Compressed air energy storage system robust optimization scheduling method and system

Abstract

The invention relates to the technical field of compressed energy storage, in particular to a compressed air energy storage system robust optimization scheduling method and system. According to the invention, a novel power system model comprising a compressed air energy storage system and a new energy unit is established. New energy output is predicted, an error range is given, and a compressed air energy storage system robust optimization scheduling model is established by using a two-stage robust optimization method. And finally, solving by adopting a Benders decomposition method to realize robust optimization scheduling of the compressed air-containing energy storage system capable of reducing the influence of the new energy output error.

Description

Technical Field

[0001] The present disclosure relates to the technical field of compressed energy storage, and in particular to a method and system for robust optimization scheduling of a compressed air energy storage system. Background Art

[0002] In recent years, new energy power generation technologies represented by wind power and photovoltaic power generation have developed rapidly. As clean energy, these new energy sources will play a vital role in solving possible energy crises in the future and building a low-carbon power system. However, the uncertainty and low inertia of wind power and photovoltaic power generation have brought unprecedented challenges to the power system. To meet this challenge, the power system must not only improve the prediction accuracy of new energy output, but also reserve considerable backup resources. As a flexible resource, energy storage technology is widely used in the coordinated dispatch of new energy, and compressed air energy storage system is one of the most economical energy storage technologies; at the same time, considering the complexity of new energy prediction technology, it is almost impossible to obtain very accurate prediction results.

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

[0004] In view of the above problems, the present disclosure provides a method and system for robust optimization scheduling of a compressed air energy storage system.

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

[0006] Establish an optimization scheduling model for a compressed air energy storage system that includes a compressed air energy storage system and new energy units; the optimization scheduling model for the compressed air energy storage system takes maximizing the benefits of the compressed air energy storage system as the objective function; establish 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 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 and sells electricity to the power grid for profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed their own upper and lower limits;

[0007] Based on the optimized dispatching 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, based on the uncertainty of weather, the output of new energy is predicted according to weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set. The first-stage objective function is to minimize the start-up and shutdown costs of the entire system, and the start-up and shutdown instructions of the compressed air energy storage power station are obtained. The second-stage objective function is to minimize the operating cost of the compressed air energy storage system, and a two-stage robust optimization model is established.

[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] Among them, the objective function (1a) is the difference between the operating income and cost of the pressure storage power station; are the electricity price, frequency regulation, and phase regulation benefits at time t; f t Peak ,f t F ,f t Phase They are respectively the output of the compressed air energy storage system responding to the peak-shaving signal at time t, the output responding to the frequency-modulation signal, and the output responding to the phase-modulation signal.

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

[0013]

[0014]

[0015] (1b) constrain the non-negativity of relevant variables; P t n-l are the power flowing into and out of the pressure storage power station at time t, and the power flowing into the load from the new energy unit;

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

[0017] (1d) Constrain the power balance of the entire power system; η out ,η in are the efficiency of outflow and inflow to the pressure storage power station, P t G ,P tload are the output and load of conventional units at time t respectively;

[0018] (1e) and (1f) respectively describe the changes in the heat storage and gas storage states of the pressure storage power station; λ Peak ,λ F ,λ Phase are the heat storage consumed per unit output of the three output forms; μ Peak ,μ F ,μ Phase The gas storage volume consumed per unit output of the three output forms respectively;

[0019] (1g) Constrain the upper and lower limits of heat and gas storage in compressed air storage power stations;

[0020] (1h) constrain the upper limit of the power of discharge and charging of the pressure storage power station; g t,in ,g t,out Boolean variables representing whether the pressure storage power station is in charging or discharging condition at time t;

[0021] (1i) Constrained pressure storage power station operating logic.

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

[0023] Taking into account the uncertainty of the initial probability distribution, a constraint is established with the initial probability distribution value as the center and the 1-norm and ∞-norm conditions as the constraints to restrict the output probability distribution value of the new energy unit, and finally form the boundary condition of the uncertain set U. The formula is as follows:

[0024] p u ≥0,u=1,2,…,n(2a)

[0025]

[0026] Among them, p u is the probability of scenario u occurring, n is the total number of scenarios, is the initial value of the probability of scene u occurring, θ1,θ ∞ are the probability allowable deviation limits, i.e., the maximum allowable values ​​of the 1-norm and infinity norm of the probability deviation.

[0027] Furthermore, the start-up and shutdown costs of the entire system are minimized as the first-stage objective function to obtain the start-up and shutdown instructions of the compressed air energy storage power station; the operation cost of the compressed air energy storage system is minimized as the second-stage objective function to establish a two-stage robust optimization model, including:

[0028] Considering the inertia support of the compressed air energy storage power station, the first-stage objective function is to minimize the startup and shutdown cost of the entire system, and the second-stage objective function is to minimize the operating cost of the compressed air energy storage system. The formula is 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 start-up and shutdown costs of the entire system; is the cost of unit g being unloaded at time t, is the cost of starting unit g at time t, is the cost of shutting down unit g at time t;

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

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

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

[0036] Furthermore, the established compressed air energy storage system optimization scheduling model 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 for maximizing the benefits of the compressed air energy storage system; C1 is the feasible domain restricted by formulas (2f)-(2i), C2 is the feasible domain restricted by formulas (1b)-(1i), and U is the feasible domain 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 used to continuously generate feasible cuts to reduce the feasible domain or reduce the distance between the upper and lower bounds. Finally, the scheduling strategy of the compressed air energy storage system is obtained, and the compressed air energy storage system is scheduled based on the scheduling strategy.

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

[0044] The scheduling model establishment unit is used to establish an optimized scheduling model for a compressed air energy storage system including a compressed air energy storage system and a new energy unit; the optimized scheduling model for the compressed air energy storage system takes maximizing the benefits 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 output of the new energy unit are determined by natural resources; the output of the new energy unit 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 and sells electricity to the power grid for profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed their own upper and lower limits;

[0045] The two-stage robust optimization model building unit is used for the optimized dispatching model of the compressed air energy storage system, predicts the output of the new energy unit and gives the error range, and adopts the two-stage robust optimization method to establish the two-stage robust optimization model; specifically, based on the uncertainty of the weather, the output of new energy is predicted according to the weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set; the start-up and shutdown instructions of the compressed air energy storage power station are obtained by minimizing the start-up and shutdown costs of the entire system as the first-stage objective function; the operation cost of the compressed air energy storage system is minimized as the second-stage objective function, and a two-stage robust optimization model is established;

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

[0047] In a third aspect, 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 via the communication bus;

[0048] a memory storing a computer program;

[0049] The processor is used to implement the above-mentioned robust optimization scheduling method for a compressed air energy storage system when executing the computer program stored in the memory.

[0050] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned robust optimization scheduling method for a compressed air energy storage system.

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

[0052] The present invention establishes a new power system model including a compressed air energy storage system and a new energy unit. Furthermore, the output of new energy is predicted and the error range is given. The robust optimization scheduling model of the compressed air energy storage system is established by using a two-stage robust optimization method. Finally, the Benders decomposition method is used to solve the problem, and the robust optimization scheduling of the compressed air energy storage system with reduced influence of the new energy output error is realized.

[0053] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures indicated in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

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

[0057] Figure 3 It is a schematic diagram of the technical implementation process of the embodiment of the present disclosure;

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

[0059] Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

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

[0062] S101, establish an optimization scheduling model for a compressed air energy storage system including a compressed air energy storage system and a new energy unit; the optimization scheduling model for the compressed air energy storage system takes maximizing the benefits of the compressed air energy storage system as the 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 unit are determined by natural resources; the output of the new energy unit 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 and sells electricity to the power grid for profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed their own upper and lower limits;

[0063] S102, based on the compressed air energy storage system optimization scheduling model, the output of the new energy unit is predicted and the error range is given, and a two-stage robust optimization method is adopted to establish a two-stage robust optimization model; specifically, the following steps are performed: based on the uncertainty of the weather, the output of the new energy unit is predicted according to the weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set; the start and stop instructions of the compressed air energy storage power station are obtained by minimizing the start and stop cost of the entire system as the first-stage objective function; the operation cost of the compressed air energy storage system is minimized as the second-stage objective function, and a two-stage robust optimization model is established;

[0064] S103, using the Benders decomposition method, solves the two-stage robust optimization model to obtain a scheduling strategy, and schedules the compressed air energy storage system based on the scheduling strategy.

[0065] The specific implementation is as follows:

[0066] The schematic diagram of the new power system structure is as follows Figure 2 As shown, the robust optimization scheduling model of the compressed air energy storage system proposed in the present disclosure and its solution system include the following steps:

[0067] S101, establish a new power system model including a compressed air energy storage system and new energy units. The upper and lower limits of the new energy output are determined by uncertain natural resources; the new energy output can be directly supplied to the load, or it can be stored in the compressed air energy storage system, and the rest can be discarded. The compressed air energy storage system can be charged by new energy, and at the same time it can sell electricity to the power grid to make a profit; the compressed air energy storage system can also make a profit by providing auxiliary services such as frequency regulation and phase regulation; the charging and discharging power and state of charge of the compressed air energy storage system cannot exceed its own upper and lower limits. Considering the above constraints, with maximizing the benefits of the compressed air energy storage system as the objective function, an optimal scheduling model for the compressed air energy storage system under the determination of the new energy output is established.

[0068] S102, predict the output of new energy and give the error range, and use the two-stage robust optimization method to establish a robust optimization scheduling model for the compressed air energy storage system. First, predict the output of new energy based on weather resources, and fully consider the uncertainty of the weather, give the upper and lower limits of the prediction error, and form an uncertainty set. Take the minimum start-up and shutdown cost of the entire system as the first-stage objective function, and obtain the start-up and shutdown instructions of the compressed air energy storage power station; take the minimum operating cost of the compressed air energy storage system as the second-stage objective function, and establish a two-stage robust optimization model.

[0069] S103, using the Benders decomposition method to solve, to achieve the robust optimization scheduling of the compressed air energy storage system with reduced impact of new energy output error. For the two-stage robust optimization model generated above, the Benders decomposition method is used to continuously generate feasible cuts to reduce the feasible domain or reduce the distance between the upper and lower bounds, and finally obtain the scheduling strategy of the compressed air energy storage system.

[0070] Technical implementation flow chart 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 new energy output are determined by uncertain natural resources; new energy output can be directly supplied to the load, or it can be stored in the compressed air energy storage system, and the rest can be discarded. The compressed air energy storage system can be charged by new energy, and at the same time it can sell electricity to the power grid to make a profit; the compressed air energy storage system can also make a profit by providing auxiliary services such as frequency modulation and phase modulation; the charging and discharging power and state of charge of the compressed air energy storage system cannot exceed its own upper and lower limits. Considering the above constraints, with maximizing the benefits of the compressed air energy storage system as the objective function, an optimal scheduling model for the compressed air energy storage system under the determination of new energy output is established.

[0073]

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

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

[0076] First, the output of renewable energy is predicted based on weather resources, and the uncertainty of weather is fully considered. Considering the uncertainty of the initial probability distribution, a constraint is established with the initial probability distribution value as the center, and the 1-norm and ∞-norm conditions are combined to restrict the output probability distribution value of renewable energy, and finally form the boundary conditions of the uncertainty set U:

[0077] p u ≥0,u=1,2,…,n(2a)

[0078]

[0079] in is the initial value of the probability of scene u occurring, θ1,θ ∞ are the probability allowable deviation limits respectively.

[0080] A new type of power system unit combination model considering the inertia support of the compressed air energy storage power station is established. The start-up and shutdown costs of the entire system are minimized as the objective function, and the start-up and shutdown instructions of the compressed air energy storage power station are obtained as the first stage of the two-stage robust optimization model. The two-stage robust optimization model is established with the minimization of the operating cost of the compressed air energy storage system as the second stage objective function.

[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 start-up and shutdown costs of the entire system; is the cost of unit g being unloaded at time t, is the cost of starting unit g at time t, is 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 and stop logic of the units. (2i) constrains the overall inertia of the new power system; where H i,t is the inertia provided by unit i at time t, is the inertia provided by the new energy unit j at time t, H CAES The inertia provided for the pressure storage power station, H t,min is the minimum inertia required by the system at time t.

[0085] The deterministic pressure storage power station optimization operation model established in 0 and the above 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:

[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 and realize the robust optimization scheduling of compressed air energy storage system with reduced impact of new energy output error. For the two-stage robust optimization model generated above, the Benders decomposition method is used to continuously generate feasible cuts to reduce the feasible domain or reduce the distance between the upper and lower bounds, and finally obtain the scheduling strategy of compressed air energy storage system.

[0091] The solution is obtained by Benders decomposition method as shown in Table 1:

[0092] Table 1

[0093]

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

[0095] The dispatch model establishment unit 401 is used to establish an optimized dispatch model of a compressed air energy storage system including a compressed air energy storage system and a new energy unit; the optimized dispatch model of the compressed air energy storage system takes maximizing the benefits of the compressed air energy storage system as the 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 unit are determined by natural resources; the output of the new energy unit 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 and sells electricity to the power grid for profit; the charging and discharging power and the 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 compressed air energy storage system optimization scheduling model, predicts the output of the new energy unit and gives the error range, adopts the two-stage robust optimization method, and establishes the two-stage robust optimization model; specifically, it includes: based on the uncertainty of the weather, predicts the output of the new energy according to the weather resources, gives the upper and lower limits of the prediction error, and forms an uncertainty set; takes the minimum start-up and shutdown cost of the entire system as the first-stage objective function, and obtains the start-up and shutdown instructions of the compressed air energy storage power station; takes the minimum operating cost of the compressed air energy storage system as the second-stage objective function, and establishes a two-stage robust optimization model;

[0097] The scheduling solution unit 403 is used to adopt 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.

[0098] like Figure 5As shown, the present 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, storing computer programs;

[0100] The processor 501 is configured to implement the above method when executing the computer program stored in the memory 503 .

[0101] The present disclosure provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed by a processor.

[0102] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0103] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for robust optimization scheduling of a compressed air energy storage system, characterized in that: The method comprises: Establish an optimization scheduling model for a compressed air energy storage system that includes a compressed air energy storage system and new energy units; the optimization scheduling model for the compressed air energy storage system takes maximizing the benefits of the compressed air energy storage system as the objective function; establish 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 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 and sells electricity to the power grid for profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed their own upper and lower limits; Based on the optimized dispatching 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, based on the uncertainty of weather, the output of new energy is predicted according to weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set. The first-stage objective function is to minimize the start-up and shutdown costs of the entire system, and the start-up and shutdown instructions of the compressed air energy storage power station are obtained. The second-stage objective function is to minimize the operating cost of the compressed air energy storage system, and a two-stage robust optimization model is established. 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.

2. A method for robust optimization scheduling of a compressed air energy storage system according to claim 1, characterized in that: Taking maximizing the benefits of the compressed air energy storage system as the objective function, the formula is as follows: Among them, the objective function (1a) is the difference between the operating income and cost of the pressure storage power station; are the electricity price, frequency regulation, and phase regulation benefits at time t; f t Peak ,f t F ,f t Phase They are respectively the output of the compressed air energy storage system responding to the peak-shaving signal at time t, the output responding to the frequency-modulation signal, and the output responding to the phase-modulation signal.

3. A method for robust optimization scheduling of a compressed air energy storage system according to claim 1, characterized in that: The optimal scheduling model of compressed air energy storage system has the following constraints: (1b) constrain the non-negativity of relevant variables; P t n-l are the power flowing into and out of the pressure storage power station at time t, and the power flowing into the load from the new energy unit; (1c) Constrain the logical relationship between the three outputs and the total output; (1d) Constrain the power balance of the entire power system; η out ,η in are the efficiency of outflow and inflow to the pressure storage power station, P t G ,P t load are the output and load of conventional units at time t respectively; (1e) and (1f) respectively describe the changes in the heat storage and gas storage states of the pressure storage power station; λ Peak ,λ F ,λ Phase are the heat storage consumed per unit output of the three output forms; μ Peak ,μ F ,μ Phase The gas storage volume consumed per unit output of the three output forms respectively; (1g) Constrain the upper and lower limits of heat and gas storage in compressed air storage power stations; (1h) constrain the upper limit of the power of discharge and charging of the pressure storage power station; g t,in ,g t,out Boolean variables representing whether the pressure storage power station is in charging or discharging condition at time t; (1i) Constrained pressure storage power station operating logic.

4. A method for robust optimization scheduling of a compressed air energy storage system according to claim 3, characterized in that: Based on the uncertainty of weather, the output of renewable energy is predicted according to weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set, including: Taking into account the uncertainty of the initial probability distribution, a constraint is established with the initial probability distribution value as the center and the 1-norm and ∞-norm conditions as the constraints to restrict the output probability distribution value of the new energy unit, and finally form the boundary condition of the uncertain set U. The formula is as follows: p u ≥0,u=1,2,…,n(2a) Among them, p u is the probability of scenario u occurring, n is the total number of scenarios, is the initial value of the probability of scene u occurring, θ1,θ ∞ are the probability allowable deviation limits, i.e., the maximum allowable values ​​of the 1-norm and infinity norm of the probability deviation.

5. A method for robust optimization scheduling of a compressed air energy storage system according to claim 4, characterized in that: Taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function, the start-up and shutdown instructions of the compressed air energy storage power station are obtained; taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function, a two-stage robust optimization model is established, including: Considering the inertia support of the compressed air energy storage power station, the first-stage objective function is to minimize the startup and shutdown cost of the entire system, and the second-stage objective function is to minimize the operating cost of the compressed air energy storage system. The formula is as follows: at g,t -at g,t-1 =v g,t -In g,t (2h) The objective function (2e) is the sum of the start-up and shutdown costs of the entire system; is the cost of unit g being unloaded at time t, is the cost of starting unit g at time t, is the cost of shutting down unit g at time t; (2f) Constrain the upper and lower limits of the output of conventional units; (2g) and (2h) constrain the start and stop logic of the unit; (2i) constrains the overall inertia of the power system; where H i,t is the inertia provided by unit i at time t, is the inertia provided by the new energy unit j at time t, H CAES The inertia provided for the pressure storage power station is used to reflect the inertia support of the pressure storage power station, H t,min is the minimum inertia required by the system at time t.

6. A method for robust optimization scheduling of a compressed air energy storage system according to claim 5, characterized in that: The established compressed air energy storage system optimization scheduling model and 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: s.t.u i,t ,v i,t ,w i,t ∈C1(2k) F1 is the objective function shown in formula (2e), F2 is the objective function for maximizing the benefits of the compressed air energy storage system; C1 is the feasible domain restricted by formulas (2f)-(2i), C2 is the feasible domain restricted by formulas (1b)-(1i), and U is the feasible domain restricted by formulas (2a)-(2d).

7. A method for robust optimization scheduling of a compressed air energy storage system according to claim 1, characterized in that: 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: For the two-stage robust optimization model, the Benders decomposition method is used to continuously generate feasible cuts to reduce the feasible domain or reduce the distance between the upper and lower bounds. Finally, the scheduling strategy of the compressed air energy storage system is obtained, and the compressed air energy storage system is scheduled based on the scheduling strategy.

8. A robust optimization scheduling system for a compressed air energy storage system, characterized in that: include: Scheduling model building unit, two-stage robust optimization model building unit and scheduling solution unit; The scheduling model establishment unit is used to establish an optimized scheduling model for a compressed air energy storage system including a compressed air energy storage system and a new energy unit; the optimized scheduling model for the compressed air energy storage system takes maximizing the benefits 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 output of the new energy unit are determined by natural resources; the output of the new energy unit 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 and sells electricity to the power grid for profit; the charging and discharging power and the state of charge of the compressed air energy storage system do not exceed their own upper and lower limits; The two-stage robust optimization model building unit is used for the compressed air energy storage system optimization scheduling model, predicts the output of new energy units and gives the error range, and adopts the two-stage robust optimization method to establish a two-stage robust optimization model; specifically, based on the uncertainty of the weather, the output of new energy is predicted according to the weather resources, and the upper and lower limits of the prediction error are given to form an uncertainty set; Taking the minimum start-up and shutdown cost of the entire system as the first-stage objective function, the start-up and shutdown instructions of the compressed air energy storage power station are obtained; taking the minimum operating cost of the compressed air energy storage system as the second-stage objective function, a two-stage robust optimization model is established; The solving scheduling unit is used to adopt the Benders decomposition method to solve the two-stage robust optimization model to obtain the scheduling strategy, and schedule the compressed air energy storage system based on the scheduling strategy.

9. An electronic device, characterized in that: It 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; a memory storing a computer program; A processor, for implementing a robust optimization scheduling method for a compressed air energy storage system as described in any one of claims 1 to 7 when executing a computer program stored in a memory.

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

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