An advanced compressed air energy storage multi-stage optimization planning method considering multi-application value

Abstract

The application belongs to the technical field of energy storage optimal configuration of power systems, and particularly relates to a multi-stage optimal planning method for advanced compressed air energy storage considering multiple application values, comprising: obtaining multiple configuration schemes based on constraint conditions of an upper-layer capacity optimal configuration model, wherein the configuration model takes the minimum life cycle economic cost as an objective; each configuration scheme is composed of configuration states of each planning stage and each access site, and the configuration state of each access site is a vector composed of configuration quantities of each capacity type A-CAES unit; obtaining a power grid day-ahead scheduling cost based on the configuration scheme by using a lower-layer typical day optimal scheduling model to calculate the life cycle economic cost, wherein the constraint conditions consider A-CAES in four aspects of peak load shifting, emergency backup, flexible ramping and inertia support; and optimizing the configuration scheme to realize multi-stage optimal planning. The application fully considers large-scale A-CAES multi-application value for configuration, and avoids energy storage resource redundancy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage optimization configuration of power systems, and more particularly, to a multi-stage optimization planning method for advanced compressed air energy storage considering multi-application value. BACKGROUND

[0002] With the accelerated construction of new power systems, the installed capacity of new energy represented by wind power and photovoltaic power is rising. As of the end of March 2024, the installed capacity of wind and light in China has exceeded 1.1 billion kilowatts, accounting for 37.3%. However, the high proportion of new energy access has brought severe challenges to the safe and stable operation of the power system. Developing new energy storage is a key means to ensure new energy consumption and power supply reliability. Among many energy storage technologies, the successful application of compressed air energy storage (CAES) technology and demonstration projects has attracted widespread attention and promotion. In the "New Energy Storage Pilot Demonstration Project List" released by the National Energy Administration, there are a total of 11 compressed air energy storage projects. Compressed air energy storage has become one of the most promising and promising new energy storage technologies. To overcome the drawbacks of traditional CAES, such as dependence on fossil fuels and low electric-to-electric conversion efficiency, advanced compressed air energy storage (A-CAES) has emerged. Currently, the 300 MW advanced compressed air energy storage national demonstration power station in Shandong Feicheng has successfully connected to the grid. Domestic compressed air energy storage projects plan to build large-scale A-CAES bases with a total installed capacity of over 1,000 MW. The development of compressed air energy storage technology will feature clean, large-scale, and high efficiency. In this context, further exploring the multi-application value of A-CAES and studying the optimization planning strategy of large-scale A-CAES is of great significance to ensuring the safe, high-quality, and economic operation of new power systems.

[0003] Nowadays, domestic and foreign scholars have conducted a large number of studies on the economic analysis of compressed air energy storage systems, providing important basis for the planning and construction of compressed air energy storage systems. For example: for a monopolistic power system, the economic benefits of A-CAES power stations in energy saving and emission reduction, and providing auxiliary services are quantitatively analyzed; considering that A-CAES provides peak shaving and emergency backup services for microgrids, the economic benefits of A-CAES in peak shaving and backup are analyzed; considering the uncertainty of both the source and the load, the influence of investment cost, power shortage cost, and power sales revenue on the economy of a combined system of a wind farm and an A-CAES power station is analyzed. However, the services provided by compressed air energy storage in existing research are mostly limited to peak shaving, and the application value of A-CAES in peak shaving, backup, climbing, inertia support, and primary frequency modulation has not been fully explored. It is urgent to analyze the multi-application value and corresponding characteristics of large-scale CAES.

[0004] On the basis of economic analysis, some scholars further study the optimization planning strategy of A-CAES of power system, such as: studying the A-CAES double-layer planning strategy considering the economic-reliability multi-objective collaborative optimization; analyzing the influence mechanism of scheduling plan on the life of A-CAES, and accordingly studying the A-CAES capacity planning strategy considering variable life characteristics; considering the cogeneration characteristics of A-CAES, studying the A-CAES system capacity configuration strategy considering the collaborative complementation of multiple heat sources. However, the existing research still has the following problems: first, most of the existing literature considers single-stage planning of a certain capacity level, and there is little research on multi-stage planning strategy of large-scale multi-capacity level A-CAES group; second, in the optimization planning process, the existing literature generally only faces the design working condition, and the variable working condition characteristics and standby characteristics of A-CAES are considered to be rough; third, there is little literature considering the high proportion of new energy access background, and considering the flexible climbing, inertia support and primary frequency modulation capacity of A-CAES.

[0005] In the actual optimization planning process, the proportion of new energy access and load demand increases year by year, and if only the regulation demand of power system at a certain stage is considered, it will cause waste or insufficient allocation of resources. In the actual regulation and operation process, due to the influence of many factors such as operating state, power boundary, dynamic characteristics and the like, the output of A-CAES presents obvious variable working condition characteristics, and the standby regulation interval is also discontinuous. In addition, the generation characteristics of A-CAES are similar to those of gas turbines, and the charging characteristics are similar to those of large electric motors, which have significant advantages in flexible climbing, inertia support.

[0006] The results of literature research show that there is still a certain gap in the research on multi-stage optimization planning strategy of large-scale advanced compressed air energy storage considering multiple application values. SUMMARY

[0007] In view of the above defects or improvement needs of the prior art, the present application provides a multi-stage optimization planning method of advanced compressed air energy storage considering multiple application values, which aims to face the background of high proportion of new energy access, fully clarify the multiple application values of A-CAES peak shaving, standby, climbing, inertia support and primary frequency modulation, and provide an A-CAES multi-stage optimization planning strategy that can meet the regulation demand of power system, so as to improve the operation economy and reliability of high proportion of new energy power system.

[0008] To achieve the above purpose, according to one aspect of the present application, a multi-stage optimization planning method of advanced compressed air energy storage considering multiple application values is provided, comprising:

[0009] Based on the power system topology, source and load data, generator parameters and A-CAES unit parameters, the constraint conditions of the upper multi-stage A-CAES capacity optimization configuration model are used to obtain multiple configuration schemes, wherein the configuration model is to minimize the life cycle economic cost as the objective, and the geographical location restriction and investment cost restriction as the constraint conditions; each configuration scheme is composed of the configuration state of each planning stage and each access location, and the configuration state of each access location is a vector composed of the configuration number of each capacity type A-CAES unit;

[0010] Based on the configuration state of each access location of each planning stage and the configuration state of each access location of each planning stage before the planning stage, the lower typical day power grid optimization scheduling model is used to obtain the day-ahead scheduling cost of the power grid of the planning stage, and the life cycle economic cost corresponding to the objective function in the configuration model is calculated based on the scheduling cost of each planning stage, wherein the scheduling model is to minimize the day-ahead scheduling cost of the power grid as the objective, and the constraint conditions include A-CAES variable working condition output, A-CAES emergency reserve capacity, A-CAES flexible climbing regulation range and frequency safety, and the four constraints are established by respectively considering the application value and operating characteristics of A-CAES in peak load shifting, emergency reserve, flexible climbing and inertia support;

[0011] Based on the configuration scheme corresponding to the minimum life cycle economic cost, an optimization algorithm is used to optimize the configuration scheme, so as to realize multi-stage optimization planning of advanced compressed air energy storage.

[0012] Further, the objective function of the configuration model is:

[0013] minC total =C con +C om +C op -B sal

[0014]

[0015] In the formula, C total is the total cost after discount, C con is the construction cost of A-CAES power station; C om is the maintenance cost of A-CAES power station, C op is the day-ahead scheduling cost of the power grid, B sal is the equipment residual value at the end of the planning period; N is the number of planning stages; γ is the discount rate, T Si is the initial year of the i-th stage S i of construction, M is the number of accessible locations, K represents the total number of selected A-CAES units, c con,kV is the construction unit price of the k-type A-CAES unit, each of the configuration schemes is denoted as V, vector denotes S i stage G j the configuration state of the point, V k i,j denotes S i stage G j the number of k-type units newly configured at the point, k = 1, 2, …, K, G j denotes the jth access point, T end is the end year of planning, c fm,k and c vm,k are respectively the fixed maintenance cost coefficient and the floating maintenance cost coefficient of the k-type unit, is the number of k-type units in the yth year; is the total output of k-type units in the yth year; N typ is the number of types of typical days; κ n is the proportion of n types of typical days in a year; D op is the total number of days in a year; C op,n is the day-ahead scheduling cost of the power grid of n types of typical days, which is the objective function of the lower scheduling model; N sal is the total number of devices that do not reach the retirement age at the end of the planning period; LS exp,l , LS use,l are respectively the expected service life and the actual service life of the device l; δ l is the net salvage rate of the device l.

[0016] Further, the objective function of the scheduling model is:

[0017] min C op,da = C G,r + C G,re + C G,rl + C G,ss + C N,w

[0018]

[0019] In the formula, C op,da , C G,r , C G,re , C G,rl , C G,ss and C N,w are respectively the day-ahead scheduling cost of the power grid, the thermal power operation cost, the thermal power reserve cost, the thermal power flexible ramping adjustment cost, the thermal power switching cost and the new energy curtailment penalty cost; T is the total scheduling period number; N G , N W , N PThe number of thermal power units, wind farms and photovoltaic power stations, respectively; The electricity purchase cost coefficient of thermal power unit i, respectively; The active power output of thermal power unit i at time period t; Δt = 1h is the unit scheduling time length of day-ahead scheduling stage; The start-stop state of thermal power unit i at time period t; The unit emergency reserve cost of thermal power unit i; The emergency reserve capacity of thermal power unit i at time period t; The unit positive and negative flexible ramp regulation cost of thermal power unit i, respectively; The positive and negative flexible ramp regulation capacity of thermal power unit i at time period t, respectively; The single switch-on and switch-off cost of thermal power unit i, respectively; c pen The penalty cost coefficient of curtailed electricity; The predicted and actual output of wind farm i at time period t, respectively. The predicted and actual output of photovoltaic power station i at time period t, respectively.

[0020] Further, the constraint conditions of the scheduling model further include: thermal power unit operation constraints, A-CAES operation condition constraints, A-CAES gas storage constraints, A-CAES heat accumulator constraints, wind farm output constraints, photovoltaic power station output constraints, system power balance constraints, system emergency reserve constraints, power supply guarantee promotion and consumption reduction constraints and flexible ramp demand constraints.

[0021] Further, the A-CAES variable working condition output constraint is the compression power and power generation power of the A-CAES power station after considering the variable working condition characteristics of the A-CAES in the peak load shifting scenario, and is expressed as:

[0022]

[0023] In the formula, P t C,c , P t C,g The compression power and power generation power output by the A-CAES power station at time period t, respectively; The isentropic efficiency of the compression process and the power generation process, respectively, both of which are functions of the load rate of the compressor and the expander and are variables; The flow rate of the compressor and the expander at time period t, respectively; λ represents the specific heat ratio of air; R g Represents the ideal gas constant; n c , n g Represents the number of stages of the compressor and the expander, respectively; Represents the air temperature entering the kth stage of compressor; Represents the rated compression ratio of the kth stage of compressor; Tl g Tin, l represents the air temperature entering the lth stage expander; E, l represents the rated expansion ratio of the lth stage expander.

[0024] Further, the A-CAES emergency reserve capacity constraint comprises:

[0025] Under compression operation:

[0026] Under standby operation:

[0027] Under power generation operation:

[0028] In the above formulae, x represents the emergency reserve capacity provided by the A-CAES unit at time period t; x is an auxiliary variable; Pmin, comp represents the minimum compression power of the A-CAES unit; Pmax, gen, l and Pmin, gen, l respectively represent the maximum and minimum power generation power of the A-CAES unit.

[0029] Further, the A-CAES flexible ramping adjustment range constraint comprises:

[0030] (1) Under compression operation:

[0031]

[0032] In the above formulae, Ppos, l and Pneg, l respectively represent the positive and negative flexible ramping capacity provided by the A-CAES unit at time period t; Δt c2g Δt represents the time for power generation ramping after switching from compression operation to power generation operation; r C,g r represents the compression operation ramping rate; Pmax, comp represents the maximum compression power of the A-CAES unit; x is an auxiliary variable;

[0033] (2) Under standby operation:

[0034]

[0035] In the above formulae, Δt o2g Δt represents the time for power generation ramping after switching from shutdown operation to power generation operation; Δt o2c Δt represents the time for compression ramping after switching from shutdown operation to compression operation; r C,c r represents the compression operation ramping rate;

[0036] (3) Under power generation operation:

[0037]

[0038] In the above formulae, Δtg2c denotes the time used for compression ramping after the transition from power generation mode to compression mode, Pmin is the minimum compression power of the A-CAES unit, t C,g denotes the power output by the A-CAES power plant to the grid at time t, Pmax and Pmin are the maximum and minimum power generation of the A-CAES unit, respectively.

[0039] Further, the frequency security constraints include a maximum frequency rate of change constraint, a steady state frequency deviation constraint and a maximum frequency deviation constraint, denoted as:

[0040]

[0041] In the above formula, the superscript * denotes the per unit value, Pmax, Pmin and Pmax are the maximum frequency rate of change, the steady state frequency deviation and the maximum frequency deviation of the system when a power disturbance is expected at time t, respectively; is the frequency regulation dead band; Pmax, Pmin and Pmax are the maximum frequency rate of change limit, the steady state frequency deviation limit and the maximum frequency deviation limit, respectively.

[0042] According to another aspect of the present application, there is provided an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method as described above when executing the computer program.

[0043] According to another aspect of the present application, there is provided a computer readable storage medium comprising a stored computer program, wherein the computer program, when executed by a processor, controls the device in which the storage medium is located to perform the steps of the method as described above.

[0044] Overall, compared with the prior art, the above technical solutions conceived by the present application mainly have the following beneficial effects:

[0045] The application provides a multi-application value considering advanced compressed air energy storage multi-stage optimization planning strategy. First, the application value and operation characteristics of A-CAES in peak shaving, emergency backup, flexible climbing, inertia support and the like are studied, an A-CAES operation constraint set is established, and a large-scale A-CAES multi-stage double-layer optimization planning model is further constructed, wherein the upper multi-stage A-CAES capacity optimization configuration model takes the minimum full life cycle economic cost as the target to generate a configuration scheme, and the lower typical day power grid optimization scheduling model takes the minimum power grid operation cost as the target to solve the scheduling result of the system under each typical day; finally, according to the iterative relationship of the double-layer optimization model, the double-layer optimization model is converted into a mixed integer linear programming problem, and an optimization algorithm is used for calculation and solving, and the final configuration scheme is obtained through repeated iteration. The design can fully consider the multi-application value of large-scale A-CAES for configuration, and avoid the redundancy of energy storage resources caused by over-investment and rough estimation. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 It is a multi-application value considering advanced compressed air energy storage multi-stage optimization planning method flow chart provided by the embodiment of the application.

[0047] Figure 2 It is a large-scale A-CAES multi-stage optimization planning architecture provided by the embodiment of the application.

[0048] Figure 3 It is a large-scale A-CAES optimization planning process provided by the embodiment of the application.

[0049] Figure 4 It is a four-quadrant diagram of A-CAES flexible climbing adjustment range provided by the embodiment of the application.

[0050] Figure 5 It is a power grid dynamic frequency response model of A-CAES power generation provided by the embodiment of the application.

[0051] Figure 6 It is an improved IEEE-118 node system topology diagram in the example provided by the embodiment of the application.

[0052] Figure 7 It is each typical day source and load prediction data in the example provided by the embodiment of the application.

[0053] Figure 8 It is a scenario A-C planning result in the example provided by the embodiment of the application.

[0054] Figure 9are economic analysis of scenarios A-C in the examples provided by the embodiments of the present application, wherein (a) is a life cycle cost analysis, (b) is an energy storage investment cost analysis, (c) is a cost analysis of each stage of energy storage, and (d) is a total cost analysis of each stage;

[0055] Figure 10 are planning results of scenarios A, D and D' in the examples provided by the embodiments of the present application;

[0056] Figure 11 are economic analysis of scenarios A, D and D' in the examples provided by the embodiments of the present application;

[0057] Figure 12 are A-CAES power station output plans in the examples provided by the embodiments of the present application;

[0058] Figure 13 are thermal power unit output plans in the examples provided by the embodiments of the present application. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0060] Embodiment One

[0061] A multi-stage optimization planning method for advanced compressed air energy storage considering multi-application value, comprising:

[0062] Based on the power system topology graph, source and load data, generator unit parameters and A-CAES unit parameters, the constraint conditions of the upper multi-stage A-CAES capacity optimization configuration model are used to obtain a plurality of configuration schemes, wherein the configuration model is to minimize the life cycle economic cost as the target, and the geographical location restriction and the investment cost restriction as the constraint conditions; each configuration scheme is composed of the configuration state of each access site in each planning stage, and the configuration state of each access site is a vector composed of the configuration number of each capacity type A-CAES unit;

[0063] Based on the access site configuration state of each planning stage and the access site configuration state of each planning stage before the planning stage, the lower typical day power grid optimization scheduling model is used to obtain the power grid day-ahead scheduling cost of the planning stage, and the all-life-cycle economic cost corresponding to the objective function in the configuration model is calculated based on the scheduling cost of each planning stage, wherein the scheduling model takes the minimum power grid day-ahead scheduling cost as the target, and the constraint conditions include A-CAES variable working condition output, A-CAES emergency reserve capacity, A-CAES flexible climbing adjustment range and frequency safety, and the four constraints are established by respectively considering the application value and operation characteristics of A-CAES in peak load shifting, emergency reserve, flexible climbing and inertia support four aspects;

[0064] Based on the configuration scheme corresponding to the minimum all-life-cycle economic cost, an optimization algorithm is used to optimize the configuration scheme, so as to realize the multi-stage optimization planning of advanced compressed air energy storage.

[0065] Considering the growth process of new energy and load, the embodiment proposes a multi-stage optimization planning architecture and process of advanced compressed air energy storage, as shown in Figure 2 and Figure 3 .

[0066] The multi-stage optimization planning architecture of A-CAES is shown in Figure 2 , according to the source and load development, the planning period of A-CAES is divided into N stages, and the construction stage sequence is recorded as S, and the A-CAES access point sequence is recorded as G

[0067] S={S1,S2,…,S i ,…,S N} (1)

[0068] G={G1,G2,…,G j ,…,G M} (2)

[0069] In the above formula, S i represents the i-th stage of construction, G j represents the j-th access site, and M represents the number of constructible geographical points.

[0070] The multi-stage optimization planning of large-scale A-CAES takes the minimum total cost of the whole life cycle of the project as the target, obtains the configuration scheme, and the corresponding configuration state variable set is recorded as V

[0071]

[0072] In the above formula, the vector represents the configuration state of S i stage G j point, and V S i Stage G j The number of k-type units is added, k = 1, 2, …, K (K types of units to be selected).

[0073] The large-scale A-CAES optimization planning process is shown in Figure 3 First, input the topological conditions, source load data, multi-capacity level A-CAES device parameters and other information; second, perform A-CAES multi-application demand quantitative analysis based on operation simulation; and finally, perform large-scale A-CAES optimization planning for multi-application scenarios to obtain the final optimization scheme. The multi-stage optimization planning idea is as follows: first, in the S1 stage (initial planning stage), the configuration state of {V 1,1 ,…,V 1,j ,…,V 1,M} is determined to meet the regulation requirements of the S1 stage; then, in the S2 stage, the configuration state of {V 2,1 ,…,V 2,j ,…,V 2,M} is determined to meet the regulation requirements of the S2 stage; and so on, until the last planning stage S N .

[0074] It should be noted that the so-called large-scale refers to the fact that the present method can realize the planning and configuration of a large number of energy storage units under the condition of the lowest life cycle economic cost through multi-stage planning.

[0075] The present embodiment method also studies the application value and operation characteristics of A-CAES in peak shaving, emergency backup, flexible ramping, inertia support and the like, and establishes an A-CAES operation constraint set. A large-scale A-CAES multi-stage double-layer optimization planning model is further constructed, in which the upper-layer multi-stage A-CAES capacity optimization configuration model takes the minimum life cycle economic cost as the target to generate a configuration result, and the lower-layer typical day power grid optimization scheduling model takes the minimum power grid operation cost as the target to solve the scheduling result of the system under each typical day. According to the iterative relationship of the double-layer optimization model, it is converted into a mixed integer linear programming problem, and an improved particle swarm optimization algorithm and mature commercial optimization software are used for calculation and solution. After repeated iterations, the final configuration scheme is obtained. The present embodiment method can fully consider the configuration of large-scale A-CAES with multiple application values, avoid the redundancy of energy storage resources caused by over-investment and rough estimation, and greatly improve the operation economy and reliability of high-proportion new energy power systems.

[0076] The A-CAES operation constraint set described above includes four aspects of peak shaving, emergency backup, flexible ramping, inertia and primary frequency modulation.

[0077] In the aspect of A-CAES peak shaving, the isentropic efficiency of A-CAES compressor / expander changes with the load rate, showing obvious variable working condition characteristics. The calculation formula of the compression power and the power generation power of A-CAES power station considering the variable working condition characteristics in the preferred peak shaving and valley filling scenario is as follows:

[0078]

[0079] In the above formula, P t C,c , P t C,g respectively represent the compression power and the power generation power output by the A-CAES power station at time period t; respectively represent the isentropic efficiency of the compression process and the power generation process, both of which are functions of the load rate of the compressor and the expander and are variables; respectively represent the flow rate of the air flowing into the compressor and the expander at time period t; λ represents the specific heat ratio of the air; R g represents the ideal gas constant; n c , n g respectively represent the number of stages of the compressor and the expander; represents the temperature of the air entering the kth stage of the compressor; represents the rated compression ratio of the kth stage of the compressor. T l g represents the temperature of the air entering the lth stage of the expander; represents the rated expansion ratio of the lth stage of the expander.

[0080] In the aspect of A-CAES emergency backup, the A-CAES power station can generally complete the switching from full compression power to full power generation power within 15 minutes. Considering the influence of the operating state, power boundary and the like, the expression of the A-CAES emergency backup value is as follows:

[0081] (1) Compression working condition

[0082]

[0083] In the above formula, P is the emergency backup capacity provided by the A-CAES power station at time period t; x is an auxiliary variable, and the same below; is the minimum compression power of the A-CAES power station; are respectively the minimum and maximum power generation powers of the A-CAES power station.

[0084] (2) Standby working condition

[0085]

[0086] (3) Power generation working condition

[0087]

[0088] In terms of A-CAES flexible ramping, the A-CAES power station has good ramping characteristics, and considering multiple factors such as operating state, power boundary, dynamic characteristics, etc., the A-CAES flexible ramping range is as shown in Figure 4 , and as preferred, the A-CAES flexible ramping constraints are as shown below:

[0089] (1) Compression working condition

[0090]

[0091] In the above formula, respectively, the positive and negative flexible ramping capacity provided by the A-CAES power station at time period t; Δt c2g is the time that can be used for power generation ramping after the compression working condition is converted to the power generation working condition; r C,g is the power generation working condition ramping rate; is the maximum compression power of the A-CAES power station.

[0092] (2) Standby working condition

[0093]

[0094] In the above formula, Δt o2g represents the time that can be used for power generation ramping after the shutdown working condition is converted to the power generation working condition; Δt o2c represents the time that can be used for compression ramping after the shutdown working condition is converted to the compression working condition; r C,c represents the compression working condition ramping rate.

[0095] (3) Power generation working condition

[0096]

[0097] In the above formula, Δt g2c represents the time that can be used for compression ramping after the power generation working condition is converted to the compression working condition.

[0098] In terms of A-CAES participating in inertia and primary frequency modulation, the frequency response model of a new energy high proportion power grid containing A-CAES power generation is as shown in Figure 5 , and the corresponding transfer function is as shown below:

[0099]

[0100]

[0101] In the above formula, ΔP epd (s) represents the expected active power disturbance; H W(s), H P (s), H G (s) represent the transfer functions of wind farms, photovoltaic power stations and thermal power units, respectively; H Eg (s) represent the transfer functions of A-CAES in power generation mode; G ine (s) represent the transfer function of system inertia response; k L represents the load frequency regulation coefficient; is the load value under rated frequency; Δf * is the system frequency variation, the superscript (*) represents the per unit value, and the same applies hereinafter; is the synchronous generator mechanical power variation; is the synchronous generator electromagnetic power variation; H is the synchronous generator inertia time constant; D is the synchronous generator damping coefficient; are the frequency response proportional coefficient and integral coefficient of thermal power units, respectively; μ G is the thermal power unit regulation coefficient; is the wind farm power variation; K W1 and K W2 are the primary frequency regulation coefficient and inertia response coefficient of the wind farm, respectively; is the photovoltaic power station power variation; K P1 and K P2 are the primary frequency regulation coefficient and inertia response coefficient of the photovoltaic power station, respectively; is the A-CAES power station power variation; are the frequency response proportional coefficient and integral coefficient of A-CAES, respectively; μ E is the A-CAES regulation coefficient.

[0102] The above equations (15) and (16) are subjected to equivalent order reduction, combination and simplification, and Laplace inverse transformation processing to obtain the analytical expressions of the maximum frequency variation rate, steady-state frequency difference and maximum frequency difference, which can be used as the preferred ones and applied to frequency safety constraints.

[0103] In addition, the objective function of the upper multi-stage A-CAES capacity optimization configuration model is to minimize the total life cycle economic cost:

[0104] min C total = C con + C om + C op - B sal (16)

[0105] In the above equation, C total is the total cost after discounting; C con is the construction cost of A-CAES power station; C om is the maintenance cost of A-CAES power station; C opThe operation cost of power grid, which is affected by the planning result and the operation mode of power grid; sal The residual value of equipment at the end of planning period. It can be preferred as:

[0106] ① Construction cost

[0107]

[0108] In the above formula, γ is the discount rate; S is the total construction cost of power grid; i The initial year of construction phase; c con,k The construction unit price of k-type unit.

[0109] ② Maintenance cost

[0110]

[0111] In the above formula, T end The end year of planning; c fm,k And c vm,k Are the fixed maintenance cost coefficient and the floating maintenance cost coefficient of k-type unit, respectively; The number of k-type unit in y year; The total output of k-type unit in y year.

[0112] ③ Operation cost

[0113]

[0114] In the above formula, N typ The number of typical days; κ n The proportion of n typical days in a year; D op The total number of days in a year; C op,n The dispatching cost of n typical days, which is the objective function of dispatching layer.

[0115] ④ Residual value of equipment

[0116] At the end of planning period, some equipment has not reached the retirement year, and the residual value of the equipment after depreciation needs to be calculated:

[0117]

[0118] In the above formula, N sal The total number of equipment that has not reached the retirement year at the end of planning period; LS exp,l , LS use,l Are the expected service life and the actual service life of equipment l, respectively; δ l The net residual value rate of equipment l.

[0119] The constraint conditions of the objective function of the upper multi-stage A-CAES capacity optimization configuration model are as follows:

[0120] ①Geographical location restriction

[0121]

[0122] In the above formula, V ·,j , respectively represent the total number of A-CAES units and the upper limit of the number of A-CAES units in the life cycle planning of the G j point.

[0123] ②Investment cost restriction

[0124]

[0125] In the above formula, C ins , respectively represent the A-CAES power station investment cost and the upper limit.

[0126] The objective function of the lower typical day power grid optimization scheduling model is to minimize the grid operation cost, which can be used as the optimization:

[0127] min C op,da = C G,r + C G,re + C G,rl + C G,ss + C N,w (23)

[0128]

[0129] In the above formula, C op,da , C G,r , C G,re , C G,rl , C G,ss and C N,w are the grid day-ahead scheduling cost, the thermal power operation cost, the thermal power standby cost, the thermal power flexible ramping adjustment cost, the thermal power start-stop cost and the new energy curtailment penalty cost, respectively; T is the total scheduling period number; N G , N W , N P are the number of thermal power units, wind farms and photovoltaic power stations, respectively; is the purchase cost coefficient of the thermal power unit i; is the active power output of the thermal power unit i at the t period; Δt=1h is the unit scheduling length of the day-ahead scheduling stage; is the start-stop state of the thermal power unit i at the t period; is the unit emergency standby cost of the thermal power unit i; is the emergency standby capacity of the thermal power unit i at the t period; respectively, are the unit positive and negative flexible ramping regulation costs of thermal power unit i; respectively, are the positive and negative flexible ramping regulation capacities of thermal power unit i at time period t; respectively, are the single on-off costs of thermal power unit i;c pen is the penalty cost coefficient of curtailed power; respectively, are the predicted and actual outputs of wind farm i at time period t. respectively, are the predicted and actual outputs of photovoltaic power station i at time period t.

[0130] The constraint conditions of the objective function of the lower-layer typical-day power grid optimal dispatching model, which can be preferred, include: ① thermal power unit operation constraints (output upper and lower limit constraints, start-stop constraints, ramping rate constraints), ② A-CAES normal operation constraints (operation condition constraints, gas storage chamber, heat storage and heat exchanger constraints), ③ wind farm output constraints, ④ photovoltaic power station output constraints, ⑤ system power balance constraints, ⑥ system emergency reserve constraints, ⑦ A-CAES variable operating condition output constraints, ⑧ A-CAES emergency reserve capacity constraints, ⑨ A-CAES flexible ramping regulation range constraints, ⑩ supply-promotion and consumption-reduction constraints, flexible ramping demand constraints, frequency safety constraints, and the like. Among them, the first six constraints are more common, the constraint expressions of ⑦-⑨ are shown in equations (4)-(14), and other constraints are shown as follows.

[0131] Supply-promotion and consumption-reduction constraints

[0132]

[0133] In the above formula, γ1 and γ2 respectively represent the limit values of the power shortage rate and the consumption rate; P t d , and P t load respectively represent the cut-load power and load demand power of the system at time period t.

[0134] Flexible ramping demand constraints

[0135]

[0136] In the above formula, respectively represent the upper and lower flexible ramping regulation demand amounts of the system at time t, and the calculation method is shown as follows.

[0137]

[0138] In the above formula, represents the net load prediction value of the system at time t, and r t + , and r t -respectively represent the upward and downward safety margin reserved at time t.

[0139] Frequency safety constraint

[0140] The system frequency safety constraint includes the maximum frequency rate of change constraint, the steady-state frequency deviation constraint and the maximum frequency deviation constraint.

[0141]

[0142] In the above formula, "*" represents the unit value, respectively represent the maximum frequency rate of change, the steady-state frequency deviation and the maximum frequency deviation of the system when it is subjected to a predicted power disturbance at time t; is the frequency modulation dead zone; respectively represent the maximum frequency rate of change limit, the steady-state frequency deviation limit and the maximum frequency deviation limit.

[0143] It should be noted that the thermal power unit operation constraint: this constraint condition is used to ensure that the start-stop state, power generation plan and standby capacity of the thermal power unit are within a reasonable range.

[0144] A-CAES normal operation constraint: this constraint condition is used to ensure that the operating condition, output value, gas pressure of the gas storage chamber and temperature of the heat storage chamber of the A-CAES power station are within a reasonable range.

[0145] Wind farm output constraint: this constraint condition is used to ensure that the wind farm output is within a reasonable range.

[0146] Photovoltaic power station output constraint: this constraint condition is used to ensure that the photovoltaic power station output is within a reasonable range.

[0147] System power balance constraint: this constraint condition is used to ensure the power balance of the power system.

[0148] System emergency reserve constraint: this constraint condition is used to ensure that the emergency reserve capacity of the power system is sufficient.

[0149] A-CAES variable operating condition output constraint: this constraint condition is used to ensure that the compression power and power generation power of the A-CAES follow the variable operating condition characteristics.

[0150] A-CAES emergency reserve capacity constraint: this constraint condition is used to ensure that the emergency reserve capacity provided by the A-CAES is within a reasonable range.

[0151] A-CAES flexible ramping regulation range constraint: this constraint condition is used to ensure that the flexible ramping regulation capacity provided by the A-CAES is within a reasonable range.

[0152] Power supply promotion and consumption constraint: this constraint condition is used to ensure that the power shortage rate and consumption rate of the power system are within a reasonable range.

[0153] Flexible ramping demand constraint: This constraint is used to ensure that the flexible ramping regulation capacity of the power system is sufficient.

[0154] Frequency security constraint: This constraint is used to ensure that the frequency and frequency variation of the power system are within a reasonable range.

[0155] The following examples are given to verify the effectiveness of the method of the present application.

[0156] A multi-application value considering large-scale advanced compressed air energy storage multi-stage optimization planning strategy, the method takes Figure 6 The improved IEEE-118 node system as shown in the figure is the implementation object, wherein G1-G15 are conventional thermal power units, W1-W20 are wind farms, P1-P10 are photovoltaic power stations, and E1-E9 are A-CAES power stations that can be planned and distributed. In order to ensure the rationality of the optimization configuration result of the compressed air energy storage power station, the present application selects the predicted new energy output and load demand of four typical days in a year to perform scheduling analysis, and then obtains the annual operation cost through weighted calculation, which is taken into account in the capacity configuration result. The specific parameters of the thermal power unit, wind and light power station, A-CAES power station and other parameters of the system are shown in Tables 1-5, the source and load growth trend of each stage is shown in Table 6, and the predicted curve of new energy and load of each typical day is shown in Figure 7 .

[0157] Table 1 Thermal power unit parameters (G1-G7)

[0158]

[0159]

[0160] Table 2 Thermal power unit parameters (G8-G15)

[0161] Parameter type G8 G9 G10 G11 G12 G13 G14 G15 Rated output / MW 300 300 350 350 350 550 550 550 Minimum technical output / MW 60 60 70 70 70 150 150 150 Start-stop cost / $ 1450 1450 1700 1700 1660 2500 2600 2550 Minimum start-stop time / h 4 4 5 5 5 6 6 6 Ramp rate / (MW / min) 8.5 8.7 9.2 9.2 9 13.7 14.2 15 Purchased power cost coefficient a / ($ / MW·h) 36.25 37.37 31.91 31.81 32.36 30.44 30.48 31.35 Purchased power cost coefficient b / ($ / h) 123.54 122.77 127.73 127.88 127.71 129.41 129.83 128.32 Positive reserve cost coefficient / ($ / MW·h) 18.06 18.44 21.91 20.5 20.33 21.26 20.98 20.5 Negative reserve cost coefficient / ($ / MW·h) 14.83 15.89 18.36 17.36 17.31 18.02 16.62 17.01 Regulation coefficient 0.043 0.043 0.044 0.044 0.044 0.044 0.044 0.044 Speed governor proportional gain Kp 18 17 15 19 17 17 16 16 Speed governor integral gain Ki 2.5 2.8 2.5 2.9 2.4 2.3 2.5 2.2 Inertia time constant H / s 14.8 14.7 15.9 16.6 16.7 15.8 16.8 16.1 Frequency modulation limit ratio 10％ 10％ 8％ 8％ 8％ 8％ 8％ 8％

[0162] Table 3 Wind and light station coefficients

[0163] Wind-solar farm parameters Parameter value Wind-solar farm parameters Parameter value Spillage penalty cost coefficient / ($ / MW·h) 200 Load increase frequency modulation limit ratio 6％ Virtual inertia control gain coefficient 15 Load decrease frequency modulation limit ratio 10％ Frequency droop control gain coefficient 100

[0164] Table 4 A-CAES power station coefficients

[0165]

[0166]

[0167] Table 5 Other system parameters

[0168] Parameter type Value Parameter type Value Discount rate 8％ Initial frequency change rate limit / (Hz / s) 0.35 Net salvage rate 6％ Frequency modulation dead zone / Hz 0.033 Wind power prediction error ratio 10％ Load frequency regulation coefficient 1.5 Load prediction error ratio 3％ Typical day ratio 180 / 78 / 74 / 33 Steady-state frequency difference limit / Hz 0.3 Planning engineering period / a 40 Maximum frequency difference limit / Hz 0.5

[0169] Table 6 Source and load growth in each stage

[0170] Planning year New energy installed capacity ratio Load demand ratio Annual average net load Net load daily peak-valley difference 0 1.00 1.00 2383.61 2735.75 10 2.00 1.42 2968.26 3832.89 20 3.00 1.74 3242.74 4647.51 30 4.00 2.02 3393.15 5371.03 40 5.00 2.14 3047.30 5678.83

[0171] To verify the effectiveness of the multi-stage planning method of the application, three running scenarios are set in the embodiment of the application: scenario A: planning the energy storage power station every 10 years, a total of 4 times; scenario B: planning the energy storage power station every 20 years, a total of 2 times; scenario C: planning only once at the beginning of the first year.

[0172] In combination with the above parameters and scenarios, this embodiment is sequentially performed according to the steps described in the application, and the optimal planning scheme of the energy storage power station under each scenario A-C is obtained by solving. The A-C A-CAES planning results are shown in Figure 8 , the life cycle cost of each scenario, the energy storage investment cost, the energy storage cost of each stage and the total cost of each stage of the system are shown in (a), (b), (c), (d) of Figure 9 .

[0173] As can be seen from Figure 8 , in terms of planning capacity, the total capacity gradually decreases from scenario A to scenario C, which is 1850MW, 1810MW and 1660MW respectively. In terms of capacity level selection, large-capacity A-CAES (300MW and 250MW) is selected as the main model of the planning scheme due to its lower unit cost and higher conversion efficiency, while small-capacity A-CAES (100MW and 60MW) has smaller single-machine capacity and cost, and the configuration is more flexible, so it becomes the detailed selection of the configuration scheme.

[0174] As can be seen from (a) of Figure 9 , the life cycle cost of scenario A is the smallest, which is reduced by 2.33×108$ and 3.21×108$ compared with scenarios B and C respectively. As can be seen from (b) of Figure 9 , although the total capacity of scenario C is the smallest, the energy storage construction cost is the highest and the residual value at the end of the planning period is the lowest. This is because scenario C only plans at the initial year, which has the problem of advance investment, while scenarios A and B adopt staged planning, which saves the energy storage investment cost and still has a certain residual value at the end of the planning period. As can be seen from Figure 7 , the total capacity of scenarios A and B is similar, but scenario A configures more 300MW power stations, and the overall construction time sequence of scenario A is later, so the energy storage construction cost and residual value of scenario A are better than those of scenario B.

[0175] As can be seen from Figure 9As can be known from (c) and (d) in the table, firstly, in the early planning stage (S1 stage), the scenarios A and B avoid the higher energy storage construction cost and operation cost of the scenario C due to over-investment, wherein the energy storage cost of the scenario A is only 36.38% of that of the scenario C. Meanwhile, because the A-CAES power station of the scenario A is less, the grid needs to pay more fees to the power generator, and the extra operation cost can be covered by the saved energy storage investment cost, so that the total cost of the scenario A in this stage is still lower than that of the scenario C; secondly, in the middle planning stage (S2 and S3 stages), the grid operation costs of the scenarios are not much different, and the energy storage investment cost needs to be paid by the scenarios A and B, so that the total system cost of the two scenarios slightly increases; finally, in the late planning stage (S4 stage), the A-CAES power stations of all scenarios have been constructed, and the total system cost in this stage is relatively close.

[0176] As can be known from the above analysis, the scenarios A and B consider the multi-stage planning according to the change of the grid regulation demand, so that the energy storage capacity redundancy caused by over-investment can be avoided, and the energy storage investment cost can be saved. The multi-stage planning scheme considered by the scenario A is more precise, and the system life cycle economy can be further improved.

[0177] In order to verify the necessity of considering the variable working condition characteristics in the A-CAES optimization planning process, the scenarios A, D and D' are further set in the embodiment of the present application. The scenario A is the planning result obtained by the model of the present application; the scenario D does not consider the variable working condition characteristics of the A-CAES, and assumes that the operation efficiency of the A-CAES power station is a fixed value (i.e. the design working condition efficiency); the scenario D' re-calculates the economic cost of the A-CAES power station by using the variable working condition model of the present application according to the planning result of the scenario D. The planning results of the scenarios A and D are shown in Figure 10 , and the economic comparison analysis of the scenarios A, D and D' is shown in Figure 11 .

[0178] As can be known from the figure, the scenario D assumes that the operation efficiency of the A-CAES is a fixed value, instead of the variable working condition efficiency of the scenario A, so that the A-CAES power station in the scenario D has a more superior value ratio, and therefore the planning capacity of the scenario D is greater than that of the scenario A, which is 1960 MW, so as to reduce the grid operation cost. However, when the planning result of the scenario D is substituted into the variable working condition model for consideration, it is found that the scenario D needs to pay the grid operation cost similar to that of the scenario A, and needs to pay 5.63% more energy storage investment cost, so that the total cost is higher than that of the scenario A. This shows that the scenario D does not consider the variable working condition characteristics of the A-CAES, and the obtained planning result is not optimal, and the life cycle economy analysis is also inaccurate. The model of the present application can consider the operation working condition of the A-CAES power station, determine a more optimal planning result, and improve the system economy.

[0179] Further analysis is carried out on the influence of variable working condition characteristics on the system dispatch plan. The dispatch results of the typical day 1 in the S4 stage are compared. The A-CAES power station output plans of the scenarios D and D' are as shown in Figure 12 , wherein the compression working condition is negative, the power generation working condition is positive, and the thermal power unit output plan is as shown in Figure 13 .

[0180] As shown in Figure 12 , the scenario D tends to use low load and long time charging and discharging for the overall A-CAES power station due to not considering the variable working condition characteristics, the power generation output is maintained in the interval of 40% to 60%, and the average working time is 14.5 h; and the scenario D' uses high load and relatively short time charging and discharging for the output of each A-CAES power station to avoid the electric energy loss caused by low conversion efficiency, the power generation output is basically maintained above 80%, and the average working time is 13.125 h.

[0181] As shown in Figure 13 , the thermal power units G13 to G15 are most economical, are basically in full-load state, the thermal power units G10 to G12 are relatively economical, can also ensure full-day operation, the thermal power units G5 to G9 are operated as supplementary output during the load peak period, and the thermal power units G1 to G4 are relatively poor in economy and are in shutdown state.

[0182] As calculated, the total output of the thermal power units in the scenario D' is increased by 891.25 MW·h compared with the scenario D, because the overall conversion efficiency of the A-CAES power station in the scenario D' is lower than that in the scenario D due to considering the variable working condition characteristics, and the thermal power units need to increase the output, therefore, the thermal power units G8 and G12 in the scenario D' are increased to different degrees. The estimation of the A-CAES power station efficiency in the scenario D is too optimistic, and there is a risk that the dispatch plan cannot be completely executed in the actual operation process.

[0183] Further analysis is carried out on the influence of the multiple application values of the A-CAES on the planning results and the life cycle cost. In the embodiment of the present application, four types of scenarios E to H are set, in which the A-CAES power station does not participate in peak shaving, standby, climbing, inertia and frequency modulation services. The planning results and economic cost are as shown in Table 7.

[0184] As can be seen from Table 7, the E scenario in which the A-CAES power station does not participate in peak shaving cannot meet the power balance constraint due to a large peak-valley difference of net load at the end of planning, resulting in no solution of the planning model; in the F scenario, the A-CAES power station does not provide emergency standby service, the value of the energy storage decreases, the configuration capacity also decreases, and the power grid needs to pay more expensive emergency standby costs to the thermal power unit, so the economic cost of the F scenario increases by 4.21% compared with the A scenario; similar to the F scenario, in the G scenario, the A-CAES power station does not provide flexible climbing service, resulting in an increase of 9.93% in the life cycle cost, and the configuration capacity of the energy storage is the lowest; in the H scenario, the inertia support and primary frequency modulation capacity provided by the A-CAES power station are lost, and a large-capacity unit combination mode needs to be adopted in some periods, which has an adverse effect on the economic efficiency of system operation.

[0185] Table 7 Comparison of multi-application values of A-CAES

[0186]

[0187]

[0188] As can be seen from the above analysis, the large-scale advanced compressed air energy storage has excellent peak shaving, standby, and climbing application values, in the embodiment of the application, if the A-CAES does not participate in peak shaving at the end of planning, the power balance constraint cannot be met, resulting in no solution of the model, therefore, the peak shaving value of the A-CAES is the most important among the four values, and the other three values are in the order of climbing value, standby value, and inertia and frequency modulation value according to the increment of the life cycle cost.

[0189] In summary, the multi-stage optimization planning strategy of the large-scale advanced compressed air energy storage considering the multi-application values proposed in the application is effective and reasonable.

[0190] Embodiment two

[0191] The application also relates to an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0192] The related technical solutions are the same as above, and will not be repeated here.

[0193] Embodiment three

[0194] The application also relates to a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0195] In particular, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device.

[0196] The related technical solutions are the same as above, and will not be described here.

[0197] Those skilled in the art will easily understand that the above description is only a preferred embodiment of the present application, and is not intended to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for multi-stage optimization planning of advanced compressed air energy storage considering multi-application value, characterized in that, The method comprises the following steps: Based on the power system topology, source and load data, generator parameters and A-CAES unit parameters, the constraint conditions of the upper multi-stage A-CAES capacity optimization configuration model are used to obtain multiple configuration schemes, wherein the configuration model takes the minimum life cycle economic cost as the target, and takes the geographical location restriction and the investment cost restriction as the constraint condition; each configuration scheme is composed of the configuration state of each access site in each planning stage, and the configuration state of each access site is a vector composed of the configuration quantity of each capacity type A-CAES unit; Based on the configuration state of each access site in each planning stage and the configuration state of each access site in each planning stage before the planning stage, the lower typical day power grid optimization scheduling model is used to obtain the day-ahead scheduling cost of the power grid in the planning stage, and the life cycle economic cost corresponding to the target function in the configuration model is calculated based on the scheduling cost of each planning stage, wherein the scheduling model takes the minimum day-ahead scheduling cost of the power grid as the target, and the constraint conditions include A-CAES variable working condition output, A-CAES emergency reserve capacity, A-CAES flexible climbing adjustment range and frequency safety, and the four constraints are established by considering the application value and operation characteristics of A-CAES in peak load shifting, emergency reserve, flexible climbing and inertia support respectively; Based on the configuration scheme corresponding to the minimum life cycle economic cost, an optimization algorithm is used to optimize the configuration scheme, so as to realize multi-stage optimization planning of advanced compressed air energy storage; The target function of the configuration model is: In the formula, is the total cost after discount, is the construction cost of A-CAES power station; is the maintenance cost of A-CAES power station, is the grid day-ahead scheduling cost, is the equipment residual value at the end of the planning period; N is the number of planning stages; is the discount rate, is the initial year of the i th stage of construction, M is the number of construction sites, K represents the total number of types of A-CAES units to be selected, is the construction unit price of A-CAES units of type k , and each configuration scheme is denoted as V , Vector represents the configuration state at the th stage point, represents the number of newly configured units of type at the th stage k point, k =1, 2, …, K , represents the j th access site, is the end year of the planning, and are the fixed maintenance cost coefficient and the floating maintenance cost coefficient of units of type k , respectively, is the number of units of type y in the k th year; is the total output of units of type y in the k th year; is the number of types of typical days; is the proportion of days of type n in a year; is the total number of days in a year; is the grid day-ahead scheduling cost of days of type n , which is the objective function of the lower scheduling model; is the total number of equipment that has not reached the retirement age at the end of the planning period; , are the expected service life and the actual service life of equipment l , respectively; is the net residual value rate of equipment l .

2. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The target function of the scheduling model is: wherein, , , , , and are the grid day-ahead scheduling cost, the thermal power operation cost, the thermal power reserve cost, the thermal power flexible ramping regulation cost, the thermal power switching cost and the new energy curtailment penalty cost, respectively; T is the total scheduling period number; , , are the number of thermal power units, wind farms and photovoltaic power stations, respectively; , are the purchase cost coefficients of thermal power units i ; is the active power output of thermal power units i in t periods; is the unit scheduling time length of the day-ahead scheduling stage; is the start-stop state of thermal power units i in t periods; is the unit emergency reserve cost of thermal power units i ; is the emergency reserve capacity of thermal power units i in t periods; , are the unit positive and negative flexible ramping regulation costs of thermal power units i ; , are the positive and negative flexible ramping regulation capacities of thermal power units i in t periods; , are the single switching costs of thermal power units i ; is the curtailment penalty cost coefficient; , are the predicted and actual outputs of wind farms i in t periods, , are the predicted and actual outputs of photovoltaic power stations i in t periods.

3. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The constraint conditions of the scheduling model further include: thermal power unit operation constraint, A-CAES operation condition constraint, A-CAES gas storage constraint, A-CAES heat accumulator constraint, wind farm output constraint, photovoltaic power station output constraint, system power balance constraint, system emergency reserve constraint, supply promotion and consumption constraint and flexible climbing demand constraint.

4. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The A-CAES variable working condition output constraint is the A-CAES power station compression power and power generation power considering the variable working condition characteristics of A-CAES in the peak load shifting scene, which is expressed as: wherein, , respectively represent the compressed power and the generated power of the A-CAES plant at time interval t ; , respectively represent the isentropic efficiency of the compression process and the generation process, both of which are functions of the load rate of the compressor and the expander respectively, and are variables; , respectively represent the flow rate of air into the compressor and the expander at time interval t ; represents the specific heat ratio of air; represents the ideal gas constant; , respectively represent the number of stages of the compressor and the expander; represents the temperature of air entering the k th stage of the compressor; represents the rated compression ratio of the k th stage of the compressor; represents the temperature of air entering the l th stage of the expander; represents the rated expansion ratio of the l th stage of the expander.

5. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The A-CAES emergency reserve capacity constraint includes: Under compression conditions: In the standby mode: In power generation mode: In the above formula, is the minimum compression power of the A-CAES unit; t is the minimum compression power of the A-CAES unit; is the minimum compression power of the A-CAES unit; is the minimum compression power of the A-CAES unit; , is the minimum compression power of the A-CAES unit.

6. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The A-CAES flexible climbing adjustment range constraint includes: (1) In the compression working condition: wherein, , is the positive and negative flexible ramping capacity provided by the A-CAES unit during the time period t , is the time for ramping to power generation after the compression mode is switched to the power generation mode; is the ramping rate of the power generation mode; is the maximum compression power of the A-CAES unit; is an auxiliary variable; (2) In the standby working condition: In the formula, represents the time for the ramp-up for power generation after the transition from the shutdown condition to the power generation condition; represents the time for the ramp-up for compression after the transition from the shutdown condition to the compression condition; represents the ramp-up rate for the compression condition; (3) In the power generation working condition: In the above formula, represents the time for compression ramping after the power generation condition is converted to the compression condition, is the minimum compression power of the A-CAES unit, represents the time period during which the A-CAES power station is in the compression condition, t is the power output by the A-CAES power station, , are the minimum and maximum power generation powers of the A-CAES unit, respectively.

7. The advanced compressed air energy storage multi-stage optimization planning method of claim 1, wherein, The frequency safety constraint includes the maximum frequency change rate constraint, the steady-state frequency difference constraint and the maximum frequency difference constraint, which is expressed as: In the above formula, the superscript * represents the taking modulus value, , , are respectively the maximum frequency change rate, the steady-state frequency difference and the maximum frequency difference when the system is subjected to a predicted power disturbance at the moment; t is the frequency modulation dead zone; , , are respectively the maximum frequency change rate limit, the steady-state frequency difference limit and the maximum frequency difference limit.​ 8.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored computer program, wherein when the computer program is run by the processor, the device where the storage medium is located is controlled to execute the steps of the method of any one of claims 1 to 7.

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