CN114725971B - Operation decision method and system based on hybrid energy storage system - Google Patents
Operation decision method and system based on hybrid energy storage system Download PDFInfo
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Abstract
The invention provides an operation decision method and system based on a hybrid energy storage system, wherein the method comprises the following steps: firstly, establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes; then, a space-time decision model of the hybrid energy storage system is constructed based on the objective function and the constraint condition; and finally, making a decision on the operation of the hybrid energy storage system by using the space-time decision model. By the operation decision method and the operation decision system based on the hybrid energy storage system, the flexibility of the hybrid energy storage system in the battery energy storage operation process can be improved, the battery energy storage efficiency is improved, and the operation income is improved.
Description
Technical Field
The invention relates to the field of energy storage, in particular to an operation decision method and system based on a hybrid energy storage system.
Background
With the large-scale access of intermittent renewable energy and traffic electrification, the uncertainty of energy supply and consumption side is increased, higher requirements are put on the flexibility of an energy and traffic system, and the energy and traffic system faces significant transformation. The main flow technology and ecology of the transformed low-carbon energy, traffic and other industries are deeply changed. The electrochemical energy storage battery is taken as a key technology for integrating renewable energy sources, is expected to promote the development of a carbon energy source system, is expected to be widely distributed in electric automobiles in the energy source system, and forms a battery network for coupling energy sources and a traffic system.
In the prior art, in order to realize energy storage of an electric vehicle, the electric vehicle can store energy by using a fixed energy storage system such as a battery energy storage power station, and can also store energy by using a mobile energy storage system such as a mobile energy storage vehicle loaded with a battery. However, most of the existing research on battery energy storage systems at present mainly focuses on the optimization research of charging and discharging or battery replacement of a single energy storage system, so that the battery energy storage operation process is not flexible and has low efficiency.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects of the prior art that a single battery energy storage system is not flexible enough and has low efficiency in the battery energy storage process, so as to provide an operation decision method and system based on a hybrid energy storage system.
In a first aspect, the present invention provides an operation decision method based on a hybrid energy storage system, where the method includes:
establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes;
constructing a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
In the method, the maximized operation income is used as a target function, in order to realize the maximization of the income, the hybrid energy storage of multiple energy storage modes is realized, the constraint of the multiple energy storage modes is used as a constraint condition, a space-time decision model is established, the operation of the hybrid energy storage system is decided, the flexibility of the battery energy storage modes is increased, the efficiency of the battery energy storage is improved, and the operation income is improved to the maximum extent.
In one embodiment, the objective function includes a market revenue function and a cost function, the objective function being the difference between the market revenue function and the cost function, the cost function including a transportation cost function, a replacement battery cost function, and a battery aging cost function.
In this manner, the value of the target is calculated based on the difference between the cost function and the market revenue function.
In one embodiment, the market revenue function is:
，
wherein the content of the first and second substances,REVthe revenue of the market is expressed and,
representing a set of battery network node-time pairs,
representing nodesgIn thattThe node at the time is marginal in electricity prices,
、
respectively representtIs located at a node at a timegThe amount of charge and the amount of discharge of the fixed energy storage system,
represents a collection of mobile energy storage vehicles,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe amount of charge and discharge at the point;
the cost function is:
，
wherein the content of the first and second substances,
which represents a cost of transportation and,
indicating the cost of replacing the battery,
representing the cost of battery aging.
In the mode, the marginal electricity price of the node and the charging amount and the discharging amount of the vehicle at the node are considered, so that the market profit is comprehensively calculated, and the accuracy of the market profit is improved.
In one embodiment, the transportation costThe function is:
，
wherein the content of the first and second substances,
representing a set of paths consisting of nodes of the battery network,
indicating a mobile energy storage vehiclevOn the way
The cost of transportation;
as a first decision variable, when the mobile energy storage vehicle passes through the path
When the utility model is used, the water is discharged,
when the mobile energy storage vehicle does not pass through the path
When the temperature of the water is higher than the set temperature,
；
the replacement battery cost function is:
，
wherein the content of the first and second substances,
the cost of unit battery replacement is expressed,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe charge and discharge generated after the battery is replaced;
the battery aging cost function is:
wherein the content of the first and second substances,
representing the battery marginal aging cost.
In the method, the cost function is determined by the transportation cost function, the battery replacement cost function and the battery cost aging function together so as to calculate the cost and improve the accuracy of the cost.
In an embodiment, the constraint conditions of the multiple energy storage mode constraints include: the method comprises the following steps of mobile energy storage system constraint, fixed energy storage system constraint and hybrid energy storage combined constraint conditions.
In the mode, multiple energy storage mode constraints are taken as constraint conditions, so that the efficiency of the battery energy storage in the power grid is improved, and the flexibility of the battery energy storage in the power grid is increased.
In one embodiment, the mobile energy storage system restraint comprises: a path constraint function, a first capacity constraint function and a charge and discharge constraint function; the fixed energy storage system restraint comprises: a second capacity constraint function; the hybrid energy storage joint constraint comprises: a third capacity constraint function.
In one embodiment, the path constraint function of the mobile energy storage system is:
wherein the content of the first and second substances,
、
respectively represents the path sets of the mobile energy storage vehicle entering and exiting nodes,
、
respectively representing the starting node and the terminating node of a mobile energy storage vehicle,nrepresenttMoving the node position of the energy storage vehicle at all times;
the first capacity constraint function of the mobile energy storage system is as follows:
，
wherein the content of the first and second substances,
to representtConstantly-moving energy-storage vehiclevThe state of charge of (a) is,
to represent
Constantly-moving energy-storage vehiclevThe state of charge of (a) is,
it is shown that the self-discharge rate,
represents the charge or discharge efficiency;
the charge and discharge constraint function constrained by the mobile energy storage system is as follows:
，
wherein the content of the first and second substances,
is a second decision variable whentConstantly-moving energy-storage vehiclevAt a nodegWhen it is time to charge or discharge or replace the battery,
when is coming into contact withtConstantly-moving energy-storage vehiclevAt a nodegWhen the battery is not charged, not discharged and not replaced,
，
representing nodesgThe number of mobile energy storage vehicles capable of simultaneously performing charging, discharging and battery replacement;
the second capacity constraint function of the fixed energy storage system is:
wherein the content of the first and second substances,
to representtIs located at a node at a momentgThe state of charge of the stationary energy storage system,
to representtTime-1 at a nodegThe state of charge of the stationary energy storage system.
The third capacity constraint function of the hybrid energy storage system is:
wherein the content of the first and second substances,
representtTime nodegThe maximum charge or discharge amount of.
In the method, the constraint condition of the mobile energy storage system is determined by the path constraint function, the first capacity constraint function and the charge-discharge constraint function together to enhance the accuracy of the constraint condition of the mobile energy storage system, the constraint condition of the fixed energy storage system is determined by the second capacity constraint function to enhance the accuracy of the constraint condition of the fixed energy storage system, and the constraint condition of the hybrid energy storage system is determined by the third capacity constraint function to enhance the accuracy of the constraint condition of the hybrid energy storage system.
In a second aspect, the present invention provides an operation decision system based on a hybrid energy storage system, the system comprising:
the establishing module is used for establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes;
the building module is used for building a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and the decision module is used for making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the hybrid energy storage system-based operation decision method according to any one of the first aspect and the optional embodiments thereof.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the hybrid energy storage system-based operation decision method according to any one of the first aspect and the optional embodiments thereof.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an operation decision method based on a hybrid energy storage system according to an embodiment of the present invention.
Fig. 2 is a block diagram of an operation decision system based on a hybrid energy storage system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In order to improve the flexibility of the battery energy storage system during the battery energy storage operation process and improve the efficiency of battery energy storage, an embodiment of the present invention provides an operation decision method based on a hybrid energy storage system, as shown in fig. 1, the method includes steps S1 to S3.
Step S1: and establishing an objective function for maximizing the operation income and constraint conditions constrained by various energy storage modes.
In the embodiment of the invention: the objective function is an optimization target, and under the constraint of constraint conditions, the value of the objective function is continuously optimized to finally obtain an optimal solution, so that the maximization of an objective function value is realized.
In a specific embodiment, the objective function may be established first, and then the constraint condition may be established; the constraint function may be established first, and then the objective function may be established, which is not limited in this embodiment.
The maximum operation income is used as the objective function, the extreme value of the objective function can be obtained when the objective function meets the constraint condition of the constraint function, the operation income of an operator is maximized, the operation income of the operator is improved to the maximum extent, and space-time arbitrage is further realized.
Step S2: and constructing a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition.
In the embodiment of the invention: the space-time decision model solves the optimal solution under the constraint of a certain constraint condition so that the objective function obtains an expected extreme value.
Step S3: and (4) making a decision on the operation of the hybrid energy storage system by using a space-time decision model.
In the embodiment of the invention: and the space-time decision module is utilized to improve the energy storage efficiency of the battery and improve the operation income.
Through the embodiment, the maximized operation income is taken as a target function, the multiple energy storage modes are used for hybrid energy storage for realizing the maximization of the income, the multiple energy storage modes are used for constraint as constraint conditions, a space-time decision model is built, the operation of the hybrid energy storage system is decided, the flexibility of the battery energy storage modes is improved, the efficiency of the battery energy storage is improved, and the operation income is improved.
In one embodiment, the objective function includes a market gain function and a cost function, the objective function being the difference between the market gain function and the cost function, the cost function including a transportation cost function, a battery replacement cost function, and a battery aging cost function.
Specifically, the market gain function is:
wherein, the first and the second end of the pipe are connected with each other,REVthe revenue of the market is expressed and,
representing a set of battery network node-time pairs,
representing nodesgIn thattThe node at the time is marginal in electricity prices,
、
respectively representtIs located at a node at a momentgThe amount of charge and the amount of discharge of the fixed energy storage system,
a collection of mobile energy storage vehicles is represented,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe amount of charge and the amount of discharge.
Specifically, the cost function is:
wherein, in the step (A),
which represents a cost of transportation and,
indicating the cost of replacing the battery,
representing the cost of battery aging.
Specifically, the cost of transportation function is:
wherein the content of the first and second substances,
representing a set of paths consisting of nodes of the battery network,
indicating a mobile energy storage vehiclevOn the path
The transportation cost of (a);
as a first decision variable, when the mobile energy storage vehicle passes through the path
When the current is over;
when the mobile energy storage vehicle does not pass through the path
When the temperature of the water is higher than the set temperature,
；
specifically, the replacement battery cost function is:
wherein the content of the first and second substances,
the cost of unit battery replacement is expressed,
、
respectively representtConstantly-moving energy storage vehiclevAt a nodegThe amount of charge and discharge generated after the battery is replaced;
specifically, the battery aging cost function is:
wherein the content of the first and second substances,
representing the battery marginal aging cost.
In summary, the objective function is:
wherein the content of the first and second substances,frepresenting the operational revenue of the hybrid energy storage system.
In another embodiment, the constraint conditions for the multiple energy storage mode constraints include: the method comprises the following steps of mobile energy storage system constraint, fixed energy storage system constraint and hybrid energy storage combined constraint conditions.
The mobile energy storage system is a battery loaded on the vehicle and a power conversion system, the mobile energy storage vehicle runs among nodes with electricity price difference, charges the nodes with low electricity price and discharges the nodes with high electricity price, so that the congestion of a power grid is relieved, and the space-time arbitrage is realized; the fixed energy storage system comprises a battery energy storage power station and can be an energy storage type electric automobile charging pile; the hybrid energy storage combination is to fuse a mobile energy storage system and a fixed energy storage system and apply the hybrid energy storage system to a power grid.
Mobile energy storage system constraints include: a path constraint function, a first capacity constraint function and a charge and discharge constraint function; the fixed energy storage system constraints include: a second capacity constraint function; hybrid energy storage joint constraints include: a third capacity constraint function.
The path constraint function of the mobile energy storage system is as follows:
wherein, the first and the second end of the pipe are connected with each other,
、
respectively represents the path sets of the mobile energy storage vehicle entering and exiting nodes,nto representtThe node position of the energy storage vehicle is moved at any moment,
、
respectively representing a starting node and a terminating node of the mobile energy storage vehicle;
specifically, the mobile energy storage vehicle satisfies ingress and egress node flow conservation except for the originating node and the terminating node. The flow conservation of the inlet and outlet nodes means that the mobile energy storage vehicles respectively enter and exit a certain node, namely pass through the node.
Specifically, the first capacity constraint function of the mobile energy storage system is:
wherein the content of the first and second substances,
to representtConstantly-moving energy-storage vehiclevThe state of charge of (a) is,
mobile energy storage vehicle capable of representing timevThe state of charge of (a) is,
it is shown that the self-discharge rate,
represents the charge or discharge efficiency;
indicating the capacity of the mobile energy storage vehicle,
to representtTime nodegThe maximum charge or discharge amount of (a),
is a second decision variable whentConstantly-moving energy-storage vehiclevAt a nodegWhen the battery is charged, discharged or replaced,
when it comes totConstantly-moving energy-storage vehiclevAt a nodegWhen the battery is not charged, not discharged and not replaced,
。
specifically, equations (7) and (8) indicate that the state of charge of the mobile energy storage vehicle cannot exceed its capacity, and equation (9) indicates that the charge or discharge amount of the mobile energy storage vehicle cannot exceed the maximum charge or discharge amount of the node.
Specifically, in the scheduling process, the charge-discharge constraint function constrained by the mobile energy storage system is as follows:
wherein the content of the first and second substances,
representing nodesgThe number of mobile energy storage vehicles capable of simultaneously performing charging, discharging and battery replacement;
the expression (10) shows that the number of charging and discharging interfaces of the mobile energy storage vehicle which is charged or discharged at the same node cannot exceed the number of the charging and discharging interfaces of the node, and the expression (11) ensures the space-time consistency of charging and discharging or battery replacement of the mobile energy storage vehicle and path planning.
Specifically, the second capacity constraint function for the fixed energy storage system is:
wherein the content of the first and second substances,
to representtIs located at a node at a timegThe state of charge of the stationary energy storage system,
to representtTime-1 at a nodegThe state of charge of the stationary energy storage system,
representing the capacity of the fixed energy storage system;
equations (12), (13) and (14) indicate that the state of charge and the amount of charge or discharge of the fixed energy storage system cannot exceed its capacity.
Specifically, the third capacity constraint function of the hybrid energy storage system is:
wherein, the first and the second end of the pipe are connected with each other,
to representtTime nodegThe maximum charge or discharge amount of.
Equation (15) represents that the charge and discharge amount of the fixed energy storage system and the mobile energy storage vehicle cannot exceed the maximum charge and discharge amount of the node, and equation (16) represents that the charge and discharge amount generated by replacing the battery of the mobile energy storage vehicle cannot exceed the capacity of the fixed energy storage system.
In a specific embodiment, a mobile energy storage system composed of an electric semi-trailer truck and a battery energy storage system, a battery energy storage power station as a fixed energy storage system, and a hybrid energy storage system composed of the battery energy storage power station and the fixed energy storage system are taken as examples, and the optimal operation strategy and economic benefit of the hybrid energy storage system in a power grid are analyzed. Wherein, each parameter of the hybrid energy storage system is shown in table 1:
TABLE 1
In this embodiment, a space-time decision model based on a hybrid energy storage system is applied to a case with 31 power grid nodes, wherein a fixed energy storage system is installed on the power grid nodes, as shown in table 2, examples 1 to 3 analyze that the hybrid energy storage system has higher operation income and lower battery aging amount compared with only mobile energy storage and only fixed energy storage. Specifically, the operation income of the hybrid energy storage system is increased by 6.4% compared with the sum of the operation income of only mobile energy storage and only fixed energy storage, and the battery aging amount is reduced by 19.6% compared with the sum of the battery aging amount of only mobile energy storage and only fixed energy storage.
TABLE 2
Based on the same inventive concept, the invention also provides an operation decision system based on the hybrid energy storage system.
Fig. 2 is a block diagram of an operation decision system based on a hybrid energy storage system according to an exemplary embodiment. As shown in fig. 2, the system includes a building module 201, a building module 202, and a decision module 203.
The establishing module 201 is used for establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes;
the building module 202 is used for building a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and the decision module 203 is used for making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
For specific limitations and beneficial effects of the operation decision system based on the hybrid energy storage system, reference may be made to the above limitations on the operation decision method based on the hybrid energy storage system, and details are not repeated here. The various modules described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 3 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. As shown in fig. 3, the apparatus includes one or more processors 310 and a storage 320, where the storage 320 includes a persistent memory, a volatile memory, and a hard disk, and one processor 310 is taken as an example in fig. 3. The apparatus may further include: an input device 330 and an output device 340.
The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3.
The storage 320, which is a non-transitory computer-readable storage medium, includes a persistent memory, a volatile memory, and a hard disk, and can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the service management method in the embodiment of the present application. The processor 310 executes various functional applications of the server and data processing by executing the non-transitory software programs, instructions and modules stored in the memory 320, that is, implements any one of the above-mentioned operation decision methods based on the hybrid energy storage system.
The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data used as needed or desired, and the like. Further, the memory 320 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from processor 310, which may be connected to a data processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control. The output device 340 may include a display device such as a display screen.
The one or more modules are stored in the memory 320 and, when executed by the one or more processors 310, perform a hybrid energy storage system based operation decision method as shown in fig. 1.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For details of the technology that are not described in detail in this embodiment, reference may be made specifically to the description related to the embodiment shown in fig. 1.
Embodiments of the present invention further provide a non-transitory computer storage medium, where a computer-executable instruction is stored in the computer storage medium, and the computer-executable instruction may execute the authentication method in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (8)
1. An operation decision method based on a hybrid energy storage system is characterized by comprising the following steps:
establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes, wherein the objective function comprises a market income function and a cost function, the objective function is the difference between the market income function and the cost function, and the cost function comprises a transportation cost function, a battery replacement cost function and a battery aging cost function;
the market gain function is:
wherein, in the step (A),REVthe revenue of the market is expressed and,
representing a set of battery network node-time pairs,
representing nodesgIn thattThe node at the time is marginal in electricity prices,
、
respectively representtIs located at a node at a timegThe amount of charge and the amount of discharge of the fixed energy storage system,
a collection of mobile energy storage vehicles is represented,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe amount of charge and discharge at the point;
the cost function is:
wherein, in the step (A),
which represents a cost of transportation and,
indicating the cost of replacing the battery,
represents the cost of battery aging;
constructing a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
2. The method of claim 1,
the transportation cost function is:
，
wherein the content of the first and second substances,
representing a set of paths consisting of nodes of the battery network,
indicating a mobile energy storage vehiclevOn the way
The transportation cost of the above;
as a first decision variable, when the mobile energy storage vehicle passes through the path
When the temperature of the water is higher than the set temperature,
when the mobile energy storage vehicle does not pass through the path
When the temperature of the water is higher than the set temperature,
；
the replacement battery cost function is:
，
wherein the content of the first and second substances,
the cost of unit battery replacement is expressed,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe charge and discharge generated after the battery is replaced;
the battery aging cost function is:
，
wherein, the first and the second end of the pipe are connected with each other,
representing the battery marginal aging cost.
3. The method according to claim 1, wherein the constraints of the plurality of energy storage mode constraints comprise: the method comprises the following steps of mobile energy storage system constraint, fixed energy storage system constraint and hybrid energy storage combined constraint conditions.
4. The method of claim 3,
the mobile energy storage system constraints include: a path constraint function, a first capacity constraint function and a charge and discharge constraint function;
the fixed energy storage system restraint comprises: a second capacity constraint function;
the hybrid energy storage joint constraint comprises: a third capacity constraint function.
5. The method of claim 4,
the path constraint function of the mobile energy storage system is as follows:
，
wherein the content of the first and second substances,
、
respectively represents the path sets of the mobile energy storage vehicle entering and exiting nodes,
、
respectively representing the starting node and the terminating node of a mobile energy storage vehicle,nto representtMoving the node position of the energy storage vehicle at all times;
the first capacity constraint function of the mobile energy storage system is as follows:
，
wherein, the first and the second end of the pipe are connected with each other,
to representtConstantly-moving energy-storage vehiclevThe state of charge of (a) is,
to represent
Constantly-moving energy-storage vehiclevThe state of charge of (a) is,
it is shown that the self-discharge rate,
represents the charge or discharge efficiency;
the charge and discharge constraint function constrained by the mobile energy storage system is as follows:
，
wherein the content of the first and second substances,
is a second decision variable whentConstantly-moving energy-storage vehiclevAt a nodegWhen it is time to charge or discharge or replace the battery,
when it comes totConstantly-moving energy storage vehiclevAt a nodegWhen the battery is not charged, not discharged and not replaced,
，
representing nodesgThe number of mobile energy storage vehicles capable of simultaneously performing charging, discharging and battery replacement;
the second capacity constraint function of the fixed energy storage system is:
，
wherein the content of the first and second substances,
representtIs located at a node at a timegThe state of charge of the stationary energy storage system,
indicating that the time is located at a nodegThe state of charge of the stationary energy storage system,
the third capacity constraint function of the hybrid energy storage system is:
，
wherein the content of the first and second substances,
to representtTime nodegThe maximum charge or discharge amount of.
6. An operation decision system based on a hybrid energy storage system, characterized in that the system comprises:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes, the objective function comprises a market income function and a cost function, the objective function is the difference between the market income function and the cost function, and the cost function comprises a transportation cost function, a battery replacement cost function and a battery aging cost function; the market gain function is:
wherein, in the step (A),REVthe revenue of the market is expressed and,
representing a set of battery network node-time pairs,
representing nodesgIn thattThe node at the time is marginal in electricity prices,
、
respectively representtIs located at a node at a timegThe amount of charge and the amount of discharge of the fixed energy storage system,
a collection of mobile energy storage vehicles is represented,
、
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe amount of charge and discharge at the point;
the cost function is:
wherein, in the step (A),
which represents a cost of transportation and,
indicating the cost of replacing the battery,
represents the cost of battery aging;
the building module is used for building a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and the decision module is used for making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
7. A computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the hybrid energy storage system-based operation decision method according to any one of claims 1 to 5.
8. A computer-readable storage medium storing computer instructions for causing a computer to perform the hybrid energy storage system based operation decision method according to any one of claims 1 to 5.
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