Advanced compressed air energy storage multi-stage optimization planning method considering multiple application values

Abstract

The invention belongs to the technical field of power system energy storage optimization configuration, and particularly relates to an advanced compressed air energy storage multi-stage optimization planning method considering multiple application values, comprising: obtaining a plurality of configuration schemes based on constraint conditions of an upper layer capacity optimization configuration model, the configuration model taking the whole life cycle economic cost minimum as a target; 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; on the basis of the configuration scheme, a lower-layer typical daily optimization scheduling model is adopted to obtain the day-ahead scheduling cost of the power grid so as to calculate the whole life cycle economical efficiency cost, and the constraint condition considers four aspects of peak load shifting, accident reserve, flexible climbing and inertia supporting of A-CAES; and optimizing the configuration scheme to realize multi-stage optimization planning. According to the method, the multi-application value of the large-scale A-CAES is fully considered for configuration, and energy storage resource redundancy is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power system energy storage optimization configuration, and more specifically, relates to an advanced compressed air energy storage multi-stage optimization planning method considering multiple application values. Background Art

[0002] With the accelerated advancement of the construction of new power systems, the installed capacity of new energy represented by wind power and photovoltaics has continued to rise. By the end of March 2024, the installed capacity of wind and solar power 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. Vigorously developing new energy storage is a key means to ensure the reliability of new energy consumption and power supply. Among many energy storage technologies, the successful application of compressed air energy storage (CAES) technology and demonstration projects has made this technology widely concerned and promoted. In the "List of New Energy Storage Pilot Demonstration Projects" 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 new energy storage technologies with the most potential and development prospects. In order to overcome the shortcomings of traditional CAES, such as reliance on fossil fuels and low electricity-to-electricity conversion efficiency, advanced compressed air energy storage (A-CAES) came into being. At present, the 300MW advanced compressed air energy storage national demonstration power station in Feicheng, Shandong Province has been successfully connected to the grid for power generation. Many domestic compressed air energy storage projects plan to build large-scale A-CAES bases with a total installed capacity of over 1000MW. The development of compressed air energy storage technology will show the characteristics of cleanliness, scale and efficiency. In this context, further exploring the multi-application value of A-CAES and studying the optimization planning strategy of large-scale A-CAES are of great significance to ensure the safe, high-quality and economical operation of new power systems.

[0003] Nowadays, domestic and foreign scholars have conducted a lot of research on the economic analysis of compressed air energy storage systems, providing an important basis for the planning and construction of compressed air energy storage systems. For example: for monopoly power systems, the economic benefits of A-CAES power stations such as energy conservation and emission reduction, and provision of auxiliary services are quantitatively analyzed; considering that A-CAES provides peak shaving and valley filling and emergency backup services for microgrids, the economic benefits of A-CAES in peak regulation and backup are analyzed; considering the uncertainty on both the source and the load side, the impact of factors such as investment cost, power shortage cost, and power sales revenue on the economic efficiency of the combined system of wind farms and A-CAES power stations is analyzed. However, the services provided by compressed air energy storage in existing studies are mostly limited to peak regulation, and the application value of A-CAES in peak regulation, backup, climbing, inertia support and primary frequency regulation has not been deeply 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 have further conducted research on the A-CAES optimization planning strategy for power systems, such as: studying the A-CAES two-level planning strategy considering the economic-reliability multi-objective collaborative optimization; analyzing the impact mechanism of the dispatch plan on the life of A-CAES, and based on this, studying the A-CAES capacity planning strategy considering the variable life characteristics; considering the cogeneration characteristics of A-CAES, studying the A-CAES system capacity configuration strategy considering the synergistic complementarity of multiple heat sources. However, there are still the following problems in existing research: first, most of the existing literature considers a certain capacity level for single-stage planning, and there are few studies on multi-stage planning strategies for large-scale multi-capacity level A-CAES groups; second, the existing literature generally only focuses on the design conditions in the optimization planning process, and the consideration of the variable operating conditions and standby characteristics of A-CAES is relatively rough; third, there are few literatures that consider the flexible climbing, inertia support and primary frequency regulation capabilities of A-CAES in the context of high-proportion new energy access.

[0005] In the actual optimization planning process, the proportion of new energy access and load demand are increasing year by year. If only the regulation demand of the power system at a certain stage is considered, it will cause waste or insufficient configuration resources. In the actual regulation and operation process, due to the influence of multiple factors such as operating status, power boundary, dynamic characteristics, etc., the output of A-CAES shows obvious variable operating conditions, and the standby regulation range is also discontinuous. In addition, the power generation characteristics of A-CAES are similar to those of gas turbines, and the charging characteristics are similar to those of large electric motors. It has significant advantages in flexible climbing and inertia support.

[0006] The results of the literature survey show that there is still a certain gap in the research on multi-stage optimization planning strategies for large-scale advanced compressed air energy storage that considers multiple application values. Summary of the invention

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides an advanced compressed air energy storage multi-stage optimization planning method that takes into account multiple application values. Its purpose is to comprehensively clarify the multiple application values ​​of A-CAES peak shaving, standby, climbing, inertia support, and primary frequency regulation in the context of a high proportion of new energy access, and provide an A-CAES multi-stage optimization planning strategy that can meet the regulation needs of the power system, so as to improve the operating economy and reliability of the high-proportion new energy power system.

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

[0009] Based on the power system topology, source and load data, generator set parameters and A-CAES unit parameters, multiple configuration schemes are obtained by using the constraints of the upper-level multi-stage A-CAES capacity optimization configuration model, wherein the configuration model takes the minimum economic cost of the entire life cycle as the goal and takes the geographical location restriction and investment cost restriction as the constraints; each configuration scheme is composed of the configuration status of each access location in each planning stage, and the configuration status of each access location is a vector composed of the configuration quantity of A-CAES units of each capacity type;

[0010] Based on the configuration status of each access location in each planning stage and the configuration status of each access location in each planning stage before the planning stage, the lower typical day power grid optimization dispatching model is adopted to obtain the day-ahead dispatching cost of the power grid in the planning stage, and the full life cycle economic cost corresponding to the objective function in the configuration model is calculated based on the dispatching cost of each planning stage, wherein the dispatching model takes the minimum day-ahead dispatching cost of the power grid as the goal, and its constraints include four constraints: A-CAES variable operating condition output, A-CAES accident reserve capacity, A-CAES flexible climbing adjustment range and frequency safety. The four constraints are established by considering the application value and operation characteristics of A-CAES in four aspects: peak shaving and valley filling, accident reserve, flexible climbing and inertia support;

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

[0012] Furthermore, 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 discounted total cost, C con is the construction cost of A-CAES power station; C om is the maintenance cost of the A-CAES power station, C op is the day-ahead dispatching cost of the power grid, B sal is the residual value of the equipment at the end of the planning period; N is the number of planning stages; γ is the discount rate, T Si For the i-th stage of construction S i The initial year, M is the number of construction sites, K is the total number of A-CAES units to be selected, c con,kis the construction unit price of type k A-CAES unit, each configuration scheme is denoted as V, vector Indicates S i Phase G j The configuration state of the point, V k i,j Indicates S i Phase G j Click to add the number of k-type units, k = 1, 2, ..., K, G j represents the jth access location, T end For the planning end year, c fm,k With c vm,k are the fixed maintenance cost coefficient and floating maintenance cost coefficient of type k unit, is the number of type k units in year y; is the total output of type k unit in year y; N typ is the number of typical day types; κ n is the proportion of n typical days in a year; D op is the total number of days in a year; C op,n is the day-ahead dispatch cost of the power grid on n typical days, which is the objective function of the lower-level dispatch model; N sal The total number of equipment that has not reached retirement age at the end of the planning period; LS exp,l LS use,l are the expected service life and actual service life of equipment l respectively; l is the net residual value rate of equipment l.

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

[0017] minC 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 They are the day-ahead dispatching cost of the power grid, the operation cost of thermal power, the standby cost of thermal power, the flexible ramping adjustment cost of thermal power, the on / off cost of thermal power and the penalty cost of renewable energy power abandonment; T is the total number of dispatching periods; N G 、N W 、N Pare the number of thermal power units, wind farms and photovoltaic power stations respectively; are the electricity purchase cost coefficients of thermal power unit i respectively; is the active power output of thermal power unit i in period t; Δt = 1h is the unit dispatching duration in the day-ahead dispatching stage; is the start and stop status of thermal power unit i in period t; is the unit accident standby cost of thermal power unit i; is the emergency reserve capacity of thermal power unit i in period t; are the unit positive and negative flexible ramping regulation costs of thermal power unit i respectively; are respectively the positive and negative flexible ramping regulation capacities of thermal power unit i in period t; are the single startup and shutdown costs of thermal power unit i; c pen is the penalty cost coefficient for power abandonment; are the predicted and actual output of wind farm i in period t respectively. They are the predicted and actual output of photovoltaic power station i in period t respectively.

[0020] Furthermore, the constraints of the scheduling model also include: thermal power unit operation constraints, A-CAES operating condition constraints, A-CAES gas storage room constraints, A-CAES heat storage heat exchanger constraints, wind farm output constraints, photovoltaic power station output constraints, system power balance constraints, system accident standby constraints, supply guarantee and consumption promotion constraints and flexible climbing demand constraints.

[0021] Furthermore, the A-CAES variable operating condition output constraint is the compression power and power generation power of the A-CAES power station after considering the variable operating condition characteristics of A-CAES in the peak shaving and valley filling scenario, which is expressed as:

[0022]

[0023] Where P t C,c , P t C,g They represent the compression power and power generation power output by the A-CAES power station in time period t respectively; They represent the isentropic efficiency of the compression process and the power generation process respectively. They are functions of the load rate of the compressor and the expander respectively, and both are variables. Respectively represent the flow rate into the compressor and expander during time period t; λ represents the specific heat ratio of air; R g represents the ideal gas constant; n c 、n g Respectively represent the number of compressor and expander stages; represents the air temperature entering the k-th stage compressor; represents the rated compression ratio of the k-th compressor; Tl g Indicates the temperature of the air entering the first stage expander; Indicates the rated expansion ratio of the first stage expander.

[0024] Furthermore, the A-CAES accident reserve capacity constraints include:

[0025] Under compression conditions:

[0026] In standby mode:

[0027] Under power generation conditions:

[0028] In the above formula, is the emergency reserve capacity provided by the A-CAES unit in period t; x is the auxiliary variable; is the minimum compression power of the A-CAES unit; They are the minimum and maximum power generation of the A-CAES unit respectively.

[0029] Furthermore, the A-CAES flexible climbing adjustment range constraints include:

[0030] (1) Under compression conditions:

[0031]

[0032] In the formula, are the positive and negative flexible ramping capacities provided by the A-CAES unit in time period t; Δt c2g is the time used for power generation ramp after switching from compression mode to power generation mode; r C,g is the ramp rate of power generation condition; is the maximum compression power of the A-CAES unit; x is the auxiliary variable;

[0033] (2) In standby mode:

[0034]

[0035] In the formula, Δt o2g Indicates the time used for power generation ramp after switching from shutdown condition to power generation condition; Δt o2c Indicates the time used for compression climbing after switching from shutdown condition to compression condition; r C,c Indicates the compression condition climbing rate;

[0036] (3) Under power generation conditions:

[0037]

[0038] In the above formula, Δtg2c Indicates the time used for compression climbing after switching from power generation mode to compression mode. is the minimum compression power of the A-CAES unit, P t C,g represents the power output of the A-CAES power station in time period t, They are the minimum and maximum power generation of the A-CAES unit respectively.

[0039] Furthermore, the frequency safety constraint includes a maximum frequency change rate constraint, a steady-state frequency difference constraint and a maximum frequency difference constraint, which are expressed as:

[0040]

[0041] In the above formula, the superscript * indicates the per-unit value. They are the maximum frequency change rate, steady-state frequency difference and maximum frequency difference when the system is subjected to the expected power disturbance at time t respectively; It is the FM dead zone; They are the maximum frequency change rate limit, the steady-state frequency difference limit and the maximum frequency difference limit respectively.

[0042] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0043] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the steps of the method described above.

[0044] In general, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0045] The present invention provides a multi-stage optimization planning strategy for advanced compressed air energy storage that considers multiple application values. First, the application value and operating characteristics of A-CAES in terms of peak shaving and valley filling, emergency standby, flexible climbing, inertia support, etc. are studied, and an A-CAES operating constraint set is established. A large-scale A-CAES multi-stage double-layer optimization planning model is further constructed, wherein the upper-layer multi-stage A-CAES capacity optimization configuration model takes the minimum economic cost of the entire life cycle as the goal to generate a configuration plan, and the lower-layer typical day power grid optimization scheduling model takes the minimum power grid operation cost as the goal to solve the system's scheduling results on each typical day; finally, according to the iterative relationship of the double-layer optimization model, it is converted into a mixed integer linear programming problem, and the optimization algorithm is used for calculation and solution, and the final configuration plan is obtained after repeated iterations. This design can fully consider the multi-application value of large-scale A-CAES for configuration, and avoid energy storage resource redundancy caused by advanced investment and rough estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of a multi-stage optimization planning method for advanced compressed air energy storage considering multiple application values ​​provided by an embodiment of the present invention;

[0047] Figure 2 It is a large-scale A-CAES multi-stage optimization planning framework provided by an embodiment of the present invention;

[0048] Figure 3 It is a large-scale A-CAES optimization planning process provided by an embodiment of the present invention;

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

[0050] Figure 5 It is a power grid dynamic frequency response model of A-CAES power generation provided by an embodiment of the present invention;

[0051] Figure 6 is an improved IEEE-118 node system topology diagram in the example provided in the embodiment of the present invention;

[0052] Figure 7 is the source load prediction data for each typical day in the example provided by the embodiment of the present invention;

[0053] Figure 8 is the planning result of scenarios A to C in the example provided in the embodiment of the present invention;

[0054] Fig. 9This is the economic analysis of scenarios A to C in the example provided in the embodiment of the present invention, where (a) is the full life cycle cost analysis, (b) is the energy storage investment cost analysis, (c) is the cost analysis of each stage of energy storage, and (d) is the total cost analysis of each stage;

[0055] Fig.10 are the planning results of scenarios A, D and D′ in the example provided by the embodiment of the present invention;

[0056] Fig.11 This is the economic analysis of scenarios A, D and D′ in the example provided by the embodiment of the present invention;

[0057] Fig.12 It is the A-CAES power station output plan in the example provided in the embodiment of the present invention;

[0058] Fig.13 This is the output plan of the thermal power unit in the example provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0060] Embodiment 1

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

[0062] Based on the power system topology, source and load data, generator parameters and A-CAES unit parameters, multiple configuration schemes are obtained by using the constraints of the upper-level multi-stage A-CAES capacity optimization configuration model, where the configuration model takes the minimum economic cost of the entire life cycle as the goal and the geographical location restriction and investment cost restriction as the constraints; each configuration scheme consists of the configuration status of each access location in each planning stage, and the configuration status of each access location is a vector composed of the configuration quantity of A-CAES units of each capacity type;

[0063] Based on the configuration status of each access location in each planning stage and the configuration status of each access location in each planning stage before the planning stage, the lower typical day power grid optimization dispatching model is adopted to obtain the day-ahead dispatching cost of the power grid in the planning stage, and the full life cycle economic cost corresponding to the objective function in the configuration model is calculated based on the dispatching cost of each planning stage, wherein the dispatching model takes the minimum day-ahead dispatching cost of the power grid as the goal, and its constraints include four constraints: A-CAES variable operating condition output, A-CAES accident reserve capacity, A-CAES flexible climbing adjustment range and frequency safety. The four constraints are established by considering the application value and operation characteristics of A-CAES in four aspects: peak shaving and valley filling, accident reserve, flexible climbing and inertia support;

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

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

[0066] A-CAES multi-stage optimization planning architecture Figure 2 As shown in Figure 2, according to the development of source and load, the planning cycle 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 jth access site, and M represents the number of constructible geographical points.

[0070] The large-scale A-CAES multi-stage optimization planning aims to minimize the total cost of the entire life cycle of the project and obtain the configuration scheme. The corresponding configuration state variable set is recorded as V

[0071]

[0072] In the above formula, the vector Indicates S i Phase G j The configuration status of the point, Indicates S i Phase G j Click to add the number of k-type units to be configured, k = 1, 2, ..., K (there are K types of units to be selected).

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

[0074] It should be noted that the so-called large-scale means that this method can realize the planning and configuration of a large number of energy storage units at the lowest economic cost over the entire life cycle through multi-stage planning.

[0075] The method of this embodiment also studies the application value and operation characteristics of A-CAES in peak shaving and valley filling, emergency standby, flexible climbing, inertia support, etc., 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-level multi-stage A-CAES capacity optimization configuration model takes the minimum economic cost of the entire life cycle as the goal to produce configuration results, and the lower-level typical day power grid optimization dispatching model takes the minimum power grid operation cost as the goal to solve the dispatching results of the system on each typical day. According to the iterative relationship of the two-layer optimization model, it is converted into a mixed integer linear programming problem, and the optimization algorithms such as the improved particle swarm algorithm and mature commercial optimization software are used for calculation and solution, and the final configuration plan is obtained after repeated iterations. The method of this embodiment can fully consider the multi-application value of large-scale A-CAES for configuration, avoid the redundancy of energy storage resources caused by advanced investment and rough estimation, and greatly improve the operation economy and reliability of high-proportion new energy power systems.

[0076] The above-mentioned A-CAES operation constraint set includes four aspects: peak load regulation, emergency standby, flexible ramping, inertia and primary frequency regulation.

[0077] In terms of A-CAES peak load regulation, the isentropic efficiency of A-CAES compressor / expander changes with the load rate, showing obvious variable operating conditions characteristics, which can be used as an optimal choice. The calculation formula of the compression power and power generation power of the A-CAES power station considering the variable operating conditions characteristics in the peak load shaving and valley filling scenario is as follows:

[0078]

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

[0080] In terms of A-CAES emergency backup, A-CAES power plants can usually complete the switch from full compression power to full power generation power within 15 minutes. Considering the impact of operating status, power boundary, etc., it can be used as the preferred option. The value of A-CAES emergency backup is expressed as follows:

[0081] (1) Compression conditions

[0082]

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

[0084] (2) Standby mode

[0085]

[0086] (3) Power generation conditions

[0087]

[0088] In terms of A-CAES flexible ramping, the A-CAES power station has good ramping characteristics. Taking into account the influence of multiple factors such as operating status, power boundary, dynamic characteristics, etc., the A-CAES flexible ramping range is as follows: Figure 4 As shown, the A-CAES flexible climbing constraint can be preferably shown as follows:

[0089] (1) Compression conditions

[0090]

[0091] In the above formula, are the positive and negative flexible ramping capacities provided by the A-CAES power station in time period t; Δt c2g is the time available for power generation ramp after switching from compression mode to power generation mode; r C,g is the ramp rate of power generation condition; It is the maximum compression power of A-CAES power station.

[0092] (2) Standby mode

[0093]

[0094] In the above formula, Δt o2g Indicates the time available for power generation ramp after switching from shutdown to power generation; Δt o2c Indicates the time that can be used for compression climbing after the shutdown condition is changed to the compression condition; r C,c Indicates the compression condition climbing rate.

[0095] (3) Power generation conditions

[0096]

[0097] In the above formula, Δt g2c It indicates that the time can be used to compress the climbing after the power generation condition is changed to the compression condition.

[0098] In terms of A-CAES participation in inertia and primary frequency regulation, the frequency response model of the power grid with a high proportion of renewable energy including A-CAES power generation is as follows: Figure 5 The corresponding transfer function is 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 farm, photovoltaic power station and thermal power unit respectively; H Eg (s) represents the transfer function of A-CAES under power generation conditions; G ine (s) represents the system inertial response transfer function; k L Indicates the load frequency regulation coefficient; is the load value at rated frequency; Δf * is the system frequency variation, the superscript (*) indicates the per-unit value, the same below; is the change in mechanical power of the synchronous generator; is the change of electromagnetic power of synchronous generator; H is the inertia time constant of synchronous generator; D is the damping coefficient of synchronous generator; are the proportional coefficient and integral coefficient of the frequency response of the thermal power unit respectively; μ G is the regulation coefficient of thermal power unit; is the power change of the wind farm; K W1 and K W2 are the primary frequency regulation coefficient and inertia response coefficient of the wind farm respectively; is the power change of the photovoltaic power station; K P1 and K P2 They are the primary frequency regulation coefficient and inertia response coefficient of the photovoltaic power station respectively; is the power change of A-CAES power station; are the proportional coefficient and integral coefficient of A-CAES frequency response respectively; μ E is the A-CAES adjustment coefficient.

[0102] The above equations (15) and (16) are processed by equivalent order reduction, combined simplification and Laplace inverse transform to obtain the analytical expressions of the maximum frequency change rate, steady-state frequency difference and maximum frequency difference, which can be used as an optimal method for frequency safety constraints.

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

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

[0105] In the above formula, C total is the discounted total cost; C con is the construction cost of A-CAES power station; C om is the maintenance cost of the A-CAES power station; C opB is the grid operation cost, which is affected by both the planning results and the grid dispatching operation mode; sal The residual value of the equipment at the end of the planning period.

[0106] ①Construction cost

[0107]

[0108] In the above formula, γ is the discount rate; For S i Initial year of construction phase; c con,k It is the construction unit price of K-type unit.

[0109] ②Maintenance cost

[0110]

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

[0112] ③Operation cost

[0113]

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

[0115] ④ Equipment residual value

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

[0117]

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

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

[0120] ①Geographical location restrictions

[0121]

[0122] In the above formula, V ·,j , Respectively represent G j The total number and upper limit of A-CAES units planned for the entire life cycle.

[0123] ② Investment cost restrictions

[0124]

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

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

[0127] minC op,da =C G,r +C G,re +C G,rl +C G,ss +C N,w (twenty three)

[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 They are the day-ahead dispatching cost of the power grid, the operation cost of thermal power, the standby cost of thermal power, the flexible ramping adjustment cost of thermal power, the on / off cost of thermal power and the penalty cost of renewable energy power abandonment; T is the total number of dispatching periods; N G 、N W 、N P are the number of thermal power units, wind farms and photovoltaic power stations respectively; are the electricity purchase cost coefficients of thermal power unit i respectively; is the active power output of thermal power unit i in period t; Δt = 1h is the unit dispatching duration in the day-ahead dispatching stage; is the start and stop status of thermal power unit i in period t; is the unit accident standby cost of thermal power unit i; is the emergency reserve capacity of thermal power unit i in period t; are the unit positive and negative flexible ramping regulation costs of thermal power unit i respectively; are respectively the positive and negative flexible ramping regulation capacities of thermal power unit i in period t; are the single startup and shutdown costs of thermal power unit i; c pen is the penalty cost coefficient for power abandonment; are the predicted and actual output of wind farm i in period t respectively. They are the predicted and actual output of photovoltaic power station i in period t respectively.

[0130] The constraints of the objective function of the lower typical daily power grid optimization dispatch model can be used as the preferred ones, including: ① thermal power unit operation constraints (output upper and lower limit constraints, start and shutdown constraints, climbing rate constraints), ② A-CAES conventional operation constraints (operating condition constraints, gas storage chamber, heat storage heat exchanger constraints), ③ wind farm output constraints, ④ photovoltaic power station output constraints, ⑤ system power balance constraints, ⑥ system accident standby constraints, ⑦ A-CAES variable operating condition output constraints, ⑧ A-CAES accident standby capacity constraints, ⑨ A-CAES flexible climbing adjustment range constraints, ⑩ supply guarantee and consumption promotion constraints, Flexible climbing demand constraints, Frequency safety constraints, etc. Among them, the first six constraints are more common, the expressions of constraints ⑦ to ⑨ are shown in equations (4) to (14), and the other constraints are shown below.

[0131] Guarantee supply and promote consumption

[0132]

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

[0134] Flexible ramp demand constraints

[0135]

[0136] In the above formula, They respectively represent the upward and downward flexible climbing adjustment requirements of the system at time t, and the calculation method is as follows.

[0137]

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

[0139] Frequency safety constraints

[0140] System frequency safety constraints include maximum frequency change rate constraints, steady-state frequency difference constraints and maximum frequency difference constraints.

[0141]

[0142] In the above formula, “*” indicates the per unit value. They are the maximum frequency change rate, steady-state frequency difference and maximum frequency difference when the system is subjected to the expected power disturbance at time t respectively; It is the FM dead zone; They are the maximum frequency change rate limit, the steady-state frequency difference limit and the maximum frequency difference limit respectively.

[0143] It should be noted that the operation constraints of thermal power units are used to ensure that the start and stop status, power generation plan and spare capacity of thermal power units are within a reasonable range.

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

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

[0146] PV power station output constraint: This constraint is used to ensure that the PV power station output is within a reasonable range.

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

[0148] System accident reserve constraint: This constraint is used to ensure that the accident reserve capacity of the power system is sufficient.

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

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

[0151] A-CAES flexible ramp adjustment range constraint: This constraint is used to ensure that the flexible ramp adjustment capacity provided by A-CAES is within a reasonable range.

[0152] Supply guarantee and consumption promotion constraints: This constraint is used to ensure that the power shortage rate and consumption rate of the power system are within a reasonable range.

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

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

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

[0156] A multi-stage optimization planning strategy for large-scale advanced compressed air energy storage considering multiple application values. Figure 6 The improved IEEE-118 node system shown is the implementation object, among which 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. In order to ensure the rationality of the optimization configuration results of the compressed air energy storage power station, the present invention selects the predicted new energy output and load demand conditions on four typical days of the year for scheduling analysis, and then obtains the annual operating cost through weighted calculation, which is included in the capacity configuration results. The specific parameters of the thermal power units, wind and solar power stations, A-CAES power stations and other system parameters are shown in Tables 1 to 5, the source-load growth trends in each stage are shown in Table 6, and the prediction curves of new energy and load on each typical day are shown in Table 6. Figure 7 shown.

[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 Power purchase cost coefficient a / ($ / MW·h) 36.25 37.37 31.91 31.81 32.36 30.44 30.48 31.35 Power purchase 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 Adjustment 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 FM limit ratio 10％ 10％ 8％ 8％ 8％ 8％ 8％ 8％

[0162] Table 3 Wind and solar station coefficients

[0163] Wind and solar station parameters Parameter Value Wind and solar station parameters Parameter Value Power curtailment penalty cost coefficient / ($ / MW·h) 200 Increased load frequency regulation limit ratio 6％ Virtual inertia control gain coefficient 15 Load reduction frequency regulation limit ratio 10％ Frequency droop control gain coefficient 100

[0164] Table 4A-CAES power plant coefficients

[0165]

[0166]

[0167] Table 5 Other system parameters

[0168] Parameter Type Numeric Parameter Type Numeric Discount rate 8％ Initial frequency change rate limit (Hz / s) 0.35 Net residual value rate 6％ FM dead zone / Hz 0.033 Wind power forecast error ratio 10％ Load frequency adjustment factor 1.5 Load forecast error ratio 3％ Typical daily ratio 180 / 78 / 74 / 33 Steady-state frequency difference limit / Hz 0.3 Planning project period / a 40 Maximum frequency difference limit / Hz 0.5

[0169] Table 6 Source load growth in each stage

[0170] Planning Year Proportion of installed capacity of new energy Load demand ratio Average annual net load Daily average peak-to-valley difference of net load 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] In order to verify the effectiveness of the multi-stage planning method proposed in the present invention, three operating scenarios are set in the embodiment of the present invention: Scenario A: planning an energy storage power station once every 10 years, a total of 4 times; Scenario B: planning an energy storage power station once every 20 years, a total of 2 times; Scenario C: only planning once at the beginning of the first year.

[0172] Combining the above parameters and scenarios, this embodiment is carried out in accordance with the steps of the present invention in sequence, and the optimal planning scheme of the energy storage power station under each scenario A to C is obtained by solving. The A-CAES planning results of each stage are as follows: Figure 8 As shown in the figure, the life cycle cost, energy storage investment cost, energy storage cost at each stage and total system cost at each stage of each scenario are as follows: Fig. 9 As shown in (a), (b), (c) and (d).

[0173] Depend on Figure 8 It can be seen that in terms of planned capacity, as the planning cycle increases, the total planned capacity of scenarios A to C gradually decreases, and 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 and medium-capacity A-CAES (100MW and 60MW) has a smaller unit capacity and cost, and is more flexible in configuration, so it becomes a detailed choice of the configuration scheme.

[0174] Depend on Fig. 9 From (a) in Figure 2, we can see that scenario A has the lowest life cycle cost, which is reduced by 2.33×108$ and 3.21×108$ compared with scenario B and scenario C, respectively. Fig. 9 From (b) in the figure, we can see that although scenario C has the smallest total planned capacity, its energy storage construction cost is the highest and the residual cost at the end of the planning period is the lowest. This is because scenario C is only planned in the initial year, which has the problem of overinvestment, while scenarios A and B adopt phased planning, which saves energy storage investment costs and still has a certain residual cost at the end of the planning period. Figure 7 It can be seen that the total planned capacity of Scenario A and Scenario B is similar, but Scenario A is equipped with more 300MW power stations, and the overall construction schedule of Scenario A is later. Therefore, the energy storage construction cost and residual value cost of Scenario A are better than those of Scenario B.

[0175] Depend on Fig. 9From (c) and (d), we can see that, first, in the early stage of planning (S1 stage), scenarios A and B avoid the high energy storage construction cost and operation and maintenance cost incurred by scenario C due to advanced investment, among which the energy storage cost of scenario A is only 36.38% of that of scenario C. At the same time, because there are fewer A-CAES power stations built in scenario A, the power grid needs to pay a certain amount of fees to the power generator, and the extra operating costs can be covered by the saved energy storage investment costs, so that the total cost of scenario A at this stage is still lower than that of scenario C; secondly, in the mid-term planning (S2 and S3 stages), the power grid operation costs under each scenario are not much different, while scenarios A and B need to pay a certain amount of energy storage investment costs, which makes the total system cost of the two scenarios slightly higher; finally, at the end of the planning (S4 stage), all scenarios have completed the construction of A-CAES power stations, and the total system cost at this stage is relatively close.

[0176] From the above analysis, we can see that scenarios A and B consider the changes in grid regulation demand for multi-stage planning, which can avoid energy storage capacity redundancy caused by advanced investment and save energy storage investment costs. The multi-stage planning scheme considered in scenario A is more sophisticated and can further improve the economic efficiency of the system throughout its life cycle.

[0177] In order to verify the necessity of considering the variable operating conditions in the A-CAES optimization planning process of the present invention, scenarios A, D and D′ are further set in the embodiment of the present invention. Scenario A: the planning result obtained by the model of the present invention; Scenario D: the variable operating conditions of A-CAES are not considered, and the operating efficiency of the A-CAES power station is assumed to be a fixed value (i.e., the design operating efficiency); Scenario D′: based on the planning results of scenario D, the variable operating condition model of the present invention is used to recalculate the economic cost of the A-CAES power station. The planning results of scenarios A and D are shown in Figure 2. Fig.10 The economic comparison analysis of scenarios A, D and D′ is shown in Fig.11 shown.

[0178] As can be seen from the figure, scenario D assumes that the operating efficiency of A-CAES is a fixed value, rather than the variable operating efficiency of scenario A. The A-CAES power station has a more superior value ratio under scenario D, so the planned capacity of scenario D is greater than that of scenario A, which is 1960MW, in order to reduce the operating costs of the power grid. However, the efficiency estimate of A-CAES in scenario D is too optimistic. When the planning results of scenario D are substituted into the variable operating model, it is found that it needs to pay a power grid operating cost similar to that of scenario A, and needs to pay 5.63% more energy storage investment costs, resulting in a total cost higher than scenario A. This shows that: scenario D does not consider the variable operating characteristics of A-CAES, the obtained planning results are not optimal, and the full life cycle economic analysis is also inaccurate. The model proposed in the present invention can consider the operating conditions of the A-CAES power station, determine better planning results, and improve the economy of the system.

[0179] Further analysis is conducted on the impact of variable operating conditions on the system dispatch plan. The dispatch results of typical day 1 in stage S4 are selected for comparison. The output plans of A-CAES power plants in scenarios D and D′ are shown in the figure below: Fig.12 As shown, the compression condition is a negative value, the power generation condition is a positive value, and the output plan of the thermal power unit is as follows: Fig.13 shown.

[0180] Depend on Fig.12 It can be seen that in scenario D, since the variable operating conditions are not taken into account, the A-CAES power station as a whole tends to operate in a low-load, long-time charging and discharging mode, and the power generation output is mostly maintained in the range of 40% to 60%, with an average working time of 14.5 hours; while in scenario D′, in order to avoid the energy loss caused by low conversion efficiency, the output of each A-CAES power station adopts a high-load, relatively short-time charging and discharging working state, and the power generation output is basically maintained at more than 80%, with an average working time of 13.125 hours.

[0181] Depend on Fig.13 It can be seen that thermal power units G13~G15 have the best economy and are basically in full power state. G10~G12 have better economy and can also guarantee full-day operation. G5~G9 operate as supplementary output during peak load periods, while G1~G4 have poor economy and are in shutdown state.

[0182] The calculation shows that the total output of thermal power units in scenario D′ increases by 891.25MW·h compared with scenario D. This is because scenario D′ takes into account the variable operating conditions. The overall conversion efficiency of the A-CAES power station is lower than that of scenario D, and the thermal power units need to increase their output. Therefore, G8 and G12 in scenario D′ have different degrees of increase. However, scenario D's estimate of the efficiency of the A-CAES power station is too optimistic. In actual operation, there is a risk that the dispatch plan cannot be fully implemented.

[0183] Further analysis is conducted on the impact of the multi-application value of A-CAES on the planning results and the full life cycle cost. In the embodiment of the present invention, four scenarios E to H are set where the A-CAES power station does not participate in peak regulation, standby, ramp climbing, inertia and frequency regulation services. The obtained planning results and economic costs are shown in Table 7.

[0184] As shown in Table 7, due to the large peak-to-valley difference of the net load at the end of the planning period, scenario E without considering the participation of the A-CAES power station in peak regulation will not be able to meet the power balance constraint, resulting in an unsolvable planning model; in scenario F, the A-CAES power station does not provide emergency backup services, the value ratio of energy storage decreases, and the configuration capacity also decreases. At the same time, the power grid needs to pay more expensive emergency backup costs to the thermal power units, so the economic cost of scenario F increases by 4.21% compared with scenario A; similar to scenario F, the A-CAES power station in scenario G does not provide flexible ramping services, resulting in a 9.93% increase in the full life cycle cost, and the energy storage configuration capacity is also the lowest; scenario H loses the inertia support and primary frequency regulation capabilities provided by the A-CAES power station, and a large-capacity unit combination is required in certain periods, which has an adverse impact on the economic efficiency of the system operation.

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

[0186]

[0187]

[0188] From the above analysis, it can be seen that large-scale advanced compressed air energy storage has excellent peak-shaving, standby, and ramp-up application value. In the embodiment of the present invention, if A-CAES is not considered to participate in peak-shaving at the end of planning, the power balance constraint cannot be met and the model will be unsolvable. Therefore, the peak-shaving value of A-CAES is the most important of the four types of value. According to the incremental cost of the entire life cycle, the ranking of the other three types of value is climbing value, standby value, inertia and frequency regulation value.

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

[0190] Embodiment 2

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

[0192] The relevant technical solutions are the same as above and will not be described in detail here.

[0193] Embodiment 3

[0194] The present application also relates to a computer-readable storage medium having a computer program stored thereon, and the steps of the above method are implemented when the computer program is executed by a processor.

[0195] Specifically, the memory may include a high-speed random access memory, and may 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 (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0196] The relevant technical solutions are the same as above and will not be described in detail here.

[0197] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-stage optimization planning method for advanced compressed air energy storage considering multiple application values, characterized in that: include: Based on the power system topology, source and load data, generator set parameters and A-CAES unit parameters, multiple configuration schemes are obtained by using the constraints of the upper-level multi-stage A-CAES capacity optimization configuration model, wherein the configuration model takes the minimum economic cost of the entire life cycle as the goal and takes the geographical location restriction and investment cost restriction as the constraints; each configuration scheme is composed of the configuration status of each access location in each planning stage, and the configuration status of each access location is a vector composed of the configuration quantity of A-CAES units of each capacity type; Based on the configuration status of each access location in each planning stage and the configuration status of each access location in each planning stage before the planning stage, the lower typical day power grid optimization dispatching model is adopted to obtain the day-ahead dispatching cost of the power grid in the planning stage, and the full life cycle economic cost corresponding to the objective function in the configuration model is calculated based on the dispatching cost of each planning stage, wherein the dispatching model takes the minimum day-ahead dispatching cost of the power grid as the goal, and its constraints include four constraints: A-CAES variable operating condition output, A-CAES accident reserve capacity, A-CAES flexible climbing adjustment range and frequency safety. The four constraints are established by considering the application value and operation characteristics of A-CAES in four aspects: peak shaving and valley filling, accident reserve, flexible climbing and inertia support; Based on the configuration scheme corresponding to the minimum economic cost of the entire life cycle, an optimization algorithm is used to optimize the configuration scheme and realize multi-stage optimization planning of advanced compressed air energy storage.

2. The multi-stage optimization planning method for advanced compressed air energy storage according to claim 1, characterized in that: The objective function of the configuration model is: minC total =C con +C om +C op -B sal In the formula, C total is the discounted total cost, C con is the construction cost of A-CAES power station; C om is the maintenance cost of the A-CAES power station, C op is the day-ahead dispatching cost of the power grid, B sal is the residual value of the equipment at the end of the planning period; N is the number of planning stages; γ is the discount rate, For the i-th stage of construction S i The initial year, M is the number of construction sites, K is the total number of A-CAES units to be selected, c con,k is the construction unit price of type k A-CAES unit, each configuration scheme is denoted as V, vector Indicates S i Phase G j The configuration status of the point, Indicates S i Phase G j Click to add the number of k-type units, k = 1, 2, ..., K, G j represents the jth access location, T end For the planning end year, c fm,k With c vm,k are the fixed maintenance cost coefficient and floating maintenance cost coefficient of type k unit, V k y is the number of type k units in year y; is the total output of type k unit in year y; N typ is the number of typical day types; κ n is the proportion of n typical days in a year; D op is the total number of days in a year; C op,n is the day-ahead dispatch cost of the power grid on n typical days, which is the objective function of the lower-level dispatch model; N sal The total number of equipment that has not reached retirement age at the end of the planning period; LS exp,l LS use,l are the expected service life and actual service life of equipment l respectively; l is the net residual value rate of equipment l.

3. The multi-stage optimization planning method for advanced compressed air energy storage according to claim 1, characterized in that: The objective function of the scheduling model is: min C op,da =C G,r +C G,re +C G,rl +C G,ss +C N,w In the formula, C op,da , C G,r , C G,re , C G,rl , C G,ss and C N,w are the day-ahead dispatching cost of the power grid, the operation cost of thermal power, the standby cost of thermal power, the flexible ramping adjustment cost of thermal power, the on / off cost of thermal power and the penalty cost of renewable energy power abandonment; T is the total number of dispatching periods; N is the total number of dispatching periods; G 、N W 、N P are the number of thermal power units, wind farms and photovoltaic power stations respectively; are the electricity purchase cost coefficients of thermal power unit i respectively; is the active power output of thermal power unit i in period t; Δt = 1h is the unit dispatching duration in the day-ahead dispatching stage; is the start and stop status of thermal power unit i in period t; is the unit accident standby cost of thermal power unit i; is the emergency reserve capacity of thermal power unit i in period t; are the unit positive and negative flexible ramping regulation costs of thermal power unit i respectively; are respectively the positive and negative flexible ramping regulation capacities of thermal power unit i in period t; are the single startup and shutdown costs of thermal power unit i; c pen is the penalty cost coefficient for power abandonment; are the predicted and actual output of wind farm i in period t respectively. They are the predicted and actual output of photovoltaic power station i in period t respectively.

4. The advanced compressed air energy storage multi-stage optimization planning method according to claim 1, characterized in that: The constraints of the scheduling model also include: thermal power unit operation constraints, A-CAES operating condition constraints, A-CAES gas storage room constraints, A-CAES heat storage heat exchanger constraints, wind farm output constraints, photovoltaic power station output constraints, system power balance constraints, system accident standby constraints, supply guarantee and consumption promotion constraints and flexible climbing demand constraints.

5. The advanced compressed air energy storage multi-stage optimization planning method according to claim 1, characterized in that: The A-CAES variable operating condition output constraint is the compression power and power generation power of the A-CAES power station after considering the variable operating condition characteristics of A-CAES in the peak shaving and valley filling scenario, expressed as: In the formula, They represent the compression power and power generation power output by the A-CAES power station in time period t respectively; They represent the isentropic efficiency of the compression process and the power generation process respectively. They are functions of the load rate of the compressor and the expander respectively, and both are variables. Respectively represent the flow rate into the compressor and expander during time period t; λ represents the specific heat ratio of air; R g represents the ideal gas constant; n c 、n g Respectively represent the number of compressor and expander stages; represents the air temperature entering the k-th stage compressor; represents the rated compression ratio of the k-th compressor; Indicates the temperature of the air entering the first stage expander; Indicates the rated expansion ratio of the first stage expander.

6. The advanced compressed air energy storage multi-stage optimization planning method according to claim 1, characterized in that: The A-CAES accident reserve capacity constraints include: Under compression conditions: In standby mode: Under power generation conditions: In the above formula, is the emergency reserve capacity provided by the A-CAES unit in period t; x is the auxiliary variable; is the minimum compression power of the A-CAES unit; They are the minimum and maximum power generation of the A-CAES unit respectively.

7. The advanced compressed air energy storage multi-stage optimization planning method according to claim 1, characterized in that: The A-CAES flexible ramp adjustment range constraints include: (1) Under compression conditions: In the formula, are the positive and negative flexible ramping capacities provided by the A-CAES unit in time period t; Δt c2g is the time used for power generation ramp after switching from compression mode to power generation mode; r C,g is the ramp rate of power generation condition; is the maximum compression power of the A-CAES unit; x is the auxiliary variable; (2) In standby mode: In the formula, Δt o2g Indicates the time used for power generation ramp after switching from shutdown condition to power generation condition; Δt o2c Indicates the time used for compression climbing after switching from shutdown condition to compression condition; r C,c Indicates the compression condition climbing rate; (3) Under power generation conditions: In the above formula, Δt g2c Indicates the time used for compression climbing after switching from power generation mode to compression mode. is the minimum compression power of the A-CAES unit, represents the power output of the A-CAES power station in time period t, They are the minimum and maximum power generation of the A-CAES unit respectively.

8. The advanced compressed air energy storage multi-stage optimization planning method according to claim 1, characterized in that: The frequency safety constraints include the maximum frequency change rate constraint, the steady-state frequency difference constraint and the maximum frequency difference constraint, which are expressed as: In the above formula, the superscript * indicates the per-unit value. They are the maximum frequency change rate, steady-state frequency difference and maximum frequency difference when the system is subjected to the expected power disturbance at time t respectively; It is the FM dead zone; They are the maximum frequency change rate limit, the steady-state frequency difference limit and the maximum frequency difference limit respectively.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the steps of the method according to any one of claims 1 to 8.

Images