CN112734253A - Energy storage planning method for unit combination embedding, electronic terminal and storage medium - Google Patents
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Abstract
The application provides an energy storage planning method for unit combination embedding, an electronic terminal and a storage medium, wherein the method comprises the following steps: acquiring system parameters of a unit energy storage system; constructing an energy storage scheduling model; embedding the energy storage scheduling model into a unit combination, constructing an energy storage scheduling embedded unit combination model, embedding the energy storage scheduling embedded unit combination model into an energy storage plan, and constructing an energy storage plan model embedded in the unit combination; and solving the energy storage planning model embedded in the unit combination based on the system parameters to obtain an energy storage planning strategy. The method can enable the established energy storage planning model to be better and more accurate, so that the optimal planning decision which accords with the actual situation can be obtained.
Description
Technical Field
The present disclosure relates to the field of energy storage planning technologies, and in particular, to an energy storage planning method, an electronic terminal, and a storage medium for unit combination embedding.
Background
At present, the permeability of new energy technology in an electric power system is continuously improved, and along with the continuous maturity and the continuous reduction of cost of energy storage technology, the application of energy storage receives more attention, so that the development and research of planning and running of energy storage are particularly important.
The existing energy storage planning is not embedded into a unit combination model for integrated solution, lacks the flexibility of an operation level and is difficult to obtain an optimized planning decision. Abundant research results have been developed in energy storage planning for many years at home and abroad, for example, in document 1, "a comprehensive optimization model for planning and operating an energy storage system to improve wind power access" (zheng le, huwei, fall yoga, mincourage, yuanye, high-bonny. china motor engineering declaration, 2014, 34 (16): 2533-. Document 2, "extended planning of a power transmission system considering combined operation of a wind farm and an energy storage system" (zheng jing, wen fuji, li power, wanke, gao.power system automation, 2013,37(01): 135-. Document 3 "robust optimal configuration method for energy storage of power system including multiple wind power plants" (hanxining, lijiaming, wenjinyu, aixiaokang, lijing, ludwighua. the report of the motor engineering in China, 2015,35(09): 2120-. Document 4 "power transmission system extension planning (zheng, fukui, li, etc.) considering combined operation of a wind farm and an energy storage system, power system automation, 2013,37(1): 135-. . In document 5, "combined storage and transmission planning considering wind power acceptance" (Huangying, Liu Bao column, Wang Kun Yu, Aixin. Power grid technology, 2018,42(05): 1480-one 1489), a combined storage and transmission network planning model for improving wind power acceptance is constructed, a solution result covers an optimal configuration position of energy storage and an optimal assumption scheme of power and transmission line, and the combined planning model can realize comprehensive optimization of minimum investment of line and energy storage and minimum wind curtailment of a system.
According to various research results, various patent applications related to energy storage planning strategies exist. For example, patent 1 "power transmission network extension and energy storage configuration joint planning method with large-scale wind power access" (huzechun, wuweiping, lingtai, qiqing, chenping, CN110071505A,2019-07-30.) proposes a power transmission network extension and energy storage configuration joint planning scheme with large-scale wind power access, which first performs annual operation simulation on a power grid, and screens out nodes to be selected for energy storage configuration and lines to be selected for power transmission network extension. After the position is selected, the annual data are clustered, a random planning scene set is constructed, and an initial joint planning scheme is determined through planning. Carrying out annual operation simulation on the basis of the initial joint planning scheme, considering the annual wind power processing change and the influence of energy storage life loss, correcting the initial joint planning scheme, and finally obtaining a joint planning scheme of power transmission network extension and energy storage configuration; patent 2 "energy storage and site selection method based on network loss sensitivity in power transmission network" (guowei, liuqihui, CN109818361A,2019-05-28.) provides an energy storage and site selection method based on network loss sensitivity in power transmission network, which sums and sequences the network loss sensitivity according to time sequence to obtain an optimal energy storage position, and can reduce the solving space during energy storage configuration and improve the calculation efficiency while finding the node with the minimum network loss in the whole network to configure energy storage; patent 3 "a power grid side energy storage system capacity configuration method based on improved simulated annealing algorithm" (zhou xie chao, wang nan, zhao peng xiang, li zheng, bush, wang ice, li jian lin. CN111641220A,2020-09-08.) proposes a power grid side energy storage configuration method based on improved simulated annealing algorithm, which covers both site selection and volume fixing in energy storage planning, and the solving algorithm combines the advantages of the longicorn algorithm and the simulated annealing algorithm, thereby reducing the possibility that the solving result falls into local optimization, and accelerating convergence speed.
However, the traditional method has the following inherent defects:
1. the unit combination is not embedded into the energy storage planning model to be solved, unit combination constraints such as the start-stop state of the unit, the minimum online and offline time of the unit and the like are added into the energy storage planning model, so that the requirements of the actual operation flexibility and the economical efficiency of the power system are met, the established energy storage planning model can be better and more accurate, and the optimal planning decision which meets the actual condition can be solved.
2. The energy storage planning comprises two aspects of site selection and volume fixing, a few schemes of site selection and volume fixing are considered at the same time, the two aspects of site selection and volume fixing are separated, for example, energy storage and volume fixing are carried out under the condition that energy storage site selection is not considered, and the optimal effect of a investment strategy cannot be achieved due to two work of cutting.
Content of application
In view of the above-mentioned shortcomings of the prior art, the present application aims to provide a unit combination embedded energy storage planning method, an electronic terminal and a storage medium for obtaining an optimal decision of energy storage planning.
To achieve the above and other related objects, a first aspect of the present application provides a method for planning energy storage embedded in a unit combination, including: acquiring system parameters of a unit energy storage system; constructing an energy storage scheduling model; embedding the energy storage scheduling model into a unit combination, and constructing an energy storage scheduling embedded unit combination model; embedding the unit combination model embedded with the energy storage scheduling into an energy storage plan, and constructing an energy storage plan model embedded with the unit combination; and solving the energy storage planning model embedded in the unit combination based on the system parameters to obtain an energy storage planning strategy.
In some embodiments of the first aspect of the present application, the system parameters of the unit energy storage system include unit parameters, grid parameters, and energy storage device parameters; the unit parameters comprise one or more of maximum output, minimum output, maximum climbing power, minimum online/offline time, output cost and starting cost; the network frame parameters comprise one or more of node load, line reactance and line transmission capacity; the energy storage device parameters comprise one or more of energy storage device capacity, charging efficiency, discharging efficiency, storage efficiency, maximum charging power and maximum discharging power.
In some embodiments of the first aspect of the present application, the constraints of the energy storage scheduling model include: the energy storage device charging power constraint, the energy storage device discharging power constraint, the energy storage device state of charge timing constraint, and the energy storage device sustainable use constraint.
In some embodiments of the first aspect of the present application, the objective function of the energy storage scheduling embedded unit combination model is to minimize a unit operation cost: min (VC + SC); wherein VC is the output cost of the unit and has the expression of
SC is the unit starting cost, and the expression is
In the formula, omegaGSet of units, Pi,tRepresenting the output of the unit i at time t, ciRepresenting the power generation cost coefficient of the unit i; v. ofi,tIs a variable 0-1 representing the starting action of the unit i at the time t: 0 denotes Start, αiRepresenting the start-up cost factor for unit i.
In some embodiments of the first aspect of the present application, the constraint condition of the energy storage scheduling embedded unit combination model includes one or more of an energy storage device charging power constraint, an energy storage device discharging power constraint, an energy storage device state of charge timing constraint, an energy storage device sustainable use constraint, a unit combination constraint, an energy storage scheduling constraint, a system grid frame constraint, and a system node power balance constraint.
In some embodiments of the first aspect of the present application, the objective function of the energy storage planning model embedded in the plant set is to minimize the sum of the operating cost of the plant set and the investment cost of the energy storage device: min (VC + SC + IC); where IC represents the investment cost of the energy storage device:
wherein omegaSTo indicate a waitSelecting a set of energy storage elements, introducing a variable z of 0-1n,sThe method comprises the steps of representing the commissioning state of energy storage equipment s at a system node n, 1 representing commissioning and RnpvThe net present value of the daily investment of the stored energy is expressed as follows:
wherein r is the presentation rate, CsFor the total cost of the energy storage element s, determined by the energy storage capacity, TcThe life span of the energy storage device.
In some embodiments of the first aspect of the present application, the constraint condition of the unit combination embedded energy storage planning model includes: the method comprises the steps of considering one or more of planned charging power constraint of the energy storage equipment, planned discharging power constraint of the energy storage equipment, planned state-of-charge time sequence constraint of the energy storage equipment, planned sustainable use constraint of the energy storage equipment, constant volume constraint of the energy storage equipment, unit combination constraint and system network frame constraint.
In some embodiments of the first aspect of the present application, the energy storage planning strategy includes a siting strategy and a crew combination strategy.
To achieve the above and other related objects, a second aspect of the present application provides an electronic terminal comprising: at least one memory for storing a computer program; at least one processor, coupled to the memory, is configured to execute the computer program to implement the unit combination embedded energy storage planning method.
To achieve the above and other related objects, a third aspect of the present application provides a storage medium storing program instructions that, when executed, implement a crew-embedded energy storage planning method as described above.
As described above, the energy storage planning method, the electronic terminal and the storage medium for unit combination embedding according to the present application have the following beneficial effects:
1. the invention can realize more accurate unit modeling, thereby accurately reflecting unit operation on a planning level and improving the economical efficiency and flexibility of system operation.
2. The invention realizes the site selection and the volume fixing of the energy storage equipment at the same time, and can reduce the congestion degree of the line by optimizing the position of the energy storage equipment, delay the expansion investment of the system and enhance the economical efficiency and the reliability of the system compared with the energy storage volume fixing of the fracture site selection and the energy storage volume fixing under the fixed feasible position only.
3. The invention models the energy storage planning problem embedded in the unit combination into a mixed integer linear programming model, and can conveniently and quickly obtain the accurate solution of the problem.
Drawings
Fig. 1 is a schematic overall flow chart of a unit combination embedded energy storage planning method according to an embodiment of the present application.
Fig. 2 is a schematic diagram showing relationships among models in the energy storage planning method embedded in the unit combination according to an embodiment of the present application.
Fig. 3 is a diagram illustrating a grid structure of an IEEE RTS 24 node system according to an embodiment of a unit combination embedded energy storage planning method according to an embodiment of the present application.
Fig. 4 shows a total load curve of a system in the energy storage planning method embedded in the unit combination according to an embodiment of the present application.
Fig. 5 shows a curve of the unit output and energy storage operation results obtained by the unit combination embedded energy storage planning method in an embodiment of the present application.
Fig. 6 is a schematic structural diagram of an electronic terminal according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and of being practiced or being carried out in various ways, and it is capable of other various modifications and changes without departing from the spirit of the present application. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in practical implementation, and the type, amount and ratio of the components in practical implementation may be changed arbitrarily, and the layout of the components may be complicated.
The embodiment aims to provide an energy storage planning method for unit combination embedding, an electronic terminal and a storage medium, which are used for obtaining an optimal decision of energy storage planning.
The principle and implementation of the energy storage planning method, the electronic terminal and the storage medium embedded in the unit combination according to the embodiment will be described in detail below, so that those skilled in the art can understand the energy storage planning method, the electronic terminal and the storage medium embedded in the unit combination according to the embodiment without creative work.
Fig. 1 shows a schematic flow chart of a unit combination embedded energy storage planning method according to an embodiment of the present invention. As shown in fig. 1, in this embodiment, the energy storage planning method for unit combination embedding includes steps S100 to S500.
S100, acquiring system parameters of a unit energy storage system;
s200, constructing an energy storage scheduling model;
step S300, embedding the energy storage scheduling model into a unit combination, and constructing an energy storage scheduling embedded unit combination model;
step S400, embedding the unit combination model embedded with the energy storage scheduling into an energy storage plan, and constructing an energy storage plan model embedded with the unit combination;
and S500, solving the energy storage planning model embedded in the unit combination based on the system parameters to obtain an energy storage planning strategy.
The following describes steps S100 to S500 of the energy storage planning method for unit combination embedding according to this embodiment in detail.
And S100, acquiring system parameters of the unit energy storage system.
In this embodiment, the system set, the rack, the energy storage device parameters, and the load data of a typical 24-hour system are obtained. In other words, in this embodiment, the system parameters of the unit energy storage system include unit parameters, rack parameters, and energy storage device parameters.
The unit parameters comprise one or more of maximum output, minimum output, maximum climbing power, minimum online/offline time, output cost and starting cost; the network frame parameters comprise one or more of node load, line reactance and line transmission capacity; the energy storage device parameters comprise one or more of energy storage device capacity, charging efficiency, discharging efficiency, storage efficiency, maximum charging power and maximum discharging power.
In this embodiment, the system parameters of the unit energy storage system are obtained as shown in table 1 below.
TABLE 1 model required System parameters
And S200, constructing an energy storage scheduling model.
Specifically, in this embodiment, the constraint conditions of the energy storage scheduling model include: the energy storage device charging power constraint, the energy storage device discharging power constraint, the energy storage device state of charge timing constraint, and the energy storage device sustainable use constraint.
In this embodiment, the charging power of the energy storage device is constrained:
and (3) discharge power constraint of the energy storage equipment:
the charging and discharging power of the energy storage element can be limited within a reasonable range. Wherein the content of the first and second substances,
respectively representing the charging and discharging power of the energy storage element s to be selected at the system node n at the moment t,
respectively representing the upper limits of the charging power and the discharging power of the energy storage element s to be selected.
In this embodiment, the energy storage device state of charge timing constraints are as follows:
the state of charge of the energy storage element at the moment t, the charging and discharging power at the moment, the state of charge at the previous moment and the storage
In this embodiment, the energy storage device may be continuously used as follows:
in order to make the energy storage device continuously available, the energy storage states of the energy storage devices at the beginning and the end of the scheduling period need to be consistent:
in the formula (I), the compound is shown in the specification,
Socn,s,Trespectively representing the state of charge of the energy storage element at the beginning and the end of the scheduling period.
Step S300, as shown in fig. 2, embedding the energy storage scheduling model into the unit combination, and constructing an energy storage scheduling embedded unit combination model.
In this embodiment, the objective function of the energy storage scheduling embedded unit combination model is to minimize the unit operation cost: min (VC + SC); wherein VC is the output cost of the unit and has the expression of
SC is the unit starting cost, and the expression is
In the formula, omegaGSet of units, Pi,tRepresenting the output of the unit i at time t, ciRepresenting the power generation cost coefficient of the unit i; v. ofi,tIs a variable 0-1 representing the starting action of the unit i at the time t: 0 denotes Start, αiRepresenting the start-up cost factor for unit i.
In this embodiment, the constraint conditions of the unit combination model embedded in the energy storage scheduling include one or more of a charging power constraint of the energy storage device, a discharging power constraint of the energy storage device, a charge state timing constraint of the energy storage device, a sustainable use constraint of the energy storage device, a unit combination constraint, an energy storage scheduling constraint, a system grid frame constraint, and a system node power balance constraint.
The energy storage device charging power constraint, the energy storage device discharging power constraint, the energy storage device state-of-charge time sequence constraint, and the energy storage device sustainable use constraint are already described in step S200, and are not described herein again. The following further explains the unit combination constraint, the energy storage scheduling constraint, the system grid frame constraint and the system node power balance constraint in the step.
In this embodiment, the unit combination constraint includes:
unit output restraint:
unit climbing restraint: pi,t-Pi,t-1≤RUi,i∈ΩG；-Pi,t+Pi,t-1≤RDi,i∈ΩG；
Minimum online and offline time constraints of the unit:
logically constraining the on-off action and the on-line state of the unit: u. ofi,t-ui,t-1＝vi,t-wi,t,i∈ΩG。
Wherein, Pi,tRepresents the output of the unit i at the moment t, ui,tTo indicate the unit i isthe 0-1 variable of the online state at time t: 1 represents online, vi,t、wi,tRespectively representing the variables of 0-1 of the starting and stopping actions of the unit: 1 represents an action. And respectively representing the upper and lower output bounds of the unit i, the maximum upper and lower climbing powers of the unit i, and the minimum online and offline time of the unit i.
In this embodiment, the energy storage scheduling constraint includes:
the energy storage scheduling constraint comprises energy storage equipment charging and discharging power constraint:
state of charge timing constraints:
the electric power system adopts direct current power flow modeling, and in this embodiment, the system grid structure constraint includes:
and (3) node phase angle constraint: -pi ≦ θn,t≤π,n∈ΩN；
And (3) direct current power flow constraint: pfl,t＝(θe,t-θs,t)bl,l∈ΩL；
And (3) power transmission line capacity constraint:
l∈ΩL
wherein omegaNRepresents the set of system nodes, ΩLRepresenting the set of transmission lines of the system, thetan,tRepresenting the phase angle, pf, of the system node n at time tl,tRepresenting the active power transmitted by the transmission line l of the system at time t, thetae,t、θs,tRepresenting the phase angle of the terminating and starting nodes of line i at time t,
blindicating the capacity and admittance of the line l.
In this embodiment, the power balance constraint of the system node is:
wherein L isn,tRepresenting the load of node n at time t, An,iRepresenting a node-set incidence matrix, if a set i is connected to a node n, then An,i＝1，Bn,lRepresenting a line-node association matrix, if the initial node of a line l is n, Bn,lIf the termination node of line l is n, B is-1n,l1, otherwise Bn,l＝0。
Step S400, as shown in fig. 2, embedding the unit combination model embedded with the energy storage scheduling into the energy storage plan, and constructing the unit combination embedded energy storage plan model.
In the unit combination model embedded with energy storage scheduling established in step S300, energy storage planning, namely site selection and volume determination, is further considered to construct the unit combination model embedded with energy storage scheduling.
Specifically, in this embodiment, the objective function of the energy storage planning model embedded in the unit combination is to minimize the sum of the unit operation cost and the investment cost of the energy storage device: min (VC + SC + IC); wherein IC represents the investment cost of the energy storage device:
wherein omegaSRepresenting a set of energy storage elements to be selected, introducing a variable z of 0-1n,sThe method comprises the steps of representing the commissioning state of energy storage equipment s at a system node n, 1 representing commissioning and RnpvThe net present value of the daily investment of the stored energy is expressed as follows:
wherein r is the presentation rate, CsFor the total cost of the energy storage element s, determined by the energy storage capacity, TcThe life span of the energy storage device.
In some embodiments, the constraint conditions of the energy storage planning model embedded in the unit combination include: the method comprises the steps of considering one or more of planned charging power constraint of the energy storage equipment, planned discharging power constraint of the energy storage equipment, planned charging state time sequence constraint of the energy storage equipment, planned sustainable use constraint of the energy storage equipment, energy storage equipment constant volume constraint, unit combination constraint and system network frame constraint.
Specifically, in this embodiment, the energy storage planning constraint is to constrain the charging power of the energy storage device, the discharging power of the energy storage device, the time sequence constraint of the state of charge of the energy storage device, and the sustainable use constraint of the energy storage device in step S200, and the constraints are modified as follows:
considering a planned energy storage device charging power constraint:
considering the planned energy storage device discharge power constraint:
wherein, the variable z is 0 to 1n,sRepresenting the commissioning condition of the s-th energy storage device of the node n, and z when the energy storage device is not commissionedn,sWhen the charge and discharge power is 0, the charge and discharge power of the stored energy is limited to 0.
Considering a planned energy storage device state of charge timing constraint:
considering planned energy storage device sustainable use constraints:
energy storage equipment constant volume restraint:
wherein the content of the first and second substances,
different subscripts s correspond to energy storage elements with different capacities, and for the node n, only an energy storage element with a certain capacity can be selected, so that the constraint condition can realize the energy storage constant volume of the node n.
In this embodiment, the unit combination constraint of step S400 is the same as the unit combination constraint and the system grid constraint of step S300, and is not described herein again.
And S500, solving the energy storage planning model embedded in the unit combination based on the system parameters to obtain an energy storage planning strategy.
And substituting the system data obtained in the step S100 into the energy storage planning model embedded in the unit combination established in the step S400 and solving to obtain a location and volume fixing strategy and a unit combination strategy of system energy storage. The energy storage planning strategy comprises a site selection and volume fixing strategy and a unit combination strategy.
The energy storage planning method embedded in the unit combination according to this embodiment is described below with specific examples.
As shown in fig. 3, the power system illustrated in this embodiment includes 32 generator sets, 37 lines, and 17 load nodes. In this embodiment, the total load data of the system is shown in fig. 4, the modified system includes 19 units, the data of the system is shown in table 3, each node to-be-selected energy storage element includes 4 capacity levels, which are respectively 40MW, 80MW, 120MW, and 160MW, and the parameters of the energy storage elements are the same, as shown in table 2. Let the pasting rate r be 5%.
TABLE 2 System Unit data
TABLE 3 basic parameters of the candidate energy storage device
The system data is substituted into the energy storage planning model embedded in the unit combination established in step S400 to be solved, the obtained energy storage planning strategy is shown in table 4, the unit combination strategy is shown in table 5, and the specific contribution of the unit output and the energy storage charge-discharge power to the load at each moment is shown in fig. 5.
As can be seen from table 4, the system decides to plan the energy storage devices at nodes 3, 6, 8, 11, 16, 18, 20, 23. Table 5 shows the unit combination strategy, and it can be seen that, in the period of 0 to 5, the system load is low, and the system only starts part of the units 9, 10, 11, 14, 15, 16, and 19 with low cost to supply power to meet the requirements of energy storage and charging and load; in the period of 6-8, along with the continuous increase of the system load, the units 3, 4, 7, 8, 12, 13, 17 and 18 with higher cost need to be further started to meet the system load, in the period of 9-13, the system load reaches the peak, the units 1, 2, 5 and 6 with the highest cost are further started to meet the peak load of the system, the cost of the unit is highest, the operation is most flexible, namely the minimum on-line time and the minimum off-line time are small, and therefore the unit can be quickly closed after the system load begins to decline at the moment of 13. After the second load peak of the system during the 19-21 period, the relatively costly unit is shut down.
As can be seen from fig. 4, the charging of the energy storage device mainly occurs in the load valley period, i.e. the time period 0-4, the time period 14-18, and the time period 23-24, at this time, the system load is low, the electric energy cost is low, i.e. the power supply of the unit with low output cost is low, so the charging is more economical; the discharge of the energy storage device mainly occurs in the load peak period, namely 9-12 time period and 19-20 time period, at the time, the energy supply cost of the system is extremely high, and the energy storage can release the low-cost electric energy stored by the energy storage device to meet the load requirement of the system so as to reduce the energy supply cost of the system. The characteristic of energy storage, namely peak clipping and valley filling, can improve the flexibility and the economical efficiency of system operation, and has good social benefit.
Table 4 energy storage planning results
TABLE 5 Unit Online State
Fig. 6 is a schematic structural diagram of an electronic terminal in the embodiment of the present application.
The electronic terminal includes: at least one memory 1002 for storing computer programs; at least one processor 1003, coupled to the display 1001 and the memory 1002, is configured to run the computer program to implement the steps of the energy storage planning method embedded in the unit assembly.
The memory 1102 is connected with the processor 1101 through a system bus and completes mutual communication, the memory 1102 is used for storing a computer program, and the processor 1101 is used for running the computer program, so that the electronic terminal executes the energy storage planning method embedded in the unit combination. The energy storage planning method for unit combination embedding has been described in detail above, and is not described herein again.
It should be noted that the above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other devices (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor 1101 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
The electronic terminal is, for example, a field device controller, such as an arm (advanced RISC machines) controller, an fpga (field Programmable Gate array) controller, a soc (system on chip) controller, a dsp (digital Signal processing) controller, or a mcu (micro controller unit) controller. The electronic terminal may also be, for example, a computer that includes components such as memory, a memory controller, one or more processing units (CPUs), a peripheral interface, RF circuitry, audio circuitry, speakers, a microphone, an input/output (I/O) subsystem, a display screen, other output or control devices, and external ports; the computer includes, but is not limited to, Personal computers such as desktop computers, notebook computers, tablet computers, smart phones, smart televisions, Personal Digital Assistants (PDAs), and the like. In other embodiments, the electronic terminal may also be a server, and the server may be arranged on one or more entity servers according to various factors such as functions, loads, and the like, or may be formed by a distributed or centralized server cluster, which is not limited in this embodiment. Therefore, in this embodiment, the electronic terminal may be, for example, a fixed terminal, such as a controller, a server, a desktop, or the like; or a mobile terminal, such as a notebook computer, a smart phone, or a tablet computer.
In addition, the present embodiment also provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the unit combination embedded energy storage planning method. The energy storage planning method for unit combination embedding has been described in detail above, and is not described herein again.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When the program is executed, executing the steps of the energy storage planning method embedded in the unit combination; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
In conclusion, the invention can realize more accurate unit modeling, thereby accurately reflecting unit operation on the planning level and improving the economical efficiency and flexibility of system operation; the invention realizes the site selection and the volume fixing of the energy storage equipment at the same time, and compared with the energy storage volume fixing of the fracture site selection and the energy storage volume fixing under the fixed feasible position, the invention can reduce the congestion degree of the line by optimizing the position of the energy storage equipment, delay the expansion investment of the system and enhance the economical efficiency and the reliability of the system; according to the invention, the energy storage planning problem of the embedded unit combination is modeled into a mixed integer linear programming model, so that an accurate solution of the problem can be conveniently and rapidly obtained. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present disclosure be covered by the claims which follow.
Claims (10)
1. An energy storage planning method for unit combination embedding is characterized in that: the method comprises the following steps:
acquiring system parameters of a unit energy storage system;
constructing an energy storage scheduling model;
embedding the energy storage scheduling model into a unit combination, and constructing an energy storage scheduling embedded unit combination model;
embedding the unit combination model embedded with the energy storage scheduling into an energy storage plan, and constructing an energy storage plan model embedded with the unit combination;
and solving the energy storage planning model embedded in the unit combination based on the system parameters to obtain an energy storage planning strategy.
2. The unit combination embedded energy storage planning method according to claim 1, characterized in that: the system parameters of the unit energy storage system comprise unit parameters, net rack parameters and energy storage equipment parameters; the unit parameters comprise one or more of maximum output, minimum output, maximum climbing power, minimum online/offline time, output cost and starting cost; the network frame parameters comprise one or more of node load, line reactance and line transmission capacity; the energy storage device parameters comprise one or more of energy storage device capacity, charging efficiency, discharging efficiency, storage efficiency, maximum charging power and maximum discharging power.
3. The unit combination embedded energy storage planning method according to claim 1, characterized in that: the constraint conditions of the energy storage scheduling model comprise: the energy storage device charging power constraint, the energy storage device discharging power constraint, the energy storage device state of charge timing constraint, and the energy storage device sustainable use constraint.
4. The unit combination embedded energy storage planning method according to claim 1, characterized in that: the objective function of the energy storage scheduling embedded unit combination model is to minimize the unit operation cost:
min(VC+SC)；
wherein VC is the output cost of the unit and has the expression of
SC is the unit starting cost, and the expression is
In the formula, omegaGSet of units, Pi,tRepresenting the output of the unit i at time t, ciRepresenting the power generation cost coefficient of the unit i; v. ofi,tIs a variable 0-1 representing the starting action of the unit i at the time t: 0 denotes Start, αiRepresenting the start-up cost factor for unit i.
5. The unit combination embedded energy storage planning method according to claim 4, characterized in that: the constraint conditions of the unit combination model embedded in the energy storage scheduling comprise one or more of energy storage equipment charging power constraint, energy storage equipment discharging power constraint, energy storage equipment state-of-charge time sequence constraint, energy storage equipment sustainable use constraint, unit combination constraint, energy storage scheduling constraint, system grid frame constraint and system node power balance constraint.
6. The unit combination embedded energy storage planning method according to claim 4, characterized in that: the objective function of the energy storage planning model embedded in the unit combination is the sum of the minimum unit operation cost and the investment cost of the energy storage equipment:
min(VC+SC+IC)；
where IC represents the investment cost of the energy storage device:
wherein omegaSRepresenting a set of candidate energy storage elements, and introducing 0-1Variable zn,sThe method comprises the steps of representing the commissioning state of energy storage equipment s at a system node n, 1 representing commissioning and RnpvThe net present value of the daily investment of the stored energy is expressed as follows:
wherein r is the presentation rate, CsFor the total cost of the energy storage element s, determined by the energy storage capacity, TcThe life span of the energy storage device.
7. The unit combination embedded energy storage planning method according to claim 6, characterized in that: the constraint conditions of the energy storage planning model embedded in the unit combination comprise: the method comprises the steps of considering one or more of planned charging power constraint of the energy storage equipment, planned discharging power constraint of the energy storage equipment, planned state of charge time sequence constraint of the energy storage equipment, planned sustainable use constraint of the energy storage equipment, constant volume constraint of the energy storage equipment, unit combination constraint and system grid constraint.
8. The unit combination embedded energy storage planning method according to claim 1, characterized in that: the energy storage planning strategy comprises a site selection and volume fixing strategy and a unit combination strategy.
9. An electronic terminal, characterized by: the method comprises the following steps:
at least one memory for storing a computer program;
at least one processor, coupled to the memory, for executing the computer program to implement the unit combination embedded energy storage planning method according to any of claims 1 to 8.
10. A storage medium storing program instructions, characterized in that: the program instructions when executed implement a crew-integrated embedded energy storage planning method according to any one of claims 1 to 8.
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