CN113346526B - Multi-node energy storage system configuration method based on discrete-continuous hybrid method - Google Patents

Multi-node energy storage system configuration method based on discrete-continuous hybrid method Download PDF

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CN113346526B
CN113346526B CN202110563474.7A CN202110563474A CN113346526B CN 113346526 B CN113346526 B CN 113346526B CN 202110563474 A CN202110563474 A CN 202110563474A CN 113346526 B CN113346526 B CN 113346526B
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周喜超
王楠
赵鹏翔
李建林
李振
崔宜琳
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Beijing Lianzhi Huineng Technology Co ltd
State Grid Comprehensive Energy Service Group Co ltd
North China University of Technology
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Abstract

The invention relates to a multi-node energy storage system configuration method based on a discrete-continuous hybrid method. The method comprises the following steps: establishing a system and an economic model under a typical daily power curve of each node, and comprehensively considering the operation performance and the economy of the system to obtain a discrete-continuous mixed method objective function; fully considering the discrete part and the continuous part, defining the form and the number of the coding strings, and initializing the coding strings; sequencing the initial coding strings, and performing iteration of the coding strings by using a discrete-continuous mixed method; and finishing the maximum iteration times, and solving the energy storage control and address selection configuration which enables the target function to be optimal. The invention comprehensively considers the running performance and the economy of the energy storage system, considers the characteristics that the genetic algorithm is easy to combine with other algorithms, the particle swarm algorithm is simple but can not effectively solve the discrete problem, and the like, has simple structure and has important significance for the research and popularization of the energy storage system.

Description

Multi-node energy storage system configuration method based on discrete-continuous hybrid method
The technical field is as follows:
the invention relates to an energy storage system, in particular to a multi-node energy storage system configuration method based on a discrete-continuous hybrid method.
Background art:
when the energy storage system is planned, whether the position of energy storage access is proper or not needs to be considered, the energy storage is used as a bidirectional power element, the tidal current flow direction of the system can be directly influenced by the access position in the power system, the line load is changed, the network loss is influenced, and the power level of the system is influenced. Therefore, it is particularly critical to select a reasonable layout to improve the safety and stability of system operation. Due to the relatively high price of stored energy, it is also an important research content to select a reasonable configuration to improve the economic level of energy storage applications. In addition, in recent years, the installed scale of new energy is continuously improved, the new energy power generation has randomness and intermittence, the energy storage system can output in two directions, the output power is stable and controllable, the dynamic response speed is high, the new energy consumption can be promoted and the new energy power fluctuation can be stabilized, and the load can be continuously supplied with power when the new energy power generation is insufficient, so that the control and site selection configuration method of the energy storage system is researched by comprehensively considering the energy storage output characteristic and the economy, the influence on enhancing the power supply reliability, improving the electric energy quality and promoting the new energy consumption is realized, and the method plays an important role in promoting the development of new energy industry in China and changing the development mode of electric power in the long run.
The invention content is as follows:
the consideration of the economy is the inevitable development trend of realizing the popularization and application of the energy storage technology, and the application of the energy storage system is influenced by factors such as the technical economy level, the market environment, relevant policies and the like. The energy storage cost sources can include initial acquisition cost, operation and maintenance cost and the like, and energy storage site selection and optimal configuration can be realized with the lowest cost as a target.
The invention comprehensively considers the running performance and the economy of the energy storage system, considers the characteristics that the combination of a genetic algorithm and other algorithms is easier, a particle swarm algorithm is simple but can not effectively solve the discrete problem and the like, uses the particle swarm algorithm to carry out continuous optimization, and realizes the configuration of the energy storage rated power and the rated capacity of the multi-node energy storage system. The specific technical scheme is as follows:
a multi-node energy storage system configuration method based on a discrete-continuous hybrid method comprises the following processes:
step 1: establishing a system model under a typical daily power curve of each node; the method specifically comprises the following steps:
step 1.1: establishing a system model according to the power condition of each node;
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
limitation of energy state of energy storage system: SOEL≤SOE(t)≤SOEU，
Limitation of energy storage system power: -Pe≤PBk(t)≤Pe，
Limitation of initial acquisition cost of energy storage system: cPPe+CQQe≤A，
The energy state of the energy storage system in the initial period of each day is limited to be the same as that in the ending period: SOE (T)s)＝SOE(Te)，
The node number of the power system is J, J is more than or equal to 0 and less than or equal to J, k is a node in which energy storage is installed, Pk(t) is the power after the energy storage is installed at the k node at the t moment, Py(t) is the power before the k node is installed with energy storage at the t moment, PBk(t) is the power of the k-node energy storage system at the t-th moment, SOE (t) is the energy state of the energy storage system at the t-th moment, delta t is sampling time, eta is the charge-discharge efficiency of the energy storage system, and QeFor rating the capacity of the energy storage system, PeFor rating of energy storage systems, QeFor rating the capacity of the energy storage system, CPFor cost per unit power of the energy storage system, CQFor the cost of the energy storage system per unit capacity, A is the upper limit of the initial acquisition cost of the energy storage system, SOE (T)s) For the energy state of the energy storage system at the initial moment of the day, SOE (T)e) For the energy state of the energy storage system at the end of the day, SOELRepresenting the lower limit of energy state, SOE, of the energy storage systemURepresents the energy state lower limit of the energy storage system;
obtaining the related power function f of the energy storage system1：
Figure BDA0003079862830000021
TsFor the first moment of daily sampling, TeThe last moment of sampling per day;
step 1.2, establishing an economic model according to the cost condition of each node;
initial acquisition cost C of energy storage systemc：Cc＝CP×Pe+CQ×Qe，
Operation and maintenance cost C of energy storage systemy:Cy＝CPy×Pe+CQy×Qe，
Coefficient of capital recovery By：
Figure BDA0003079862830000031
CPyFor the unit power operation and maintenance cost of the energy storage system, CQyThe unit capacity operation and maintenance cost of the energy storage system is calculated, r is the current rate, Y is the operation age of the energy storage system, the total cycle life can be known through the battery type, and the equivalent daily cycle life can be known through the power loss, for example, a rain flow counting method is used, the total cycle life is divided by the daily cycle life to obtain a value, and then the value is divided by 365, so that the service life age of the energy storage system can be obtained;
constraint conditions of the economic model:
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
SOEL≤SOE(t)≤SOEU，
-Pe≤PBk(t)≤Pe
CPPe+CQQe≤A
SOE(Ts)＝SOE(Te)；
obtaining a daily cost function f of the energy storage system2：
f2＝(Cc×By+Cy)÷365，
Step 1.3: according to the energy storage system power loss function and the energy storage system daily cost function obtained in the steps 1.1 and 1.2, obtaining an objective function f comprehensively considering the system operation performance and the economy and used for a discrete-continuous hybrid method3：
Figure BDA0003079862830000032
Wherein F [ F ]1]Representation and function f1A related system power loss formula;
step 2: fully considering the discrete part and the continuous part, defining the form and the number of the coding strings, and initializing the coding strings;
step 2.1: fully considering discrete part and continuous part, defining energy storage installation node, energy storage rated power and rated capacity in the node, and energy storage power P of the nodeBk(t) in the g-th iteration, the code string is
Figure BDA0003079862830000041
The code string length is equal to the number of code bits m + n, wherein the discrete part binary code string is
Figure BDA0003079862830000042
The discrete part binary code string length is equal to the number of code bits n,
Figure BDA0003079862830000043
to
Figure BDA0003079862830000044
The binary coding bit between the nodes corresponds to the selected node of the energy storage, and the value of the selected node is any integer from zero to the serial number J of the energy storage node, 1≤p≤n-1，
Figure BDA0003079862830000045
To
Figure BDA0003079862830000046
Binary coded bit correspondence between
Figure BDA0003079862830000047
To
Figure BDA0003079862830000048
The rated power and the rated capacity of the node installation energy storage corresponding to the binary coding bit between the two nodes need to satisfy the condition that n-p is an even number, and the values of the rated power and the rated capacity are from zero to zero
Figure BDA0003079862830000049
The length of the continuous partial code string is
Figure BDA00030798628300000410
Wherein b iskdFor the power of the energy storage system at the d-th moment of the k node for installing the energy storage, S code strings are initially randomly generated
Figure BDA00030798628300000411
e＝1,2,…,F；
Step 2.2, initializing the maximum iteration times, the selection rate, the cross rate, the variation rate, the particle speed, the position and the like of the coding string;
and step 3: sequencing the initial coding strings, and performing iteration of the coding strings by using a discrete-continuous mixed method;
and 4, step 4: and finishing the maximum iteration times, and solving the energy storage control and address selection configuration which enables the target function to be optimal.
Compared with the closest prior art, the invention has the beneficial effects that:
in the technical scheme of the invention, by taking the speed updating and position updating ideas in the particle swarm optimization and the coding, selecting, crossing and variation ideas in the genetic evolution process as reference, the operating performance and the economical efficiency of an energy storage system are comprehensively considered, the genetic algorithm is easily combined with other algorithms, the particle swarm optimization is simple but can not effectively solve the problems of dispersion and combination optimization, and the like, each coding string is split into a discrete part and a continuous part when optimized iteration is carried out, the particle swarm optimization is used for carrying out continuous part iteration, the genetic algorithm is used for carrying out discrete part iteration, the respective iteration finishes synthesizing the coding string into one complete iteration, when the requirement is no longer met, the coding string which enables the operating performance and the economical efficiency of the system to be optimal is output, further, the energy storage system control and the address selection configuration of one node of the power system are realized, and a single-layer model is constructed, simple structure is favorable to energy storage system's research popularization.
Description of the drawings:
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of an embodiment of an encoding string.
FIG. 3 is a schematic process flow diagram of step 2, step 3 and step 4 in the example
FIG. 4 is a schematic diagram of the genetic crossover operation in the example.
FIG. 5 is a schematic diagram of the genetic variation calculation process in the example.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
A multi-node energy storage system configuration method based on a discrete-continuous hybrid method comprises the following processes:
step 1: establishing a system model under a typical daily power curve of each node; the method specifically comprises the following steps:
step 1.1: establishing a system model according to the power condition of each node;
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
limitation of energy state of energy storage system: SOEL≤SOE(t)≤SOEU，
Of power in energy-storage systemsAnd (3) limiting: -Pe≤PBk(t)≤Pe，
Limitation of initial acquisition cost of energy storage system: cPPe+CQQe≤A，
The energy state of the energy storage system in the initial period of each day is limited to be the same as that in the ending period: SOE (T)s)＝SOE(Te)，
The node number of the power system is J, J is more than or equal to 0 and less than or equal to J, k is a node in which energy storage is installed, Pk(t) is the power after the energy storage is installed at the k node at the t moment, Py(t) is the power before the k node is installed with energy storage at the t moment, PBk(t) is the power of the k-node energy storage system at the t-th moment, SOE (t) is the energy state of the energy storage system at the t-th moment, delta t is sampling time, eta is the charge-discharge efficiency of the energy storage system, and QeFor rating the capacity of the energy storage system, PeFor rating of energy storage systems, QeFor rating the capacity of the energy storage system, CPFor cost per unit power of the energy storage system, CQThe unit capacity cost of the energy storage system, A is the upper limit of the initial acquisition cost of the energy storage system, SOE (T)s) For the energy state of the energy storage system at the initial moment of the day, SOE (T)e) For the energy state of the energy storage system at the end of the day, SOELRepresenting the lower limit of energy state, SOE, of the energy storage systemURepresents the energy state lower limit of the energy storage system;
obtaining the related power function f of the energy storage system1：
Figure BDA0003079862830000061
TsFor the first moment of daily sampling, TeThe last moment of sampling per day;
step 1.2, establishing an economic model according to the cost condition of each node;
initial acquisition cost C of energy storage systemc：Cc＝CP×Pe+CQ×Qe，
Operation and maintenance cost C of energy storage systemy:Cy＝CPy×Pe+CQy×Qe，
Coefficient of capital recovery By：
Figure BDA0003079862830000062
CPyFor the unit power operation and maintenance cost of the energy storage system, CQyThe unit capacity operation and maintenance cost of the energy storage system is calculated, r is the current rate, Y is the operation age of the energy storage system, the total cycle life can be known through the battery type, and the equivalent daily cycle life can be known through the power loss, for example, a rain flow counting method is used, the total cycle life is divided by the daily cycle life to obtain a value, and then the value is divided by 365, so that the service life age of the energy storage system can be obtained;
constraint conditions of the economic model:
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
SOEL≤SOE(t)≤SOEU，
-Pe≤PBk(t)≤Pe
CPPe+CQQe≤A
SOE(Ts)＝SOE(Te)；
obtaining a daily cost function f of the energy storage system2：
f2＝(Cc×By+Cy)÷365，
Step 1.3: according to the energy storage system power loss function and the energy storage system daily cost function obtained in the steps 1.1 and 1.2, obtaining an objective function f comprehensively considering the system operation performance and the economy and used for a discrete-continuous hybrid method3：
Figure BDA0003079862830000075
Wherein F [ F ]1]Representation and function f1The associated system power loss formula;
step 2: fully considering the discrete part and the continuous part, defining the form and the number of the coding strings, and initializing the coding strings;
step 2.1: fully considering discrete part and continuous part, defining energy storage installation node, energy storage rated power and rated capacity in the node, and energy storage power P of the nodeBk(t) in the g-th iteration, as shown in FIG. 2, the code string is
Figure BDA0003079862830000071
The code string length is equal to the number of code bits m + n, wherein the discrete part binary code string is
Figure BDA0003079862830000072
The discrete part binary code string length is equal to the number of code bits n,
Figure BDA0003079862830000073
to
Figure BDA0003079862830000074
The binary coding bit between the nodes corresponds to the selected nodes of the energy storage, the value of the selected nodes is any integer from zero to the number J of the energy storage nodes, p is more than or equal to 1 and less than or equal to n-1,
Figure BDA0003079862830000081
to
Figure BDA0003079862830000082
Binary coded bit correspondence between
Figure BDA0003079862830000083
To
Figure BDA0003079862830000084
The rated power and the rated capacity of the node installation energy storage corresponding to the binary coding bit between the two nodes need to satisfy the condition that n-p is an even number, and the values of the rated power and the rated capacity are from zero to zero
Figure BDA0003079862830000085
The length of the continuous partial code string is
Figure BDA0003079862830000086
Wherein b iskdFor the power of the energy storage system at the d-th moment of the k node for installing the energy storage, S code strings are initially randomly generated
Figure BDA0003079862830000087
e＝1,2,…,F；
Step 2.2, initializing the maximum iteration times, the selection rate, the cross rate, the variation rate, the particle speed, the position and the like of the coding string; the method comprises the following specific processes:
step 2.2.1: initializing the maximum iteration number G of the code string, and initializing the discrete part cross rate PcThe rate of variation PmSetting a learning factor C1And C2The inertia weight w initializes the speed and the position of the continuous part of particles, the d-dimension position vector represents the power of the energy storage system at the d-th moment, the d-dimension speed vector represents the change amount of the power of the energy storage system at the d-th moment, and the constraint conditions are as follows:
particle position limitation:
Figure BDA0003079862830000088
Figure BDA0003079862830000089
is the position of the particle f in the d-dimension in the j-th iteration, xminIs the minimum value of position, corresponding to-Pe, known from the discrete part-Pe, xmaxIs the maximum value of the position, corresponding to Pe, which is known from the discrete part,
particle velocity limitation:
Figure BDA00030798628300000810
Figure BDA00030798628300000811
is the d-dimensional velocity, v, of the particle f in the j iterationminIs the minimum value of velocity, vmaxIs the maximum value of the speed;
step 2.2.2: selecting corresponding energy storage installation node, energy storage rated power and rated capacity in the node and energy storage power P of the node according to the number value of the coding bits of the coding stringBk(t) substituting the constraint conditions of the system and the economic model established in the step 1, judging whether the F initial coding strings generated randomly meet the constraint conditions, then removing the coding strings which do not meet the constraint conditions and randomly generating the coding strings with the same number as the removed coding strings again until all the F coding strings meet the constraint conditions, wherein the initialization iteration number is 0, namely g is 0;
and step 3: sequencing the initial coding strings, and performing iteration of the coding strings by using a discrete-continuous mixed method; the method comprises the following specific processes:
step 3.1: after F coding strings which all meet the constraint condition are obtained, the corresponding energy storage installation node, the energy storage rated capacity and rated power of the node and the energy storage power P of the node are selected according to the coding bit valueBk(t) substitution of f3Calculating the function values corresponding to the F code strings, if so
Figure BDA0003079862830000091
If the binary code string corresponding to the energy storage addressing node number is larger than the default corresponding energy storage node 0, sorting the advantages and disadvantages according to the objective function;
step 3.2: each time coding string
Figure BDA0003079862830000092
When performing the iteration, dividing into discrete parts
Figure BDA0003079862830000093
And a continuous portion
Figure BDA0003079862830000094
Iteration, wherein the continuous part uses the speed update and the position update of the particle swarm algorithm in an iteration mode, the discrete part uses the selection, the intersection and the variation of the genetic algorithm in an iteration mode, and the coding string which is finished in an iteration mode and meets the constraint condition is the next generation coding string; such asFig. 3 shows the following specific processes:
step 3.2.1: performing iteration on the code string which is sorted according to the advantages and disadvantages of the target function, and dividing the iteration into discrete part iteration and continuous part iteration;
step 3.2.2: discrete part is formulated for particle swarm
Figure BDA0003079862830000095
The speed is updated in a way that the speed is updated,
according to the formula
Figure BDA0003079862830000096
Carrying out position updating;
Figure BDA0003079862830000097
the position of the particle f at the individual extreme point in the d-th dimension,
Figure BDA0003079862830000098
is the position of the global extreme point of the whole population in the d-dimension1、r2A random number from 0 to 1;
step 3.2.3: the continuous part performs selection operation, crossover operation and mutation operation on the binary coded bit values of each binary coded string, as shown in fig. 4 and 5;
step 3.2.4: judging whether the code strings after the iteration of the discrete part and the continuous part meet the constraints of the system and the economic model established in the step 1, then removing the code strings which do not meet the constraints and randomly generating the code strings with the same number as the removed code strings again until all the F code strings are judged to meet the constraints, wherein the iteration number is increased by 1, namely g is g + 1;
and 4, step 4: and ending the maximum iteration times to obtain the energy storage control and address selection configuration which enables the target function to be optimal, wherein the method comprises the following specific processes:
step 4.1: judging whether the current iteration number reaches the maximum iteration number G, if so, outputting a coding string which enables the system operation performance and the economy to be optimal and not continuing to optimize, otherwise, returning to the step 3.2.1;
step 4.2: and 4.1, obtaining the energy storage power control condition of one node of the power system, the energy storage installation node, and the energy storage rated power and rated capacity of the node through the encoding string output in the step 4.1.

Claims (5)

1. A multi-node energy storage system configuration method based on a discrete-continuous hybrid method is characterized by comprising the following processes:
step 1: establishing a system model under a typical daily power curve of each node; the method specifically comprises the following steps:
step 1.1: establishing a system model according to the power condition of each node;
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
limitation of energy state of energy storage system: SOEL≤SOE(t)≤SOEU，
Limitation of energy storage system power: -Pe≤PBk(t)≤Pe，
Limitation of initial acquisition cost of energy storage system: cPPe+CQQe≤A，
The energy state of the energy storage system in the initial period of each day is limited to be the same as that in the ending period: SOE (T)s)＝SOE(Te)，
The node number of the power system is J, J is more than or equal to 0 and less than or equal to J, k is a node in which energy storage is installed, Pk(t) is the power after the energy storage is installed at the k node at the t moment, Py(t) is the power before the k node is installed with energy storage at the t moment, PBk(t) is the power of the k-node energy storage system at the t-th moment, SOE (t) is the energy state of the energy storage system at the t-th moment, delta t is sampling time, eta is the charge-discharge efficiency of the energy storage system, and QeFor rating the capacity of the energy storage system, PeFor rating of energy storage systems, QeFor rating the capacity of the energy storage system, CPFor cost per unit power of the energy storage system, CQFor the cost of the energy storage system per unit capacity, A is the upper limit of the initial acquisition cost of the energy storage system, SOE (T)s) For the energy state of the energy storage system at the initial moment of the day, SOE (T)e) For the energy state of the energy storage system at the end of the day, SOELRepresenting the lower limit of energy state, SOE, of the energy storage systemURepresents the energy state lower limit of the energy storage system;
obtaining the related power function f of the energy storage system1：
Figure FDA0003079862820000011
TsFor the first moment of daily sampling, TeThe last moment of sampling per day;
step 1.2, establishing an economic model according to the cost condition of each node;
initial acquisition cost C of energy storage systemc：Cc＝CP×Pe+CQ×Qe，
Operation and maintenance cost C of energy storage systemy:Cy＝CPy×Pe+CQy×Qe，
Coefficient of capital recovery By：
Figure FDA0003079862820000021
CPyFor the unit power operation and maintenance cost of the energy storage system, CQyThe unit capacity operation and maintenance cost of the energy storage system is calculated, r is the current rate, Y is the operation age of the energy storage system, the total cycle life can be known through the battery type, the equivalent daily cycle life can be known through the power loss, and the service life of the energy storage system can be obtained by dividing 365 by the value obtained by dividing the daily cycle life by the total cycle life by a rain flow counting method;
constraint conditions of the economic model:
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
SOEL≤SOE(t)≤SOEU，
-Pe≤PBk(t)≤Pe
CPPe+CQQe≤A
SOE(Ts)＝SOE(Te)；
obtaining a daily cost function f of the energy storage system2：
f2＝(Cc×By+Cy)÷365，
Step 1.3: according to the energy storage system power loss function and the energy storage system daily cost function obtained in the steps 1.1 and 1.2, obtaining an objective function f comprehensively considering the system operation performance and the economy and used for a discrete-continuous hybrid method3：
Figure FDA0003079862820000022
Wherein F [ F ]1]Representation and function f1The associated system power loss formula;
step 2: fully considering the discrete part and the continuous part, defining the form and the number of the coding strings, and initializing the coding strings;
step 2.1: fully considering discrete part and continuous part, defining energy storage installation node, energy storage rated power and rated capacity in the node, and energy storage power P of the nodeBk(t) in the g-th iteration, the code string is
Figure FDA0003079862820000031
The code string length is equal to the number of code bits m + n, wherein the discrete part binary code string is
Figure FDA0003079862820000032
The discrete part binary code string length is equal to the number of code bits n,
Figure FDA0003079862820000033
to
Figure FDA0003079862820000034
The binary coding bit between the nodes corresponds to the selected nodes of the energy storage, the value of the selected nodes is any integer from zero to the number J of the energy storage nodes, p is more than or equal to 1 and less than or equal to n-1,
Figure FDA0003079862820000035
to
Figure FDA0003079862820000036
Binary coded bit correspondence between
Figure FDA0003079862820000037
To
Figure FDA0003079862820000038
The rated power and the rated capacity of the node installation energy storage corresponding to the binary coding bit between the two nodes need to satisfy the condition that n-p is an even number, and the values of the rated power and the rated capacity are from zero to zero
Figure FDA0003079862820000039
The length of the continuous partial code string is
Figure FDA00030798628200000310
Wherein b iskdFor the power of the energy storage system at the d-th moment of the k node for installing the energy storage, S code strings are initially randomly generated
Figure FDA00030798628200000311
Step 2.2, initializing the maximum iteration times, the selection rate, the crossing rate, the variation rate, the particle speed and the position of the coding string;
and step 3: sequencing the initial coding strings, and performing iteration of the coding strings by using a discrete-continuous mixed method;
and 4, step 4: and finishing the maximum iteration times, and solving the energy storage control and address selection configuration which enables the target function to be optimal.
2. A multi-node energy storage system configuration method based on a discrete-continuous hybrid method according to claim 1, characterized in that the step 2.2 comprises the following processes:
step 2.2.1: initializing the maximum iteration number G of the code string, and initializing the discrete part cross rate PcThe rate of variation PmSetting a learning factor C1And C2The inertia weight w initializes the speed and the position of the continuous part of particles, the d-dimension position vector represents the power of the energy storage system at the d-th moment, the d-dimension speed vector represents the change amount of the power of the energy storage system at the d-th moment, and the constraint conditions are as follows:
particle position limitation:
Figure FDA0003079862820000041
Figure FDA0003079862820000042
for the position of the particle f in the d-dimension, x, in the j-th iterationminIs the minimum value of position, corresponding to-Pe, known from the discrete part-Pe, xmaxIs the maximum value of the position, corresponding to Pe, which is known from the discrete part,
particle velocity limitation:
Figure FDA0003079862820000043
Figure FDA0003079862820000044
is the d-dimensional velocity, v, of the particle f in the j iterationminIs the minimum value of velocity, vmaxIs the maximum value of the speed;
step 2.2.2: selecting corresponding energy storage installation node, energy storage rated power and rated capacity in the node and energy storage power P of the node according to the number value of the coding bits of the coding stringBk(t) substituting the constraint conditions of the system and economic model established in the step 1 to judge F initial coding strings generated randomlyAnd if the constraint condition is met, removing the coding strings which do not meet the constraint condition and randomly generating the same number of coding strings as the removed coding strings again until all the F coding strings are judged to meet the constraint condition, wherein the initialization iteration number is 0, namely g is 0.
3. The method for configuring the multi-node energy storage system based on the discrete-continuous hybrid method as claimed in claim 1, wherein the step 3 comprises the following processes:
step 3.1: after F coding strings which all meet the constraint condition are obtained, the corresponding energy storage installation node, the energy storage rated capacity and rated power of the node and the energy storage power P of the node are selected according to the coding bit valueBk(t) substitution of f3Calculating the function values corresponding to the F code strings, if so
Figure FDA0003079862820000045
If the binary code string corresponding to the energy storage addressing node number is larger than the default corresponding energy storage node 0, sorting the advantages and disadvantages according to the objective function;
step 3.2: each time coding string
Figure FDA0003079862820000046
When performing the iteration, dividing into discrete parts
Figure FDA0003079862820000051
And a continuous portion
Figure FDA0003079862820000052
And (3) iteration, wherein the continuous part uses the speed update and the position update of the particle swarm algorithm in an iteration mode, the discrete part uses the selection, the intersection and the variation of the genetic algorithm in an iteration mode, and the encoding string which is finished in an iteration mode and meets the constraint condition is the next generation encoding string.
4. A method for configuring a multi-node energy storage system based on a discrete-continuous hybrid method according to claim 3, wherein the step 3.2 comprises the following steps:
step 3.2.1: performing iteration on the code string which is sorted according to the advantages and disadvantages of the target function, and dividing the iteration into discrete part iteration and continuous part iteration;
step 3.2.2: discrete part to particle group according to formula
Figure FDA0003079862820000053
The speed is updated in a way that the speed is updated,
according to the formula
Figure FDA0003079862820000054
Carrying out position updating;
Figure FDA0003079862820000055
the position of the particle f at the individual extreme point in dimension d,
Figure FDA0003079862820000056
is the position of the global extreme point of the whole population in the d-dimension1、r2A random number from 0 to 1;
step 3.2.3: the continuous part carries out selection operation, cross operation and mutation operation on binary coding bit values of each binary coding string;
step 3.2.4: and (3) judging whether the code strings after the iteration of the discrete part and the continuous part meet the constraints of the system and the economic model established in the step (1), then removing the code strings which do not meet the constraints and randomly generating the code strings with the same number as the removed code strings again until all the F code strings meet the constraints, wherein the iteration number is increased by 1, namely g is g + 1.
5. The method for configuring the multi-node energy storage system based on the discrete-continuous hybrid method as claimed in claim 4, wherein the step 4 comprises the following processes:
step 4.1: judging whether the current iteration number reaches the maximum iteration number G, if so, outputting a coding string which enables the system operation performance and the economy to be optimal and not continuing to optimize, otherwise, returning to the step 3.2.1;
step 4.2: and 4.1, obtaining the energy storage power control condition of one node of the power system, the energy storage installation node, and the energy storage rated power and rated capacity of the node through the encoding string output in the step 4.1.
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