Robust optimization scheduling method and system for compressed air energy storage system

Abstract

The present disclosure relates to the technical field of compressed energy storage, and particularly relates to a compressed air energy storage system robust optimization scheduling method and system. The present disclosure establishes a new type of power system model containing a compressed air energy storage system and a new energy unit. The output of the new energy is predicted and an error range is given. A two-stage robust optimization method is used to establish a compressed air energy storage system robust optimization scheduling model. Finally, the Benders decomposition method is used for solving, so as to realize the robust optimization scheduling of the compressed air energy storage system containing the new energy output error influence reduction.

Description

Technical Field

[0001] This disclosure relates to the field of compressed energy storage technology, and in particular to a robust optimization scheduling method and system for compressed air energy storage systems. Background Technology

[0002] In recent years, new energy power generation technologies, represented by wind power and photovoltaic power generation, have developed rapidly. As clean energy sources, these new energy sources will play a crucial role in addressing potential future energy crises and building low-carbon power systems. However, the uncertainties and low inertia of wind and photovoltaic power generation have brought unprecedented challenges to the power system. To address this challenge, the power system not only needs to improve the accuracy of new energy output forecasts but also needs to reserve substantial backup resources. As a flexible resource, energy storage technology is widely used in the coordinated dispatch of new energy sources, and compressed air energy storage systems are among the most economical energy storage technologies. At the same time, considering the complexity of new energy forecasting technology, obtaining highly accurate forecast results is almost impossible.

[0003] In summary, how to achieve economical scheduling of compressed air energy storage systems and ensure their feasibility when the forecast results of new energy output have errors is an urgent problem to be solved; therefore, it is necessary to establish a robust optimization scheduling method model and solution system for compressed air energy storage systems. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure provides a robust optimization scheduling method and system for compressed air energy storage systems.

[0005] In a first aspect, a robust optimization scheduling method for a compressed air energy storage system is provided, the method comprising:

[0006] An optimal scheduling model for a compressed air energy storage system (CAES) including CAES and renewable energy units is established. The objective function of the CAES optimization scheduling model is to maximize the revenue of the CAES. Constraints are established under the following conditions: the upper and lower limits of the renewable energy unit's output are determined by natural resources; the renewable energy unit's output is directly supplied to the load or stored in the CAES; the CAES is charged by renewable energy sources and simultaneously sells electricity to the grid for profit; the charging and discharging power and state of charge of the CAES do not exceed their own upper and lower limits.

[0007] Based on the optimized scheduling model of compressed air energy storage system, the output of new energy units is predicted and the error range is given. A two-stage robust optimization method is adopted to establish a two-stage robust optimization model. Specifically, it includes: based on the uncertainty of weather, predicting the output of new energy units according to weather resources, giving the upper and lower limits of prediction error, and forming an uncertainty set; taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function to obtain the start-up and shutdown commands of the compressed air energy storage power station; and taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function to establish a two-stage robust optimization model.

[0008] The Benders decomposition method is used to solve the two-stage robust optimization model to obtain the scheduling strategy, and the compressed air energy storage system is scheduled based on the scheduling strategy.

[0009] Furthermore, taking maximizing the benefits of the compressed air energy storage system as the objective function, the formula is as follows:

[0010]

[0011] Wherein, the objective function (1a) is the difference between the operating revenue and cost of the pressure storage power station; These represent the electricity price, frequency regulation revenue, and phase regulation revenue at time t, respectively; f t Peak ,f t F ,f t Phase These represent the output power of the compressed air energy storage system at time t in response to the peak-shaving signal, the output power in response to the frequency modulation signal, and the output power in response to the phase modulation signal.

[0012] Furthermore, the optimized scheduling model for the compressed air energy storage system has the following constraints:

[0013]

[0014]

[0015] (1b) Constrain the nonnegativity of relevant variables; P t n-l These represent the power flowing into and out of the gas storage power station at time t, and the power flowing into the load from the new energy unit, respectively.

[0016] (1c) Constrain the logical relationship between the three types of output and the total output;

[0017] (1d) Constrain the power balance of the entire power system; η out ,η in The outflow and inflow efficiencies of the pressurized energy storage power station are P and P, respectively. t G ,P tload These represent the output and load of the conventional generating units at time t, respectively.

[0018] (1e) and (1f) describe the changes in the thermal and gas storage states of the compressed air storage power station, respectively; λ Peak ,λ F ,λ Phase These represent the heat storage consumed per unit output for the three different output modes; μ Peak ,μ F ,μ Phase These represent the gas storage consumption per unit output for the three different output modes;

[0019] (1g) Limits the upper and lower limits of thermal and gas storage in pressure-controlled energy storage power plants;

[0020] (1h) Limits the power output of the voltage-storage power station for discharging and charging; g t,in ,g t,out These are Boolean variables representing whether the pneumatic energy storage power station is in charging or discharging mode at time t;

[0021] (1i) Constraints the operating logic of the pressure storage power station.

[0022] Furthermore, based on the uncertainty of weather, the output of new energy sources is predicted according to weather resources, and upper and lower limits of prediction error are given, forming an uncertainty set, including:

[0023] Considering the uncertainty of the initial probability distribution, a boundary condition is established centered on the initial probability distribution value, combining the 1-norm and ∞-norm conditions as constraints, to limit the output probability distribution value of the new energy unit, ultimately forming the uncertainty set U. The formula is as follows:

[0024] p u ≥0, u=1,2,…,n(2a)

[0025]

[0026] Where, p u Let be the probability of scenario u occurring, and n be the total number of scenarios. Let θ1, θ be the initial probabilities of scenario u occurring. ∞ These are the permissible deviation limits for probability, namely the maximum permissible values ​​of the 1-norm and the infinite norm of the probability deviation.

[0027] Furthermore, the first-stage objective function is to minimize the overall system start-up and shutdown cost, thereby obtaining the start-up and shutdown commands for the compressed air energy storage power station; the second-stage objective function is to minimize the operating cost of the compressed air energy storage system, establishing a two-stage robust optimization model, including:

[0028] Considering the inertia support of the compressed air energy storage power station, the objective function for the first stage is to minimize the start-up and shutdown cost of the entire system, and the objective function for the second stage is to minimize the operating cost of the compressed air energy storage system. The formulas are as follows:

[0029]

[0030] u g,t -u g,t-1 =v g,t -w g,t (2h)

[0031]

[0032] The objective function (2e) is the sum of the unit start-up and shutdown costs of the entire system; where Let g be the cost of unit g being unloaded at time t. The cost of starting up unit g at time t. The cost of shutting down unit g at time t;

[0033] (2f) Constrain the upper and lower limits of output of conventional units;

[0034] (2g) and (2h) constrain the start-up and shutdown logic of the unit;

[0035] (2i) Constrain the overall inertia of the power system; where H i,t The inertia provided by unit i at time t. H is the inertia provided by the new energy unit j at time t. CAES The inertia provided to the pneumatic storage power station, H, is used to reflect the inertia support of the pneumatic storage power station. t,min Let be the minimum inertia required by the system at time t.

[0036] Furthermore, the established optimized scheduling model of the compressed air energy storage system and the uncertainty set are combined as the second stage of the two-stage robust optimization model, and the two-stage robust optimization model is established as follows:

[0037]

[0038] stu i,t ,v i,t ,w i,t ∈C1(2k)

[0039]

[0040] F1 is the objective function shown in formula (2e), F2 is the objective function to maximize the benefits of the compressed air energy storage system; C1 is the feasible region restricted by formulas (2f)-(2i), C2 is the feasible region restricted by formulas (1b)-(1i), and U is the feasible region restricted by formulas (2a)-(2d).

[0041] Furthermore, the Benders decomposition method is used to solve the two-stage robust optimization model to obtain the scheduling strategy, and the compressed air energy storage system is scheduled based on the scheduling strategy, including:

[0042] For the two-stage robust optimization model, the Benders decomposition method is adopted. By continuously generating feasible cuts to reduce the feasible region or the distance between the upper and lower bounds, the scheduling strategy of the compressed air energy storage system is finally obtained, and the compressed air energy storage system is scheduled based on the scheduling strategy.

[0043] Secondly, a robust optimization scheduling system for compressed air energy storage system includes: a scheduling model establishment unit, a two-stage robust optimization model establishment unit, and a scheduling solution unit;

[0044] The scheduling model establishment unit is used to establish an optimal scheduling model for a compressed air energy storage system (CAES) that includes CAES and renewable energy units. The CAES optimization scheduling model takes maximizing the revenue of the CAES as its objective function. Constraints are established through the following conditions: the upper and lower limits of the renewable energy unit's output are determined by natural resources; the renewable energy unit's output is directly supplied to the load or stored in the CAES; the CAES is charged by renewable energy sources and simultaneously sells electricity to the grid for profit; the charging and discharging power and state of charge of the CAES do not exceed their own upper and lower limits.

[0045] The two-stage robust optimization model establishment unit is used for the optimized scheduling model of the compressed air energy storage system. It predicts the output of new energy units and gives the error range. The two-stage robust optimization method is used to establish a two-stage robust optimization model. Specifically, it includes: based on the uncertainty of weather, predicting the output of new energy units according to weather resources, giving the upper and lower limits of prediction error, and forming an uncertainty set; taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function to obtain the start-up and shutdown commands of the compressed air energy storage power station; and taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function to establish a two-stage robust optimization model.

[0046] The scheduling unit is used to solve the two-stage robust optimization model using the Benders decomposition method to obtain the scheduling strategy, and to schedule the compressed air energy storage system based on the scheduling strategy.

[0047] Thirdly, an electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0048] Memory, which stores computer programs;

[0049] When the processor executes the computer program stored in the memory, it implements the aforementioned robust optimization scheduling method for a compressed air energy storage system.

[0050] Fourthly, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned robust optimization scheduling method for a compressed air energy storage system.

[0051] This disclosure includes at least the following beneficial effects:

[0052] This disclosure establishes a novel power system model incorporating a compressed air energy storage system and renewable energy units. Furthermore, it predicts the renewable energy output and provides an error range, then uses a two-stage robust optimization method to establish a robust optimal scheduling model for the compressed air energy storage system. Finally, it employs the Benders decomposition method to solve the model, achieving robust optimal scheduling of the compressed air energy storage system while minimizing the impact of renewable energy output errors.

[0053] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the scheduling method flow according to an embodiment of the present disclosure;

[0056] Figure 2 This is a schematic diagram of the novel power system structure according to an embodiment of the present disclosure;

[0057] Figure 3 This is a schematic diagram illustrating the technical implementation process of an embodiment of this disclosure;

[0058] Figure 4 This is a schematic diagram of the scheduling system architecture according to an embodiment of the present disclosure;

[0059] Figure 5 This is a schematic diagram of the electronic device structure according to an embodiment of the present disclosure. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0061] like Figure 1 As shown, a robust optimization scheduling method for a compressed air energy storage system includes:

[0062] S101, establish an optimal scheduling model for a compressed air energy storage system (CAES) that includes CAES and renewable energy units. The CAES optimization scheduling model takes maximizing the revenue of the CAES as its objective function. Constraints for the new power system model are established under the following conditions: the upper and lower limits of renewable energy unit output are determined by natural resources; the renewable energy unit output is directly supplied to the load or stored in the CAES; the CAES is charged by renewable energy sources and simultaneously sells electricity to the grid for profit; the charging and discharging power and state of charge of the CAES do not exceed their own upper and lower limits.

[0063] S102, based on the optimized scheduling model of the compressed air energy storage system, predicts the output of new energy units and gives the error range. A two-stage robust optimization method is adopted to establish a two-stage robust optimization model. Specifically, it includes: based on the uncertainty of weather, predicting the output of new energy according to weather resources, giving the upper and lower limits of prediction error, and forming an uncertainty set; taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function to obtain the start-up and shutdown commands of the compressed air energy storage power station; and taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function to establish a two-stage robust optimization model.

[0064] S103. The Benders decomposition method is used to solve the two-stage robust optimization model to obtain the scheduling strategy, and the compressed air energy storage system is scheduled based on the scheduling strategy.

[0065] The specific implementation details are as follows:

[0066] A schematic diagram of the new power system structure is shown below. Figure 2 As shown, the robust optimization scheduling model for compressed air energy storage systems and its solution system proposed in this disclosure include the following steps:

[0067] S101. Establish a novel power system model incorporating a compressed air energy storage system (CAES) and renewable energy units. The upper and lower limits of renewable energy output are determined by uncertain natural resources; renewable energy output can be directly supplied to the load or stored in the CAES, with the remainder being discarded. The CAES can be charged by renewable energy sources and can also profit by selling electricity to the grid; the CAES can also profit by providing ancillary services such as frequency regulation and phase regulation; the charging and discharging power and state of charge of the CAES cannot exceed their own upper and lower limits. Considering these constraints, and with maximizing the revenue of the CAES as the objective function, establish an optimal scheduling model for the CAES under a given renewable energy output.

[0068] S102. The power output of new energy sources is predicted, and an error range is given. A robust optimization scheduling model for the compressed air energy storage system is established using a two-stage robust optimization method. First, the power output of new energy sources is predicted based on weather resources, and the uncertainty of weather is fully considered, giving upper and lower limits for the prediction error, forming an uncertainty set. The first-stage objective function is to minimize the start-up and shutdown cost of the entire system, obtaining the start-up and shutdown commands for the compressed air energy storage power station. The second-stage objective function is to minimize the operating cost of the compressed air energy storage system, establishing a two-stage robust optimization model.

[0069] S103 employs the Benders decomposition method to achieve robust optimization scheduling of the compressed air energy storage system, reducing the impact of new energy output errors. For the generated two-stage robust optimization model, the Benders decomposition method is used to continuously generate feasible cuts to reduce the feasible region or the distance between the upper and lower bounds, ultimately yielding the scheduling strategy for the compressed air energy storage system.

[0070] Technical implementation flowchart as follows Figure 3 As shown, the formulas for each part are as follows:

[0071] S101, Establish a new power system model:

[0072] The upper and lower limits of renewable energy output are determined by uncertain natural resources. Renewable energy output can be directly supplied to the load or stored in a compressed air energy storage system (CAES), with any surplus being discarded. CAES can be charged by renewable energy sources and can also profit by selling electricity to the grid. CAES can also profit by providing ancillary services such as frequency regulation and phase regulation. The charging and discharging power and state of charge of the CAES cannot exceed their own upper and lower limits. Considering these constraints, an optimal scheduling model for the CAES under a given renewable energy output is established, with the objective function of maximizing the revenue of the CAES.

[0073]

[0074] The objective function (1a) is the difference between the operating revenue and cost of the pressure storage power station; These represent the electricity price, frequency regulation revenue, and phase regulation revenue at time t, respectively; f t Peak ,f t F ,f t Phase These represent the output power of the compressed air energy storage system at time t in response to the peak-shaving signal, the output power in response to the frequency modulation signal, and the output power in response to the phase modulation signal. (1b) The non-negativity of the relevant variables is constrained; P t n-l These represent the power flowing into and out of the power storage station at time t, and the power flowing into the load from the new energy units, respectively. (1c) Constrains the logical relationship between the three types of output and the total output. (1d) Constrains the power balance of the entire new power system; η out ,η in The outflow and inflow efficiencies of the pressurized energy storage power station are P and P, respectively. t G ,P t load λ represents the output and load of the conventional units at time t. (1e) and (1f) describe the changes in the thermal and gas storage states of the pressurized gas storage power station, respectively; λ Peak ,λ F ,λ Phase These represent the heat storage consumed per unit output for the three different output modes; μ Peak ,μ F ,μ Phase These represent the gas storage consumption per unit output for the three output modes. (1f) constrains the upper and lower limits of thermal and gas storage in the pressurized storage power station. (1h) constrains the upper limit of discharge and charging power in the pressurized storage power station; g t,in ,g t,out These represent Boolean variables indicating whether the pneumatic energy storage power station is in charging or discharging mode at time t. (1i) constrains the operating logic of the pneumatic energy storage power station.

[0075] S102, Establish a two-stage robust optimization model:

[0076] First, the output of new energy sources is predicted based on weather resources, taking into full account the uncertainties of weather. Considering the uncertainty of the initial probability distribution, a boundary condition is established centered on the initial probability distribution value, using both 1-norm and ∞-norm conditions as constraints to limit the probability distribution value of new energy output, ultimately forming the boundary condition for the uncertainty set U:

[0077] p u ≥0, u=1,2,…,n(2a)

[0078]

[0079] in Let θ1, θ be the initial probabilities of scenario u occurring. ∞ These are the probability tolerance limits.

[0080] A novel power system unit combination model considering the inertia support of compressed air energy storage power stations is established. The first stage of the two-stage robust optimization model is to obtain the start-up and shutdown commands of the compressed air energy storage power stations with the objective function of minimizing the start-up and shutdown cost of the entire system. The second stage of the two-stage robust optimization model is to establish the objective function of minimizing the operating cost of the compressed air energy storage system.

[0081]

[0082] u g,t -u g,t-1 =v g,t -w g,t (2h)

[0083]

[0084] The objective function (2e) is the sum of the unit start-up and shutdown costs of the entire system; where Let g be the cost of unit g being unloaded at time t. The cost of starting up unit g at time t. Let H be the cost of shutting down unit g at time t. (2f) constrains the upper and lower limits of the output of conventional units. (2g) and (2h) constrain the start-up and shutdown logic of the units. (2i) constrains the overall inertia of the new power system; where H i,t The inertia provided by unit i at time t. H is the inertia provided by the new energy unit j at time t. CAES The inertia provided by the pneumatic storage power station, H t,min Let be the minimum inertia required by the system at time t.

[0085] The deterministic pressure-storage power station optimization operation model established in step 0 is combined with the aforementioned uncertainty set as the second stage of the two-stage robust optimization model, and the two-stage robust optimization model is established as follows:

[0086]

[0087] stu i,t ,v i,t ,w i,t ∈C1(2k)

[0088]

[0089] S103, Solve the two-stage robust optimization model:

[0090] The Benders decomposition method is used to solve the problem, achieving robust optimization scheduling of compressed air energy storage systems to reduce the impact of new energy output errors. For the generated two-stage robust optimization model, the Benders decomposition method is employed to continuously generate feasible cuts to reduce the feasible region or the distance between the upper and lower bounds, ultimately yielding the scheduling strategy for the compressed air energy storage system.

[0091] The solutions obtained using the Benders decomposition method are shown in Table 1.

[0092] Table 1

[0093]

[0094] like Figure 4 As shown, a robust optimization scheduling system for compressed air energy storage system includes: a scheduling model establishment unit 401, a two-stage robust optimization model establishment unit 402, and a scheduling solution unit 403;

[0095] The scheduling model establishment unit 401 is used to establish an optimal scheduling model for the compressed air energy storage system, which includes the compressed air energy storage system and new energy units. The optimal scheduling model for the compressed air energy storage system takes maximizing the revenue of the compressed air energy storage system as its objective function. The constraints of the new power system model are established through the following conditions: the upper and lower limits of the output of the new energy units are determined by natural resources; the output of the new energy units is directly supplied to the load or stored in the compressed air energy storage system; the compressed air energy storage system is charged by new energy sources and simultaneously sells electricity to the grid for profit; the charging and discharging power and state of charge of the compressed air energy storage system do not exceed their own upper and lower limits.

[0096] The two-stage robust optimization model establishment unit 402 is used for the optimized scheduling model of the compressed air energy storage system. It predicts the output of new energy units and gives the error range. The two-stage robust optimization method is used to establish a two-stage robust optimization model. Specifically, it includes: based on the uncertainty of weather, predicting the output of new energy according to weather resources, giving the upper and lower limits of prediction error, and forming an uncertainty set; taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function to obtain the start-up and shutdown commands of the compressed air energy storage power station; and taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function to establish a two-stage robust optimization model.

[0097] The scheduling unit 403 is used to solve the two-stage robust optimization model using the Benders decomposition method to obtain the scheduling strategy, and to schedule the compressed air energy storage system based on the scheduling strategy.

[0098] like Figure 5As shown, this disclosure provides an electronic device, including a processor 501, a communication interface 502, a memory 503, and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other through the communication bus 504.

[0099] Memory 503 stores computer programs;

[0100] The processor 501 implements the above method when executing a computer program stored in the memory 503.

[0101] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0102] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0103] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0104] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A robust optimization scheduling method for compressed air energy storage system, characterized in that, The method comprises: An air compression energy storage system optimal scheduling model is established, which takes maximizing the benefit of the air compression energy storage system as an objective function, and establishes constraints through the following conditions: the upper and lower limits of the output of the new energy unit are determined by natural resources; the output of the new energy unit directly supplies the load or is stored in the air compression energy storage system; the air compression energy storage system is charged by the new energy unit and sells electricity to the power grid to make a profit; the charging and discharging power and the state of charge of the air compression energy storage system do not exceed the upper and lower limits thereof; Based on the air compression energy storage system optimal scheduling model, the output of the new energy unit is predicted and an error range is given, a two-stage robust optimization model is established by using a two-stage robust optimization method, and specifically comprises: based on the uncertainty of the weather, the output of the new energy unit is predicted according to the weather resources, the upper and lower limits of the prediction error are given, and an uncertainty set is formed; taking the minimum start-stop cost of the whole system as the first-stage objective function, the start-stop instruction of the air compression energy storage system is obtained; taking the minimum operation cost of the air compression energy storage system as the second-stage objective function, the two-stage robust optimization model is established; The scheduling strategy is obtained by solving the two-stage robust optimization model by using a Benders decomposition method, and the air compression energy storage system is scheduled based on the scheduling strategy.

2. The robust optimization scheduling method of the air compression energy storage system according to claim 1, characterized in that the objective function is to maximize the benefit of the air compression energy storage system, and the formula is as follows:

3. The robust optimization scheduling method of the air compression energy storage system according to claim 1, characterized in that the air compression energy storage system optimal scheduling model has the following constraints: （1a） Wherein, the target function (1a) is the difference between the operation income and cost of the pressure storage power station; respectively The price of electricity at the moment, frequency modulation, phase modulation income; respectively The output of the response peak shaving signal, the output of the response frequency modulation signal, and the output of the response phase modulation signal at the moment; is The power flowing into the pressure storage power station at the moment. (1c) constraints on the logical relationship between the three outputs and the total output; (1g) constraints on the upper and lower limits of the heat storage and gas storage of the air compression energy storage system; （1b） （1c） （1d） （1e） （1f） （1g） （1h） （1i） wherein, respectively, a compressed air energy storage system the output of the response frequency modulation signal, the output of the response frequency modulation signal, and the output of the response frequency modulation signal. (1b) constraining the non-negativity of the relevant variables; respectively the power flowing into and out of the storage power plant at the moment and the power flowing into the load from the new energy units; (1i) constraints on the working condition logic of the air compression energy storage system. (1d) constraining the power balance of the entire power system; efficiencies of the outflow and inflow pumped storage power stations, respectively, the conventional unit output and the load at the time instant, respectively.​ (1e) and (1f) describe the change of the heat storage and gas storage state of the pressure storage power station, respectively; Qh, Qg, and Qgh are the heat storage amount, gas storage amount, and combined heat and gas storage amount consumed per unit output of the three output forms, respectively. Qh, Qg, and Qgh are the heat storage amount, gas storage amount, and combined heat and gas storage amount consumed per unit output of the three output forms, respectively.

4. The robust optimization scheduling method of the air compression energy storage system according to claim 3, characterized in that based on the uncertainty of the weather, the output of the new energy unit is predicted according to the weather resources, the upper and lower limits of the prediction error are given, and an uncertainty set is formed, comprising: (1h) constraining the power upper limit of discharging and charging of the pressure storage power plant; respectively represent a Boolean variable indicating whether the pressure storage power plant is in charging or discharging regime at the instant 5. The robust optimization scheduling method of the air compression energy storage system according to claim 4, characterized in that the minimum start-stop cost of the whole system is taken as the first-stage objective function to obtain the start-stop instruction of the air compression energy storage system, and the minimum operation cost of the air compression energy storage system is taken as the second-stage objective function to establish the two-stage robust optimization model, comprising: Considering the inertia support of the air compression energy storage system, the minimum start-stop cost of the whole system is taken as the first-stage objective function, and the minimum operation cost of the air compression energy storage system is taken as the second-stage objective function, and the formula of the minimum start-stop cost of the whole system is as follows: (2f) constraints on the upper and lower limits of the output of the conventional unit; Considering the uncertainty of the initial probability distribution, the 1-norm and - norm conditions are established as constraints to limit the output probability distribution value of new energy units, and the boundary conditions of the uncertainty set are finally formed , as follows: （2a） （2b） （2c） （2d） where p u is the probability of scenario u occurring, n is the total number of scenarios, is the probability of scenario occurring, and are the probability tolerance limits, respectively, the 1-norm and infinity-norm allowed maximums of the probability deviation. (2g) and (2h) constraints on the start-stop logic of the unit; 6. The robust optimization scheduling method of the air compression energy storage system according to claim 5, characterized in that the established air compression energy storage system optimal scheduling model and the uncertainty set are combined as the second stage of the two-stage robust optimization model. ​ （2e） （2f） （2g） （2h） （2i） The objective function (2e) is the sum of the unit start-stop costs of the whole system; wherein is the unit start cost of the unit at the time , is the unit stop cost of the unit at the time , is the unit start cost of the unit at the time ; ​ ​ (2i) make a constraint on the overall inertia of the power system; wherein is the inertia provided by the new energy units at the moment is the inertia provided by the new energy units at the moment is the inertia provided by the new energy units at the moment is the inertia provided by the new energy units is the minimum inertia required by the system at the moment is the minimum inertia required by the system at the moment ​ ​ 7. The robust optimization scheduling method of compressed air energy storage systems according to claim 1, characterized in that, the Benders decomposition method is used to solve the two-stage robust optimization model to obtain a scheduling strategy, and the compressed air energy storage system is scheduled based on the scheduling strategy, including: For the two-stage robust optimization model, the Benders decomposition method is used to continuously generate a feasible cut to reduce the feasible region or reduce the distance between the upper and lower bounds, and finally obtain the scheduling strategy of the compressed air energy storage system, and schedule the compressed air energy storage system based on the scheduling strategy.

8. A compressed air energy storage system robust optimization scheduling system, characterized in that, It includes: a scheduling model establishment unit, a two-stage robust optimization model establishment unit, and a scheduling solution unit; the scheduling model establishment unit is used to establish a compressed air energy storage system optimization scheduling model containing a compressed air energy storage system and a new energy unit; the compressed air energy storage system optimization scheduling model takes maximizing the benefit of the compressed air energy storage system as the objective function; the constraints are established through the following conditions: the upper and lower limits of the new energy unit output are determined by natural resources; the new energy unit output directly supplies the load or is stored in the compressed air energy storage system; the compressed air energy storage system charges through the new energy unit and sells electricity to the grid to make a profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed its upper and lower limits; the two-stage robust optimization model establishment unit is used to predict the output of the new energy unit and give the error range based on the compressed air energy storage system optimization scheduling model, and a two-stage robust optimization model is established by using the two-stage robust optimization method; specifically including: based on the uncertainty of the weather, the output of the new energy unit is predicted according to the weather resources, the upper and lower limits of the prediction error are given, and an uncertainty set is formed; taking the minimum start-stop cost of the entire system as the first-stage objective function to obtain the start-stop instruction of the compressed air energy storage station; taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function to establish a two-stage robust optimization model; the scheduling solution unit is used to use the Benders decomposition method to solve the two-stage robust optimization model to obtain a scheduling strategy, and schedule the compressed air energy storage system based on the scheduling strategy.

9. An electronic device, comprising: It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory stores a computer program; the processor is used to execute the computer program stored on the memory, and realizes the robust optimization scheduling method of the compressed air energy storage system according to any one of claims 1-7.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to realize the robust optimization scheduling method of the compressed air energy storage system according to any one of claims 1-7.

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