Energy storage planning method and system taking unit combination as operation inspection

Abstract

The invention is suitable for the technical field of energy storage planning, and provides an energy storage planning method and system with unit combination as operation inspection. The method comprises the following steps: constructing a target function of an energy storage planning model by taking unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation, maintenance and replacement cost as target parameters; determining constraint conditions of the energy storage planning model, wherein the constraint conditions comprise a power balance constraint, a unit operation constraint, an energy storage operation constraint and a carbon emission constraint; and determining a target energy storage planning model based on the target function and the constraint condition, and carrying out solving by using a binary algorithm to obtain a target energy storage planning scheme taking the unit combination as operation inspection. The optimal target energy storage planning scheme is obtained through unit combination operation inspection, and cost minimization and resource utilization rate improvement are realized while system operation requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to an energy storage planning method and system using unit combination as an operation test. Background Art

[0002] At present, the penetration rate of new energy technologies in the power system is constantly increasing. With the continuous maturity of energy storage technology and the continuous reduction of costs, the application of energy storage has received more attention. Therefore, it is particularly important to study the planning and operation of energy storage. Energy storage planning refers to the comprehensive and systematic planning of the scale, layout, type selection and operation strategy of energy storage systems in energy systems.

[0003] At present, there are some significant defects in energy storage planning. On the one hand, the existing planning process misses the key link and does not embed the unit combination into the energy storage planning model for solution. When the power system is actually operating, the unit combination constraints such as the start and stop status of the unit and the minimum online and offline time affect the flexibility of the system. When the unit is started and shut down affects whether the power supply can accurately match the dynamic changes of the load. Ignoring these factors makes it impossible to efficiently store electricity when the unit has surplus power, or to smoothly release energy during peak power consumption and unit exhaustion, resulting in resource mismatch. On the other hand, the energy storage plan does not consider the linkage effect of site selection and capacity determination. Site selection determines the relative position of energy storage facilities and load centers and power supply sides, affecting transmission losses and response speed; capacity determination affects whether the energy storage capacity can match the peak and valley difference of load and the volatility of renewable energy. Failure to combine treatment is very likely to lead to investment mistakes. The location may be selected well, but the advantages of energy storage cannot be fully utilized due to improper capacity planning, or the capacity is adapted, but the investment strategy cannot achieve the optimal effect due to site selection errors.

[0004] In view of this, there is a need for an energy storage planning method and system using unit combination as an operation test. Summary of the invention

[0005] The embodiments of the present application provide a method and system for energy storage planning using unit combination as an operation test, which is used to solve the problem of low implementation effect of energy storage planning strategy.

[0006] A first aspect of an embodiment of the present application provides an energy storage planning method using unit combination as an operation test, comprising:

[0007] An objective function construction unit is used to construct an objective function of an energy storage planning model using unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost, and equipment operation and maintenance replacement cost as target parameters;

[0008] A constraint determination unit, used to determine the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraints, unit operation constraints, energy storage operation constraints, and carbon emission constraints;

[0009] The target energy storage planning scheme determining unit is used to determine the target energy storage planning model based on the objective function and the constraint conditions, and solve it using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test.

[0010] Furthermore, the objective function of the energy storage planning model is constructed with the unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters, including:

[0011] Z=C g +C inv +C r +C e +C ct +C om

[0012] Where: Z is the minimum cost of energy storage planning, C g is the power generation cost of the unit, C inv is the energy storage investment cost, C r is the system reliability cost, C e is the pollutant emission cost, C ct is the carbon emission cost, C om It is the equipment operation and maintenance replacement cost.

[0013] Furthermore, the objective function of the energy storage planning model is constructed with the unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters, including:

[0014] Unit power generation cost C g :

[0015]

[0016] in: is the fuel cost of the i-th unit at time t, U i is the single startup cost of unit i, u it U i is the total start-up and shutdown cost of unit i at time t;

[0017] Energy storage investment cost C inv :

[0018]

[0019] Where: cvj is the unit capacity investment cost of the jth selected energy storage site, y j is the variable for selecting site j, c pj is the unit power investment cost of the jth selected energy storage site, E j is the corresponding energy storage capacity, P dj is the rated power;

[0020] System reliability cost C r :

[0021]

[0022] Where: c r is the reliability cost coefficient, R t is the system reliability index at time t;

[0023] Pollutant emission cost C e :

[0024]

[0025] in: is the pollutant emission of unit i at time t, c p is the unit pollutant emission cost;

[0026] Carbon emission cost C ct :

[0027]

[0028] in: is the carbon emissions of unit i at time t, P ct is the carbon trading price;

[0029] Equipment maintenance and replacement cost C om :

[0030]

[0031] Where: c mj is the unit capacity operation and maintenance cost of the jth selected energy storage site, c rep is the replacement cost coefficient, P it is the power generation of unit i at time t.

[0032] Furthermore, the constraint conditions of the energy storage planning model are determined, and the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, including:

[0033] Power balance constraints:

[0034]

[0035] Where: P cj,t and P dj,t are the charging and discharging power of the energy storage in time period t, L t is the system load demand, P lt For system loss.

[0036] Furthermore, the constraint conditions of the energy storage planning model are determined, and the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, including:

[0037] Unit operation constraints:

[0038]

[0039] ―R di ≤P it ―P i(t―1) ≤R ui

[0040] z it ≥u it ―u i(t―1)

[0041]

[0042] in: and are the minimum and maximum power generation of unit i, R di is the downward climbing rate limit of unit i, R ui is the upward climbing rate limit of unit i, is the minimum startup time of unit i, z it It is an auxiliary variable, indicating whether the unit has just started.

[0043] Furthermore, the constraint conditions of the energy storage planning model are determined, and the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, including:

[0044] Energy storage operation constraints:

[0045]

[0046] in: and are the upper limits of charging and discharging power of energy storage at site j, and are the charging and discharging power of energy storage, and are the minimum and maximum values ​​of energy storage capacity respectively;

[0047] Carbon emission constraints:

[0048]

[0049] in: It is the carbon emission limit of the power system within a certain dispatching cycle.

[0050] Furthermore, the target energy storage planning model is determined based on the objective function and the constraint conditions, and is solved using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test, including:

[0051] Inputting the objective function and the parameters in the constraint conditions into a mixed integer programming solver;

[0052] The solver iteratively solves binary and continuous variables based on branch and bound and cutting plane algorithms;

[0053] The target energy storage planning scheme is output based on the result of the iterative solution, and the target energy storage planning scheme includes energy storage site selection, capacity configuration, unit start and stop plan, and charging and discharging strategy.

[0054] A second aspect of the embodiment of the present application provides an energy storage planning system using unit combination as an operation test, including:

[0055] An objective function construction unit is used to construct an objective function of an energy storage planning model using unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost, and equipment operation and maintenance replacement cost as target parameters;

[0056] A constraint determination unit, used to determine the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraints, unit operation constraints, energy storage operation constraints, and carbon emission constraints;

[0057] The target energy storage planning scheme determining unit is used to determine the target energy storage planning model based on the objective function and the constraint conditions, and solve it using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test.

[0058] A third aspect of an embodiment of the present application provides a computer device, including:

[0059] memories, transceivers, processors, and bus systems;

[0060] Wherein, the memory is used to store programs;

[0061] The processor is used to execute the program in the memory, including executing the energy storage planning method with unit combination as the operation test as described above;

[0062] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0063] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute the steps of the energy storage planning method with unit combination as an operation test as described above.

[0064] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0065] The present invention constructs an objective function by comprehensively considering the energy storage planning model of various costs, including the unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost, and comprehensively considers the economic and environmental factors affecting the energy storage system; secondly, it determines the constraints including power balance, unit operation, energy storage operation and carbon emission to ensure the feasibility of the planning scheme in actual operation; finally, based on the objective function and constraints, it establishes a target energy storage planning model, and uses a binary algorithm to solve it. By verifying the combined operation of the units, the optimal target energy storage planning scheme is obtained, which minimizes costs and improves resource utilization while meeting the system operation requirements.

[0066] Other advantages, objectives, and features of the present invention will be set forth in part in the following description, and in part will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The figure is a flow chart of an embodiment of an energy storage planning method using unit combination as an operation test in the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] In this embodiment, the energy storage planning method with unit combination as operation test is used to meet the system operation requirements while minimizing costs and improving resource utilization. In this embodiment, the implementation method can be implemented in the system, on the server, or on the terminal, without specific limitation.

[0070] Embodiment 1

[0071] See also Figure 1, an embodiment of an energy storage planning method using unit combination as an operation test in the present invention comprises the following steps:

[0072] S11. Construct the objective function of the energy storage planning model with unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters;

[0073] In this embodiment, it is assumed that the dispatch cycle of the power system is divided into T time periods, and there are I generator groups and J energy storage sites to be selected in the system. Since the present invention adopts a binary algorithm to solve the model, the variables are binarized here, and the binary capacity is directly converted into the energy storage construction state, thereby constraining the energy storage charging and discharging, forming a coordinated integration of energy storage planning and scheduling, as follows:

[0074] Unit start and stop state variables: For each unit i=1,2,…,I, in each time period t=1,2,…,T, define a binary variable u it To indicate the start and stop status of the unit. it =1 means that unit i starts in time period t, u it = 0 means shutdown. For example, if there are 3 units and the dispatch cycle is divided into 4 periods, there will be 12 u it Energy storage site selection variable: For J candidate energy storage sites, define a binary variable y j (j=1,2,…,J) to indicate whether to select the site to build energy storage facilities. j =1 means select site j, y j =0 means not selected.

[0075] Other possible binarizations of integer variables:

[0076] Converting integer variables into 0-1 variables, the binary decomposition method of energy storage capacity decision is expressed as:

[0077]

[0078] Among them, the integer variable z n represents the number of unit energy storage units invested and built at system node n, z n After binary conversion, it is represented as a 0-1 variable The weighted sum of n Index z n The corresponding energy storage capacity for the lth bit in binary is units, the maximum value is L n ; That is to decide n Whether the lth bit in binary is taken into account, 1 means investment and construction.

[0079] Objective function:

[0080] Z=C g +C inv +C r +C e +C ct +C om

[0081] Where: Z is the minimum cost of energy storage planning, C g is the power generation cost of the unit, C inv is the energy storage investment cost, C r is the system reliability cost, C e is the pollutant emission cost, C ct is the carbon emission cost, C om It is the equipment operation and maintenance replacement cost.

[0082] Specifically, for the i-th unit in time period t, the fuel cost is often related to the power generation. Assuming that its cost tree forms a quadratic function, where a i 、b i and c i is the fuel cost coefficient of unit i, which is related to the unit type and fuel characteristics, P it is the power generation of unit i at time t, then the total fuel cost is: The following power generation cost is obtained.

[0083] Unit power generation cost C g :

[0084]

[0085] in: is the fuel cost of the i-th unit at time t, U i is the single startup cost of unit i, u it U i is the total start-up and shutdown cost of unit i at time t.

[0086] Energy storage investment cost C inv :

[0087]

[0088] Where: c vj is the unit capacity investment cost of the jth selected energy storage site, y j is the variable for selecting site j, c pj is the unit power investment cost of the jth selected energy storage site, E j is the corresponding energy storage capacity, P dj is the rated work.

[0089] System reliability cost C r :

[0090]

[0091] Where: c r is the reliability cost coefficient, R t is the system reliability index at time t.

[0092] Pollutant emission cost C e :

[0093]

[0094] in: is the pollutant emission of unit i at time t, c p is the unit pollutant emission cost.

[0095] Carbon emission cost C ct :

[0096]

[0097] in: is the carbon emissions of unit i at time t, P ct The carbon trading price.

[0098] Equipment maintenance and replacement cost C om :

[0099]

[0100] Where: c mj is the unit capacity operation and maintenance cost of the jth selected energy storage site, c rep is the replacement cost coefficient, P it is the power generation of unit i at time t, where y j Ensure that only selected energy storage sites are calculated for operation and maintenance costs.

[0101] S12. Determine the constraints of the energy storage planning model, where the constraints include power balance constraints, unit operation constraints, energy storage operation constraints, and carbon emission constraints;

[0102] At any time period t, the sum of the power generation of all units and the charging and discharging power of the energy storage system must be equal to the sum of the system load demand and the system loss, that is:

[0103]

[0104] Where: P cj,t and P dj,t are the charging and discharging power of the energy storage in time period t, L t is the system load demand, Plt For system loss.

[0105] The unit operation constraints include power limit, ramp rate and minimum start and stop time, as follows:

[0106] Power Limitation: Climbing rate: -R di ≤P it ―P i(t―1) ≤R ui ; Minimum start-up and shutdown time: The minimum time limit after the unit is started and shut down must be followed to avoid frequent start-ups and shutdowns. Suppose the minimum start-up time of unit i is Introduce auxiliary variable z it (also a 0-1 variable) to indicate whether the unit has just started: z it ≥u it ―u i(t―1) ; in and are the minimum and maximum power generation of unit i, R di is the downward climbing rate limit of unit i, R ui is the upward climbing rate limit of unit i, is the minimum startup time of unit i, z it It is an auxiliary variable, indicating whether the unit has just started.

[0107] Energy storage operation approx.

[0108]

[0109]

[0110] in: and are the upper limits of charging and discharging power of energy storage at site j, and are the charging and discharging power of energy storage, and are the minimum and maximum values ​​of the energy storage capacity respectively.

[0111] Carbon emission constraints:

[0112]

[0113] in; It is the carbon emission limit of the power system within a certain dispatching cycle.

[0114] In addition, there are also unit-energy storage coupling constraints: the charging and discharging strategy of energy storage must be consistent with the unit's power generation plan and load fluctuations. When the unit fails or the output is insufficient, the energy storage responds in time; the unit start and stop operations must also be combined with the current state of the energy storage to ensure a smooth transition of power. Through logical association constraints, for example, when the unit's power generation is insufficient, the energy storage needs to increase the discharge strength. Specific constraints are constructed according to actual conditions.

[0115] S13. Determine the target energy storage planning model based on the objective function and constraints, and solve it using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test.

[0116] This example includes the following:

[0117] 1. Input the parameters of the objective function and constraints into the mixed integer programming solver;

[0118] Here, CPLEX is selected as the mixed integer programming solver, and the system objective function and constraint parameters are input into the solver, including the unit parameters (a i 、b i 、c i , U i , R di , R ui , etc.), energy storage parameters (c vj 、c pj , etc.), load forecast data L t 、System loss data P lt , cost coefficient (c r 、c p , P ct 、c mj 、c rep etc.) and carbon emission limits wait.

[0119] 2. The solver iteratively solves binary and continuous variables based on branch and bound and cutting plane algorithms;

[0120] The solver is based on branch and bound, cutting plane and other algorithms, and solves binary variables (u it ,y j 、z it etc.) and continuous variables (P it , P cj,t , P dj,t 、c mj 、c repIn each iteration, the solver checks whether all constraints are met based on the current variable values ​​and calculates the objective function value. By continuously adjusting the variable values, the optimal solution that meets the constraints and minimizes the objective function is gradually found.

[0121] 3. Output the target energy storage planning scheme based on the results of iterative solution. The target energy storage planning scheme includes energy storage site selection, capacity configuration, unit start and stop plan, and charging and discharging strategy.

[0122] Output the optimal energy storage planning scheme, including energy storage site selection, that is, j Determine which energy storage sites to select and their capacity configuration, i.e. the E of each selected energy storage site. j , unit start-up and shutdown plan, that is, the u of each unit i in each period t it , and the charging and discharging strategy, that is, P of each energy storage site j in each time period t cj,t and P dj,t .

[0123] The above-mentioned embodiment realizes the site selection and capacity determination of energy storage equipment at the same time. Compared with the energy storage capacity determination with separated site selection and the energy storage capacity determination under only fixed feasible locations, the location of energy storage equipment can be optimized to reduce the congestion of the line, delay the system expansion investment, and enhance the economy and reliability of the system.

[0124] Embodiment 2

[0125] An embodiment of an energy storage planning system using unit combination as an operation test in the present invention comprises the following steps:

[0126] An objective function construction unit is used to construct an objective function of an energy storage planning model using unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost, and equipment operation and maintenance replacement cost as target parameters;

[0127] A constraint determination unit, used to determine the constraint conditions of the energy storage planning model, the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint;

[0128] The target energy storage planning scheme determination unit is used to determine the target energy storage planning model based on the objective function and constraint conditions, and solve it using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test.

[0129] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0130] Embodiment 3

[0131] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0132] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0133] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0135] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for energy storage planning based on unit combination as operation test, characterized in that: include: The objective function of the energy storage planning model is constructed with unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters; Determining constraints of the energy storage planning model, wherein the constraints include power balance constraints, unit operation constraints, energy storage operation constraints, and carbon emission constraints; A target energy storage planning model is determined based on the objective function and the constraint conditions, and is solved using a binary algorithm to obtain a target energy storage planning scheme using unit combination as an operation test.

2. The energy storage planning method based on unit combination as operation test according to claim 1 is characterized in that: The objective function of the energy storage planning model is constructed by taking the unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters, including: Z=C g +C inv +C r +C e +C ct +C om Where: Z is the minimum cost of energy storage planning, C g is the power generation cost of the unit, C inv is the energy storage investment cost, C r is the system reliability cost, C e is the pollutant emission cost, C ct is the carbon emission cost, C om It is the equipment operation and maintenance replacement cost.

3. The energy storage planning method based on unit combination as operation test according to claim 2 is characterized in that: The objective function of the energy storage planning model is constructed by taking the unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and maintenance replacement cost as target parameters, including: Unit power generation cost C g : in: is the fuel cost of the i-th unit at time t, U i is the single startup cost of unit i, u it U i is the total start-up and shutdown cost of unit i at time t; Energy storage investment cost C inv : Where: c vj is the unit capacity investment cost of the jth selected energy storage site, y j is the variable for selecting site j, c pj is the unit power investment cost of the jth selected energy storage site, E j is the corresponding energy storage capacity, P dj is the rated power; System reliability cost C r : Where: c r is the reliability cost coefficient, R t is the system reliability index at time t; Pollutant emission cost C e : in: is the pollutant emission of unit i at time t, c p is the unit pollutant emission cost; Carbon emission cost C ct : in: is the carbon emissions of unit i at time t, P ct is the carbon trading price; Equipment maintenance and replacement cost C om : Where: c mj is the unit capacity operation and maintenance cost of the jth selected energy storage site, c rep is the replacement cost coefficient, P it is the power generation of unit i at time t.

4. The energy storage planning method based on unit combination as operation test according to claim 1 is characterized in that: The determining of the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, includes: Power balance constraints: Where: P cj,t and P dj,t are the charging and discharging power of the energy storage in time period t, L t is the system load demand, P lt For system loss.

5. The energy storage planning method based on unit combination as operation test according to claim 4 is characterized in that: The determining of the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, includes: Unit operation constraints: ―R di ≤P it ―P i(t―1) ≤R ui With it ≥u it -at i(t―1) in: and are the minimum and maximum power generation of unit i, respectively, R di is the downward climbing rate limit of unit i, R ui is the upward climbing rate limit of unit i, is the minimum startup time of unit i, z it It is an auxiliary variable, indicating whether the unit has just started.

6. The energy storage planning method based on unit combination as operation test according to claim 5 is characterized in that: The determining of the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint, includes: Energy storage operation constraints: in: and are the upper limits of charging and discharging power of energy storage at site j, and are the charging and discharging power of energy storage, and are the minimum and maximum values ​​of energy storage capacity respectively; Carbon emission constraints: in; It is the carbon emission limit of the power system within a certain dispatching cycle.

7. The energy storage planning method based on unit combination as operation test according to claim 1 is characterized in that: The target energy storage planning model is determined based on the objective function and the constraint conditions, and is solved by using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test, including: Inputting the objective function and the parameters in the constraint conditions into a mixed integer programming solver; The solver iteratively solves binary and continuous variables based on branch and bound and cutting plane algorithms; The target energy storage planning scheme is output based on the result of the iterative solution, and the target energy storage planning scheme includes energy storage site selection, capacity configuration, unit start and stop plan, and charging and discharging strategy.

8. An energy storage planning system with unit combination as operation test, characterized in that: include: An objective function construction unit is used to construct an objective function of an energy storage planning model using unit power generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost, and equipment operation and maintenance replacement cost as target parameters; A constraint determination unit, used to determine the constraint conditions of the energy storage planning model, wherein the constraint conditions include power balance constraints, unit operation constraints, energy storage operation constraints, and carbon emission constraints; The target energy storage planning scheme determining unit is used to determine the target energy storage planning model based on the objective function and the constraint conditions, and solve it using a binary algorithm to obtain a target energy storage planning scheme with unit combination as an operation test.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the energy storage planning method with unit combination as operation verification according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the steps of the energy storage planning method with unit combination as operation verification as claimed in any one of claims 1 to 7.

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