A method and system for energy storage planning with unit commitment as operation test

Abstract

The application belongs to the technical field of energy storage planning and provides an energy storage planning method and system with unit combination as operation inspection, which comprises the following steps: constructing an objective 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 and replacement cost as target parameters; determining constraint conditions of the energy storage planning model, which comprise power balance constraint, unit operation constraint, energy storage operation constraint and carbon emission constraint; determining a target energy storage planning model based on the objective function and the constraint conditions and solving by using a binary algorithm to obtain a target energy storage planning scheme with unit combination as operation inspection. The application obtains an optimized target energy storage planning scheme through unit combination operation inspection, which can meet system operation requirements, minimize cost and improve resource utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage planning, and particularly relates to an energy storage planning method and system taking unit commitment as operation verification. BACKGROUND

[0002] At present, the penetration rate of new energy technology in the power system is continuously improved. With the continuous maturity and cost reduction of energy storage technology, the application of energy storage has attracted more attention, and therefore it is particularly important to study the planning and operation of energy storage. Energy storage planning refers to the overall and systematic planning of the scale, layout, type selection and operation strategy of the energy storage system in the energy system.

[0003] There are some significant defects in the current energy storage planning. On the one hand, the existing planning process misses a key link and does not embed unit commitment into the energy storage planning model for solving. When the power system is actually running, the start-stop state, minimum online-offline time and other unit commitment constraint conditions of the unit affect the flexibility of the system. When and how to start and stop the unit affect whether the power supply can accurately match the dynamic changes of the load. Ignoring these factors makes it impossible to efficiently store electric energy when the unit has excess power, or to smoothly release energy when the unit is weak during the power consumption peak, 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 the energy storage facility and the load center and the power supply side, affecting power transmission loss and response speed. Capacity determination affects whether the energy storage capacity can match the load peak-valley difference and the volatility of renewable energy. Without combined processing, it is easy to cause investment mistakes. It may be that the address is selected well but the capacity is not planned properly, so that the energy storage advantage cannot be fully utilized, or the capacity is adapted but the site selection is a failure, resulting in suboptimal investment strategy.

[0004] Therefore, an energy storage planning method and system taking unit commitment as operation verification are needed. SUMMARY

[0005] The embodiments of the present application provide an energy storage planning method and system taking unit commitment as operation verification, which are used to solve the problem of low implementation effect of energy storage planning strategy.

[0006] The first aspect of the embodiments of the present application provides an energy storage planning method taking unit commitment as operation verification, comprising:

[0007] A target function construction unit is configured to construct a target function of an energy storage planning model by taking unit generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and replacement cost as target parameters.

[0008] A constraint condition determination unit is configured to determine a constraint condition of the energy storage planning model, and the constraint condition comprises a power balance constraint, a unit operation constraint, an energy storage operation constraint, and a carbon emission constraint.

[0009] A target energy storage planning scheme determination unit is configured to determine a target energy storage planning model based on the target function and the constraint condition, and solve the target energy storage planning model by using a binary algorithm to obtain a target energy storage planning scheme which is subjected to operation verification of unit combination.

[0010] Further, the target function of the energy storage planning model which takes the unit power generation cost, the energy storage investment cost, the system reliability cost, the pollutant emission cost, the carbon emission cost, and the equipment operation and replacement cost as target parameters comprises:

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

[0012] Wherein, Z is a minimum cost of energy storage planning, C g is the unit power generation cost, 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, and C om is the equipment operation and replacement cost.

[0013] Further, the target function of the energy storage planning model which takes the unit power generation cost, the energy storage investment cost, the system reliability cost, the pollutant emission cost, the carbon emission cost, and the equipment operation and replacement cost as target parameters comprises:

[0014] The unit power generation cost C g :

[0015]

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

[0017] The energy storage investment cost C inv :

[0018]

[0019] Wherein: cvj The unit capacity investment cost of the jth selected energy storage site, y j The variable whether to select site j, c pj The unit power investment cost of the jth selected energy storage site, E j The corresponding energy storage capacity, P dj The rated power;

[0020] System reliability cost C r

[0021]

[0022] Wherein: c r The reliability cost coefficient, R t The system reliability index at time t;

[0023] Pollutant emission cost C e

[0024]

[0025] Wherein: The pollutant emission amount of unit i at time t, c p The unit pollutant emission cost;

[0026] Carbon emission cost C ct

[0027]

[0028] Wherein: The carbon emission amount of unit i at time t, P ct The carbon trading price;

[0029] Equipment operation and replacement cost C om

[0030]

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

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

[0033] Power balance constraint:

[0034] ​​​​

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

[0036] Further, the determining the constraint conditions of the energy storage planning model, the constraint conditions comprising a power balance constraint, a unit operation constraint, an energy storage operation constraint, and a carbon emission constraint, comprises:

[0037] The unit operation constraint:

[0038]

[0039] — R di ≤ P it — P i(t―1) ≤ R ui

[0040] z it ≥ u it — u i(t―1)

[0041]

[0042] wherein: and are the minimum and maximum values of the power generated by unit i, R di is the downward ramping rate limit of unit i, R ui is the upward ramping rate limit of unit i, is the minimum on time of unit i, z it is an auxiliary variable, indicating whether the unit has just started.

[0043] Further, the determining the constraint conditions of the energy storage planning model, the constraint conditions comprising a power balance constraint, a unit operation constraint, an energy storage operation constraint, and a carbon emission constraint, comprises:

[0044] The energy storage operation constraint:

[0045]

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

[0047] Carbon emission constraint:

[0048]

[0049] Wherein: is the carbon emission limit of the power system in a certain dispatching period.

[0050] Further, the target energy storage planning model is determined based on the objective function and the constraint condition, and a binary algorithm is used for solving to obtain a target energy storage planning scheme for unit combination as operation verification, including:

[0051] The parameters in the objective function and the constraint condition are input into a mixed integer programming solver;

[0052] The solver iteratively solves the binary variables and continuous variables based on branch and bound and cut plane algorithm;

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

[0054] The second aspect of the embodiment of the application provides an energy storage planning system for unit combination as operation verification, including:

[0055] An objective function construction unit is configured to construct an objective 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 and replacement cost as target parameters;

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

[0057] A target energy storage planning scheme determination unit is configured to determine a target energy storage planning model based on the objective function and the constraint condition, and solve by using a binary algorithm to obtain a target energy storage planning scheme for unit combination as operation verification.

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

[0059] A memory, a transceiver, a processor and a bus system;

[0060] The memory is configured to store a program;

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

[0062] The bus system is used to connect the memory and the processor to make the memory and the processor communicate.

[0063] The fourth aspect of the embodiment of the application provides a readable storage medium, including instructions, which, when running on a computer, make the computer execute steps of the energy storage planning method with unit commitment as operation verification.

[0064] From the above technical solutions, the embodiment of the application has the following advantages:

[0065] The energy storage planning model of the application comprehensively considers the objective function of various costs, including unit generation cost, energy storage investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and replacement cost, and comprehensively considers the influencing factors of the energy storage system in the economic and environmental aspects; secondly, the constraint conditions including power balance, unit operation, energy storage operation and carbon emission are determined to ensure the feasibility of the planning scheme in actual operation; finally, the target energy storage planning model is established based on the objective function and the constraint condition, and a binary algorithm is used for solving, and the optimal target energy storage planning scheme is obtained through the unit commitment operation verification, which realizes the cost minimization and improves the utilization rate of resources while meeting the system operation requirements.

[0066] Other advantages, objects, and features of the application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art from the examination of the following specification, or can be learned from practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 The embodiment flowchart of the energy storage planning method with unit commitment as operation verification in the application. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

[0069] The energy storage planning method with unit commitment as operation verification in the embodiment is used to meet the system operation requirements, minimize the cost and improve the utilization rate of resources. The implementation method in the embodiment can be implemented in the system, can be implemented in the server, or can be implemented in the terminal, and the specific implementation is not limited.

[0070] Embodiment one

[0071] Please refer to Figure 1An embodiment of the energy storage planning method for unit combination operation inspection in the application comprises the following steps:

[0072] S11. A target 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 replacement cost as target parameters;

[0073] In this embodiment, the dispatching period of the power system is divided into T time periods, and there are I engine units and J selected energy storage sites in the system. Since the binary algorithm is used for model solving in the application, the variable binary is performed here, and the binary capacity is directly converted into the energy storage construction state, thereby constraining the energy storage charging and discharging, forming the coordination and integration of energy storage planning and dispatching, and specifically as follows:

[0074] Unit start-stop state variable: for each unit i = 1, 2,..., I, at each time period t = 1, 2,..., T, a binary variable u it is defined to represent the unit start-stop state. u it = 1 indicates that the unit i starts at time period t, and u it = 0 indicates that the unit is stopped. For example, if there are 3 units and the dispatching period is divided into 4 time periods, there will be 12 u it variables. Energy storage site selection variable: for J selected energy storage sites, a binary variable y j (j = 1, 2,..., J) is defined to represent whether to select the site to construct energy storage facilities. y j = 1 indicates that the site j is selected, and y j = 0 indicates that the site is not selected.

[0075] Other possible integer variable binary:

[0076] The integer variable is converted into a 0-1 variable, and the binary decomposition method of the energy storage capacity decision is represented as:

[0077]

[0078] wherein the integer variable z n represents the number of units of energy storage constructed at the system node n, and z n is converted into a 0-1 variable after binary conversion; l n is an index, and z n corresponds to the lth bit in binary, and the corresponding energy storage capacity is units, and the maximum value is L n ; that is, whether z n takes a value in the lth bit in binary is determined, and 1 indicates 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 unit generation cost of the i-th unit, C inv is the investment cost of energy storage, C r is the system reliability cost, C e is the pollutant emission cost, C ct is the carbon emission cost, C om is the equipment operation and replacement cost.

[0082] Specifically, for the i-th unit at time period t, the fuel cost is usually related to the power generation, assuming that the cost is formed as a quadratic function, where a i , b i and c i are the fuel cost coefficients of unit i, related to the unit type and fuel characteristics, P it is the power generation of unit i at time t, and the total fuel cost is: The following generation cost is obtained.

[0083] The unit generation cost C g :

[0084]

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

[0086] The energy storage investment cost C inv :

[0087]

[0088] Where: c vj is the unit capacity investment cost of the j-th selected energy storage site, y j is the variable whether to select site j, c pj is the unit power investment cost of the j-th selected energy storage site, E j is the corresponding energy storage capacity, P dj is the rated power.

[0089] System reliability cost C r :

[0090]

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

[0092] Pollutant emission cost C e :

[0093]

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

[0095] Carbon emission cost C ct :

[0096]

[0097] wherein: is the carbon emission amount of unit i at time t, P ct is the carbon trading price.

[0098] Equipment operation and replacement cost C om :

[0099]

[0100] wherein: c mj is the unit capacity operation cost of the jth selected energy storage site, c rep is a replacement cost coefficient, P it is the power generation of unit i at time t, wherein y j ensures that only the selected energy storage site calculates the operation cost.

[0101] S12. Determine the constraint conditions of the energy storage planning model, the constraint conditions including 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 is equal to the sum of the system load demand and the system loss, i.e.:

[0103]

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

[0105] The unit operation constraints include power limits, ramp rates and minimum start-up and shut-down times, which are specified as follows:

[0106] Power limits: Ramp rates: R di ≤ P it — P i(t―1) ≤ R ui Minimum start-up and shut-down times: The minimum time limit after the unit is started or stopped should be followed to avoid frequent start-up and shut-down. Let the minimum start-up time of unit i be An auxiliary variable z it (also a 0-1 variable) is introduced to represent whether the unit has just been started: z it ≥ u it — u i(t―1) where and are the minimum and maximum values of the power generated by unit i, R di is the downward ramp rate limit of unit i, R ui is the upward ramp rate limit of unit i, is the minimum start-up time of unit i, and z it is the auxiliary variable representing whether the unit has just been started.

[0107] Energy storage operation constraints:

[0108]

[0109]

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

[0111] Carbon emission constraints:

[0112]

[0113] where: is the carbon emission limit of the power system within a certain dispatching period.

[0114] ​In addition, the unit-energy storage coupling constraint is included: the charge and discharge strategy of the energy storage should match the unit generation plan and load fluctuation, and the energy storage should respond in time when the unit fails or the output is insufficient; the unit start-stop operation also needs to consider the current state of the energy storage to ensure smooth transition of power. Through logical association constraints, for example, when the unit generation power is insufficient, the energy storage needs to increase the discharge power, and the specific constraints are constructed according to the actual situation.

[0115] S13. Determine the target energy storage planning model based on the objective function and the constraint condition, and solve it by using a binary algorithm to obtain the target energy storage planning scheme for unit combination operation inspection.

[0116] In this fact example, the following is included:

[0117] 1. Input the parameters in the objective function and the constraint condition into the mixed integer programming solver;

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

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

[0120] The solver iteratively solves the binary variables (u it , y j , z it , etc.) and continuous variables (P it , P cj,t , P dj,t , c mj , c rep , etc.) based on the branch and bound, cutting plane, etc. algorithms.The iterative solver is used to solve the problem. In each iteration, the solver checks whether all constraints are satisfied according to the current variable values, and calculates the value of the objective function. By constantly adjusting the variable values, the optimal solution that satisfies the constraints and minimizes the objective function is gradually found.

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

[0122] Output the optimal energy storage planning scheme, including energy storage site selection, i.e. j determine which energy storage sites to select, capacity configuration, i.e. j , unit start-stop plan, i.e. it , and charging-discharging strategy, i.e. cj,t and P dj,t .

[0123] The above embodiment simultaneously realizes energy storage device site selection and capacity configuration. Compared with energy storage capacity configuration that separates site selection and energy storage capacity configuration that only considers fixed feasible positions, the position of the energy storage device can be optimized to reduce the congestion of the line, delay system expansion investment, and enhance system economy and reliability.

[0124] Embodiment two

[0125] An embodiment of the energy storage planning system in the application, which takes unit combination as the operation test, includes the following steps:

[0126] A target function construction unit is configured to construct a target function of the 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 and replacement cost as target parameters.

[0127] A constraint condition determination unit is configured to determine the constraint conditions of the energy storage planning model, and the constraint conditions include power balance constraints, unit operation constraints, energy storage operation constraints and carbon emission constraints.

[0128] A target energy storage planning scheme determination unit is configured to determine the target energy storage planning model based on the target function and the constraint conditions, and solve the target energy storage planning model by using a binary algorithm to obtain the target energy storage planning scheme taking the unit combination as the operation test.

[0129] The specific limitations of the system can refer to the limitations of the method described above, which will not be repeated here. Each module in the system described above can be implemented by software, hardware and their combination. The above modules can be embedded in the processor in the computer device or independent of the processor in the computer device, or stored in the memory in the computer device in the form of software, so as to be called and executed by the processor.

[0130] Embodiment three

[0131] The present application provides a computer device, comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, the processor executes the computer readable instructions to implement the steps of the above method.

[0132] Those skilled in the art can realize that the units of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0133] In the embodiments provided by the present application, it should be understood that the division of units is only a logical functional division, and actual implementation can have another division manner. For example, 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 application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit.

[0134] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0135] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and 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 application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for energy storage planning with unit commitment as operational check, characterized in that, Comprise: A target function of a storage energy planning model is constructed with unit generation cost, storage energy investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and replacement cost as target parameters; the target function of the storage energy planning model with unit generation cost, storage energy investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and replacement cost as target parameters comprises: wherein: is the minimum cost of energy storage planning, is the unit generation cost of the unit, is the investment cost of energy storage, is the cost of system reliability, is the cost of pollutant emission, is the cost of carbon emission, is the cost of equipment operation and replacement; Generating cost of the unit : wherein: is the number of units of fuel consumed by the unit at time is the number of units of fuel consumed by the unit at time is the fuel cost of the unit at time is the number of units of fuel consumed by the unit at time is the single start cost of the unit at time is the number of units of fuel consumed by the unit at time is the number of units of fuel consumed by the unit at time is the total start-stop cost of the unit at time Energy storage investment cost : in: For the first The unit capacity investment cost of a selected energy storage site. To select a site variables, For the first The unit power investment cost of a selected energy storage site, For the corresponding energy storage capacity, Rated power; System reliability cost : wherein: is a reliability cost coefficient, is is the instantaneous system reliability indicator; Cost of pollutant emissions : wherein: is the unit pollutant emission cost; is the unit pollutant emission cost; is the unit pollutant emission cost; is the unit pollutant emission cost; Carbon emission costs : wherein: is the carbon emission of the unit at time , and is the carbon trading price; Device operation and maintenance replacement cost : wherein: is the unit capacity operation and maintenance cost of the selected energy storage site, is the replacement cost coefficient, is the unit capacity operation and maintenance cost of the selected energy storage site, is the unit capacity operation and maintenance cost of the selected energy storage site, is the power generation capacity of the selected energy storage site at time t, is the power generation capacity of the selected energy storage site at time t. Determine the constraint conditions of the storage energy planning model, and the constraint conditions comprise power balance constraint, unit operation constraint, storage energy operation constraint and carbon emission constraint; Determine the target storage energy planning model based on the target function and the constraint conditions, and solve it by using a binary algorithm to obtain a target storage energy planning scheme with unit combination as operation inspection, and the target storage energy planning scheme comprises storage energy site selection, capacity configuration, unit start-stop plan and charging and discharging strategy.

2. The energy storage planning method with combined unit as operation check according to claim 1, characterized in that, The determination of the constraint conditions of the storage energy planning model comprises: Power balance constraint: wherein: and are the charging and discharging power stored in the time period is the system load demand, is the system loss.​ 3. The energy storage planning method with unit commitment as operation check according to claim 2, characterized in that, The determination of the constraint conditions of the storage energy planning model comprises: Unit operation constraint: wherein: and are the minimum and maximum values of the power output of the unit, are the minimum and maximum values of the power output of the unit, is the down ramp rate limit of the unit, is the down ramp rate limit of the unit, is the up ramp rate limit of the unit, is the up ramp rate limit of the unit, is the minimum on time of the unit, is the minimum on time of the unit, is the minimum on time of the unit, 4. The energy storage planning method with unit commitment as operation check according to claim 3, characterized in that, The determination of the constraint conditions of the storage energy planning model comprises: Storage energy operation constraint: wherein: and are upper limits of charging and discharging power of the energy storage at the site , and are charging and discharging power of the energy storage, and are minimum and maximum values of the energy storage capacity, respectively; Carbon emission constraint: wherein; is the carbon emission limit of the power system in a certain dispatching period.

5. The energy storage planning method for unit commitment verification according to claim 1, wherein, The determination of the target storage energy planning model based on the target function and the constraint conditions, and the solving by using a binary algorithm to obtain a target storage energy planning scheme with unit combination as operation inspection comprises: Input the parameters in the target function and the constraint conditions into a mixed integer programming solver; The solver iteratively solves binary variables and continuous variables based on branch and bound and cutting plane algorithm; Output the target storage energy planning scheme based on the result of iterative solving.

6. An energy storage planning system with unit commitment as operational check, characterized by, The method of any one of claims 1-5 comprises: A target function construction unit for constructing a target function of a storage energy planning model with unit generation cost, storage energy investment cost, system reliability cost, pollutant emission cost, carbon emission cost and equipment operation and replacement cost as target parameters; A constraint condition determination unit for determining constraint conditions of the storage energy planning model, and the constraint conditions comprise power balance constraint, unit operation constraint, storage energy operation constraint and carbon emission constraint; A target storage energy planning scheme determination unit for determining a target storage energy planning model based on the target function and the constraint conditions, and solving it by using a binary algorithm to obtain a target storage energy planning scheme with unit combination as operation inspection.

7. A computer device, characterized by Comprise: Memory, transceiver, processor and bus system; The memory is used to store programs; The processor is used to execute the programs in the memory, comprising the method of claim 1-5 for storage energy planning with unit combination as operation inspection; The bus system is used to connect the memory and the processor to make the memory and the processor communicate.

8. A readable storage medium, characterized by, The computer program product comprises instructions which, when run on a computer, cause the computer to perform the steps of the unit commitment based operational test method for energy storage planning of any one of claims 1 to 5.

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