable corresponding coefficient matrix, x is an unknown variable matrix including each timeThe method comprises the steps of generating capacity of a segment motor, transmission power of lines in each period, phase angles of nodes in each period and charge quantity of energy storage in each period, h is a constraint constant item matrix, E is an energy storage charge quantity maximum value matrix, J is a wind power output matrix in each period, w is a position matrix of the wind motor, z * Is an energy storage position matrix;
step 3.2: decoupling the subproblem objective function by using a dual theorem, which is changed into:
max(h-Ez * -Jw) T τ
the constraints are noted as: a. The T τ≤b
Wherein, tau is a dual decoupled variable, b is C soc Transposing;
step 3.3: transmitting the sub-problem objective function and the constraint condition to a Pso algorithm, and solving the problem by using the Pso algorithm;
step 3.3.1: initializing variable values and adaptive value functions, setting the type as a discrete type if the problem is a main problem, and setting the type as a continuous type if the problem is a sub problem; performing random initialization distribution on all particles, setting the number of the particles as n, the current iteration number as k, the initial value of the iteration number as 1, and the maximum iteration number as k max In this embodiment, k max Taking 30;
step 3.3.2: according to the step 2.2.1, the fitness value of the current individual and the maximum fitness values of all the current individuals are obtained through comparison, and each particle is subjected to speed updating:
V i ’＝ωV i +c 1 ×rand×(q ibest -X i )+c 2 ×rand×(g best -X i )
X’ i ＝X i +V i
k＝k+1
wherein, V i Representing the speed, V, of the individual i before update i ' is the updated velocity, omega is the inertial weight coefficient, c 1 And c 2 Are all acceleration factors, rand denotes a random number between (0, 1), q ibest For the individual optimal position of the individual i, g best For the optimal position of the current group, X i Is the current oneBody position, X' i Is the updated individual location.
In this example, c 1 Take 0.3,c 2 0.3 is taken, and 0.4 is taken as omega.
Step 3.3.3: repeating the step 3.3.2 for k = k max When the iteration is stopped;
step 3.3.4: and outputting the position of the particle with the highest fitness, wherein the value of the problem to be solved is the fitness value of the particle with the highest fitness.
Step 3.4: recording the objective function value of the subproblem obtained by solving the pso algorithm as eta;
step 3.5: adding eta and tau obtained by the subproblems into the main problem, and changing the main objective function and the constraint into:
min cz+η
Figure GDA0003911061980000091
step 3.6: the changed main problem objective function and the constraint condition are transmitted to a Pso algorithm of an inner layer, a negative value of the main problem objective function value is taken as an individual fitness value, and the main problem is solved;
step 3.7: updating the solution to the main problem z to z * Marking the target function value as xi, judging the values of xi and eta, and if eta-xi =0, ending the optimization and outputting a result; if η - ξ ≠ 0, then steps 3.3-3.7 are executed for the next loop.
And 4, step 4: and analyzing and outputting the optimization result.

Claims (2)

1. A distributed energy storage robustness address selection method based on a Benders-Pso algorithm is characterized by comprising the following steps:
step 1: acquiring and transmitting parameter information of a current power distribution network, wherein the parameter information comprises power distribution network node information, branch information, position information of a distributed power supply and output information of the distributed power supply;
step 2: determining an objective function and constraint conditions of a distributed energy storage robustness addressing system model;
step 2.1: taking the received network parameter information in each time interval as a set, combining all the sets into a large data set, converting the data set into a matrix mode, wherein each column in the matrix is data of one piece of network parameter information at one moment, and each row in the matrix is the number of time intervals;
step 2.2: determining an adaptive value function and various constraint conditions;
step 2.2.1: the fitness function is set to:
Figure FDA0003911061970000011
wherein, C soc For energy storage construction costs, z for energy storage construction locations, f g As a function of the cost of the generator, P g,t Generating electric quantity for each generator in each time interval, G is a set of the generator sets, and T is a set of the time intervals, wherein
Figure FDA0003911061970000012
The meaning is that a proper wind power output value is selected to enable the minimum value of the output of the generator to be maximum;
step 2.2.2: the line constraint conditions are set as follows:
Figure FDA0003911061970000013
B (i,j) (θ i,t -θ j,t )-P (i,j),t ＝0
wherein, P g,t The g output of the generator at the time t is P w,t The power of the fan w at the time t is P (i,j),t The power flowing from line i to line j at time t, P SOC,n,t The magnitude of the energy storage output force P on the node n at the time t L,n,t Is the magnitude of the load on node n at time t, B (i,j) For admittance from line i to line j, θ, corresponding to the DC power flow i,t Is the voltage phase angle of node i;
step 2.2.3: the constraint conditions of the set are set as follows:
Figure FDA0003911061970000014
Figure FDA0003911061970000015
Figure FDA0003911061970000016
Figure FDA0003911061970000017
Figure FDA0003911061970000018
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003911061970000019
a minimum value of power is issued for the generator g,
Figure FDA00039110619700000110
a maximum value of power is generated for the generator g,
Figure FDA00039110619700000111
predicting the output force alpha of the fan w at the moment t w,t Predicting the output deviation l for the fan w at time t (i,j) For the line connection state matrix, if the line i is connected to the line j, the corresponding value is 1, otherwise, it is 0,
Figure FDA0003911061970000021
and
Figure FDA0003911061970000022
for the upper and lower limits of the transmission power of the line,
Figure FDA0003911061970000023
and with
Figure FDA0003911061970000024
The upward climbing limit value and the downward climbing limit value processed by the unit g in a period of time;
step 2.2.4: setting the energy storage constraint as:
P soc,min ≤P soc,n,t ≤P soc,max
0≤SOC n,t ≤SOC max
SOC n,t ＝SOC n,t-1 +ΔtP SOC,n,t
wherein, P soc,max And P soc,min Upper and lower limits of output, SOC, for energy storage at node n n,t For storing the charge at time t, SOC max The maximum value of the energy storage charge;
and 3, step 3: optimizing a distributed energy storage robustness addressing system model based on a Pso algorithm by adopting a Benders algorithm, dividing the problem into two problems of robustness optimization and distributed energy storage addressing, and obtaining an optimization result;
step 3.1: the robustness distributed energy storage addressing problem is divided into a main problem and a sub problem,
the main problem objective function is: min C soc z
The sub-problem objective function is:
Figure FDA0003911061970000025
the main and sub-problem constraints are:
Ax≥h-Ez * -Jw
wherein A is an unknown variable corresponding coefficient matrix, x is an unknown variable matrix and comprises the generated energy of the motor in each time interval, the transmission power of the line in each time interval, the phase angle of each node in each time interval and the charge capacity of each energy storage time interval, h is a constraint constant term matrix, and E is the charge capacity of the energy storageThe maximum matrix, J is the wind power output matrix of each time interval, w is the position matrix of the wind motor, z * Is an energy storage position matrix;
step 3.2: decoupling the subproblem objective function by using a dual theorem, which is changed into:
max(h-Ez * -Jw) T τ
the constraints are noted as: a. The T τ≤b
Wherein tau is a variable after dual decoupling, b is C soc Transposing;
step 3.3: transmitting the sub-problem objective function and the constraint condition to a Pso algorithm, and solving the problem by using the Pso algorithm;
step 3.3.1: initializing various variable values and adaptive value functions, setting the type as a discrete type if the problem is a main problem, and setting the type as a continuous type if the problem is a sub problem; performing random initialization distribution on all particles, setting the number of the particles as n, the current iteration number as k, the initial value of the iteration number as 1, and the maximum iteration number as k max ；
Step 3.3.2: according to the step 2.2.1, the fitness value of the current individual and the maximum fitness values of all the current individuals are obtained through comparison, and each particle is subjected to speed updating:
V i '＝ωV i +c 1 ×rand×(q ibest -X i )+c 2 ×rand×(g best -X i )
X' i ＝X i +V i
k＝k+1
wherein, V i Representing the speed, V, of the individual i before updating i ' is the updated velocity, omega is the inertial weight coefficient, c 1 And c 2 Are all acceleration factors, rand denotes a random number between (0, 1), q ibest For the individual optimal position of the individual i, g best For the optimal position of the current group, X i Is the current individual position, X' i Is the updated individual position;
step 3.3.3: step 3.3.2 straight track k = k is repeatedly executed max When the iteration is finished, stopping the iteration;
step 3.3.4: outputting the position of the particle with the highest fitness, wherein the value of the problem to be solved is the fitness value of the particle with the highest fitness;
step 3.4: recording a target function value of the subproblem obtained by solving the pso algorithm as eta;
step 3.5: adding eta and tau obtained by the sub-problem into the main problem, and changing the main objective function and the constraint into:
min cz+η
Figure FDA0003911061970000031
step 3.6: the changed main problem objective function and the constraint condition are transmitted to a Pso algorithm of an inner layer, a negative value of the main problem objective function value is taken as an individual fitness value, and the main problem is solved;
step 3.7: updating the solution to the main problem z to z * Marking the target function value as xi, judging the values of xi and eta, and if eta-xi =0, ending the optimization and outputting a result; if eta-xi is not equal to 0, executing the step 3.3 to the step 3.7 and entering the next cycle;
and 4, step 4: and analyzing and outputting the optimization result.
2. A distributed energy storage robustness addressing system based on a Benders-Pso algorithm is realized based on the distributed energy storage robustness addressing method based on the Benders-Pso algorithm in claim 1, and is characterized by comprising the following steps:
the power distribution network information acquisition module is used for acquiring and transmitting network parameter information of the current power distribution network;
the modeling module is used for constructing a planning model of the Benders-Pso algorithm;
the optimization module is used for optimizing the network parameter information by adopting Benders-Pso to obtain an optimization result, namely an optimal energy storage position;
the analysis output module is used for analyzing and outputting the result of the optimization module;
the distribution network information acquisition module comprises: the power distribution network information acquisition device is used for acquiring network initial information of the current power distribution network, wherein the network initial information comprises power distribution network node information, branch information, position information and output information of the distributed power supply; the relay transmitter is used for transmitting the information collected by the power distribution network information collector to the modeling module;
the modeling module includes: the information preprocessing unit is used for receiving network initial information and preprocessing the initial information into a matrix form required by model construction; the model construction unit is used for determining an adaptive value function and each constraint condition so as to construct a planning model of the Benders-Pso algorithm;
the optimization module comprises:
the Benders algorithm unit is used for dividing the optimization problem into a robustness optimization problem and a distributed energy storage site selection problem, and ensuring that the final result is obtained to optimize the two problems;
the Pso algorithm unit is used for calculating the optimal values of the robustness optimization problem and the distributed energy storage site selection problem;
the Benders algorithm unit comprises a Benders initialization subunit, a Benders transformation subunit and a Benders judgment subunit;
the Benders initializing subunit is used for dividing the problems in the distributed energy storage robustness addressing system into a main problem part and a sub problem part and decoupling the sub problems;
the Benders transformation subunit is used for generating and restricting the solved result of the subproblem and adding the constraint into the main problem;
a Benders judging subunit, which is used for updating the variable and judging whether to carry out circulation;
the Pso algorithm unit comprises a Pso initial setting subunit and a Pso individual optimization subunit;
the Pso initial setting subunit is used for initializing the initial value of the individual in the Pso algorithm unit;
and the Pso individual optimization subunit updates the position information of the individuals in the population according to the optimization scheme so as to obtain the individuals with higher adaptive values and iterate.
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