Power distribution network random scheduling method considering advanced adiabatic compressed air energy storage

Abstract

The invention relates to a power distribution network random scheduling method considering advanced adiabatic compressed air energy storage, and the method comprises the steps: carrying out the sampling processing of photovoltaic prediction output data through employing a Latin hypercube sampling method, generating an uncertain output scene, and determining a representative output scene; establishing a scheduling model objective function of the advanced adiabatic compressed air energy storage; establishing related constraint conditions of a power distribution network alternating current power flow constraint and an advanced adiabatic compressed air energy storage scheduling model constraint based on a second-order cone model; and establishing a mixed integer linear programming model, solving and optimizing the mixed integer linear programming model, and obtaining a power distribution network random scheduling result considering advanced adiabatic compressed air energy storage. Therefore, the problem that the proposed scheduling strategy cannot meet the actual operation requirement of the power distribution network due to the fact that the related technology does not consider the influence of the uncertain output of the renewable energy on the scheduling operation of the power distribution network containing the advanced adiabatic compressed air energy storage is solved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage systems, and particularly relates to a stochastic scheduling method for a distribution network considering advanced adiabatic compressed air energy storage. Background Art

[0002] Due to the increasingly severe problems such as environmental crisis and resource shortage, vigorously developing and utilizing renewable energy sources such as wind power and photovoltaic power has become the strategic consensus of governments of various countries. With a large number of new energy power stations and energy storage devices such as photovoltaic power and wind power being incorporated into the distribution network, how to reasonably utilize flexible resources to achieve the optimal scheduling of the distribution network and improve the operating economy and security of the distribution network has become an important problem that needs to be solved urgently at present.

[0003] In related technologies, large-scale energy storage is an important method for suppressing the fluctuations of new energy output and solving the problem of large-scale utilization of wind power and photovoltaic power. Compressed air energy storage and pumped-storage energy storage are currently relatively mature large-scale energy storage technologies. Compared with pumped-storage energy storage, compressed air energy storage has less strict requirements on geographical location and can be more flexibly applied to various scenarios. Advanced adiabatic compressed air energy storage recovers the compression heat to replace fuel combustion on the basis of traditional compressed air energy storage technology, further improving the operating efficiency and economy of the system, and realizing no combustion and zero carbon emissions during the operation process. Therefore, advanced adiabatic compressed air energy storage is also considered to be one of the large-scale energy storage technologies with the most development potential at present.

[0004] Compared with the transmission network, the distribution network at the end of the power system has obvious deficiencies in coping with uncertain weather. However, in the research on the economic dispatch problem of the distribution network containing an advanced adiabatic compressed air energy storage system in related technologies, the impact of the uncertain output of renewable energy on the dispatch operation of the distribution network containing advanced adiabatic compressed air energy storage is not considered. The inherent volatility and intermittency of new energy sources such as wind energy and solar energy may cause prominent problems such as reverse power flow, overcurrent, and node voltage over-limit in the distribution network, bringing technical challenges to the safe dispatch operation of the distribution network, and may lead to the proposed dispatch strategy being unable to meet the actual operation requirements of the distribution network, which urgently needs to be improved. Summary of the Invention

[0005] The present application provides a stochastic scheduling method for a distribution network considering advanced adiabatic compressed air energy storage to solve the problems that in the research on the economic dispatch problem of the distribution network containing an advanced adiabatic compressed air energy storage system in related technologies, the impact of the uncertain output of renewable energy on the dispatch operation of the distribution network containing advanced adiabatic compressed air energy storage is not considered, and the inherent volatility and intermittency of new energy sources such as wind energy and solar energy may cause prominent problems such as reverse power flow, overcurrent, and node voltage over-limit in the distribution network, bringing technical challenges to the safe dispatch operation of the distribution network, and may lead to the proposed dispatch strategy being unable to meet the actual operation requirements of the distribution network.

[0006] The first aspect of the present application provides a method for stochastic scheduling of a distribution network considering advanced adiabatic compressed air energy storage, including the following steps: sampling the photovoltaic predicted output data by using the Latin hypercube sampling method to generate uncertain output scenarios, and reducing the uncertain scenarios by using the scenario reduction method to determine representative output scenarios; based on the representative output scenarios, establishing an objective function of a scheduling model for advanced adiabatic compressed air energy storage, including at least one of the electricity purchase cost, the cost of wind and light abandonment, and the operation cost of advanced adiabatic compressed air energy storage; establishing relevant constraint conditions based on the second-order cone model for the AC power flow constraint of the distribution network and the scheduling model constraint of advanced adiabatic compressed air energy storage; based on the scheduling model objective function and the relevant constraint conditions, establishing a mixed-integer linear programming model, solving and optimizing the mixed-integer linear programming model to obtain the stochastic scheduling result of the distribution network considering advanced adiabatic compressed air energy storage.

[0007] Optionally, in an embodiment of the present application, the step of reducing the uncertain scenarios by using the scenario reduction method to determine representative output scenarios includes: iteratively reducing the photovoltaic output scenarios by using the photovoltaic output scenario probability and the scenario distance until the photovoltaic output scenarios reach a preset iteration stop condition, and determining the representative output scenarios.

[0008] Optionally, in an embodiment of the present application, the formula of the objective function of the scheduling model for advanced adiabatic compressed air energy storage is:

[0009]

[0010] where are respectively the electricity purchase cost from the main grid, the cost of wind and light abandonment, and the operation cost of compressed air energy storage in scenario s, and Ω s is the set of CAES compressed air energy storage connected to the main grid.

[0011] Optionally, in an embodiment of the present application, the formula of the electricity purchase cost from the main grid is:

[0012]

[0013] where is the electricity quantity purchased from the main grid, and ρ L is the electricity price for purchasing electricity from the main grid, and Ω T , Ω U are respectively the set of time and the set of nodes connected to the main grid;

[0014] The formula of the cost of wind and light abandonment is:

[0015]

[0016] where and are the wind curtailment volume and the photovoltaic curtailment volume at time t in the scenario s, respectively, and ρ re is the penalty coefficient for wind and photovoltaic curtailment;

[0017] The formula for the operating cost of the compressed air energy storage is:

[0018]

[0019] where s is the scenario, and are the start-up costs of the compression side and the expansion side of the compressed air energy storage, respectively, and Ω C is the set of compressed air energy storage connected to the main power grid, and Ω T is the set of times connected to the main power grid.

[0020] Optionally, in an embodiment of the present application, the establishment of relevant constraint conditions for the distribution network AC power flow constraint and the advanced adiabatic compressed air energy storage scheduling model constraint based on the second-order cone model includes: establishing a distribution network AC power flow constraint based on the second-order cone model including at least one of the active power and reactive power balance constraint, the apparent power constraint, and the upper and lower limits of the photovoltaic power; establishing an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of the upper and lower limits of the gas storage pressure, the compression state, the power generation state, and the heat storage state relationship constraint; and forming the relevant constraint conditions with the distribution network AC power flow constraint based on the second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint.

[0021] Optionally, in an embodiment of the present application, the formula for the active power and reactive power balance constraint is:

[0022]

[0023]

[0024]

[0025]

[0026] where and are the lower and upper limits of the active power of the generator, respectively, and are the lower and upper limits of the reactive power of the generator, respectively, and are the lower and upper limits of the square of the voltage amplitude of node i, respectively, and are the upper limits of the wind power and photovoltaic power at time t in scenario s, respectively, and are the actual grid-connected wind power and photovoltaic power at time t in the scenario s, respectively, and are the fluctuations in wind power and photovoltaic power at time t in the scenario s, respectively, is the reactive power of the generator at time t in the scenario s, is the remaining reactive load, is the reactive power load of node j at time t, is the reactive power of the AA-CAES system at time t, U i,t,s is the square of the voltage amplitude of node i;

[0027] The formula for the apparent power constraint is:

[0028]

[0029]

[0030]

[0031] where, and are the rated active power and rated reactive power of line ij in the scenario s, respectively, and are the active power and reactive power of line ij at time t in the scenario s, respectively, is the square of the current amplitude on line ij, is the resistance of line ij at time t, is the reactance of line ij at time t.

[0032] Optionally, in an embodiment of the present application, the formula for the upper and lower limits of the gas storage pressure constraint is:

[0033]

[0034] where, p st,t is the air pressure in the gas storage chamber at time t, Δp st,0 is the initial air pressure change rate of the gas storage chamber, P st,t is the air pressure in the tank of the gas storage chamber at time t, P stmin and P stmax are the upper and lower limits of the air pressure in the gas storage chamber, respectively, Δt is a scheduling period, Δp st,t is the air pressure change rate in the gas storage chamber at time t;

[0035] The formula for the relationship constraint between the compression state, power generation state and heat storage state is:

[0036] u CAESc,t+u CAESg,t ≤1,

[0037] where u CAESc,t and u CAESg,t are binary variables for the compression condition and the power generation condition of the compressed air energy storage system, respectively.

[0038] In a second aspect embodiment of the present application, a distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage is provided, including: a determination module, configured to sample and process photovoltaic predicted output data by using the Latin hypercube sampling method to generate uncertain output scenarios, and reduce the uncertain scenarios by using the scenario reduction method to determine representative output scenarios; a first establishment module, configured to establish an objective function of a scheduling model for advanced adiabatic compressed air energy storage including at least one of the purchase cost of electricity from the main grid, the cost of curtailed wind and light, and the operation cost of advanced adiabatic compressed air energy storage based on the representative output scenarios; a second establishment module, configured to establish relevant constraint conditions based on the second-order cone model for the distribution network AC power flow constraint and the advanced adiabatic compressed air energy storage scheduling model constraint; a scheduling module, configured to establish a mixed-integer linear programming model based on the scheduling model objective function and the relevant constraint conditions, solve and optimize the mixed-integer linear programming model to obtain a distribution network stochastic scheduling result considering advanced adiabatic compressed air energy storage.

[0039] Optionally, in an embodiment of the present application, the determination module includes: a determination unit, configured to iteratively reduce the photovoltaic output scenarios by using the photovoltaic output scenario probability and the scenario distance until the photovoltaic output scenarios reach a preset iteration stop condition to determine the representative output scenarios.

[0040] Optionally, in an embodiment of the present application, the formula for the objective function of the scheduling model for advanced adiabatic compressed air energy storage is:

[0041]

[0042] where are respectively the purchase cost of electricity from the main grid, the cost of curtailed wind and light, and the operation cost of compressed air energy storage for scenario s, and Ω S is the set of CAES compressed air energy storage connected to the main grid.

[0043] Optionally, in an embodiment of the present application, the formula for the purchase cost of electricity from the main grid is:

[0044]

[0045] where is the electricity quantity purchased from the main grid, and ρ L is the electricity price for purchasing electricity from the main grid, and Ω T 、Ω UThe time set and node set connected to the main power grid respectively;

[0046] The formula for the curtailment cost of wind and light is:

[0047]

[0048] Wherein, and are the wind curtailment volume and light curtailment volume at time t in the scenario s respectively, and ρ re is the penalty coefficient for wind and light curtailment;

[0049] The formula for the operating cost of compressed air energy storage is:

[0050]

[0051] Where s is the scenario, and are the start-up costs of the compression side and expansion side of the compressed air energy storage respectively, Ω C is the set of compressed air energy storage connected to the main power grid, and Ω T is the time set connected to the main power grid.

[0052] Optionally, in an embodiment of the present application, the second establishment module includes: a first establishment unit for establishing a distribution network AC power flow constraint based on a second-order cone model including at least one of active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power; a second establishment unit for establishing an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of upper and lower limits of gas storage pressure constraints, compression state, power generation state, and heat storage state relationship constraints; a composition unit for composing the relevant constraint conditions with the distribution network AC power flow constraint based on the second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint.

[0053] Optionally, in an embodiment of the present application, the formula for the active power and reactive power balance constraint is:

[0054]

[0055]

[0056]

[0057]

[0058] Wherein, and are the lower limit and upper limit of the active power of the generator respectively, and are the lower limit and upper limit of the reactive power of the generator respectively, and are the lower and upper limits of the square of the voltage amplitude of node i, respectively, and are the upper limits of the wind power and photovoltaic power at time t under scenario s, respectively, and are the actual grid-connected wind power and photovoltaic power at time t under the said scenario s, respectively, and are the fluctuations of the wind power and photovoltaic power at time t under the said scenario s, respectively, is the reactive power of the generator at time t under scenario s, is the amount of the remaining reactive load, is the reactive power load of node j at time t, is the reactive power of the AA-CAES system at time t, U i,t,s is the square of the voltage amplitude of node i;

[0059] The formula for the apparent power constraint is:

[0060]

[0061]

[0062]

[0063] where, and are the rated active power and rated reactive power of line ij under scenario s, respectively, and are the active power and reactive power of line ij at time t under scenario s, respectively, is the square of the current amplitude on line ij, is the resistance of line ij at time t, is the reactance of line ij at time t.

[0064] Optionally, in an embodiment of the present application, the formula for the upper and lower limits of the gas storage pressure constraint is:

[0065]

[0066] where, p st,t is the air pressure in the gas storage chamber at time t, Δp st,0 is the initial air pressure change rate of the gas storage chamber, P st,t is the air pressure in the tank of the gas storage chamber at time t, P stmin and P stmax are the upper and lower limits of the air pressure in the gas storage chamber, respectively, and Δt is a scheduling period, Δpst,t is the air pressure change rate in the gas storage chamber at time t;

[0067] The formula for the relationship constraints of the compression state, power generation state, and heat storage state is:

[0068] u CAESc,t +u CAESg,t ≤1,

[0069] where u CAESc,t and u CAESg,t are binary variables for the compression condition and power generation condition of the compressed air energy storage system, respectively.

[0070] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement a method for stochastic scheduling of a distribution network considering advanced adiabatic compressed air energy storage as described in the above embodiment.

[0071] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements a method for stochastic scheduling of a distribution network considering advanced adiabatic compressed air energy storage as described above.

[0072] Embodiments of the present application can use Latin hypercube sampling and scenario reduction method to describe the uncertain output of photovoltaic power. Secondly, the objective function of the scheduling model is established, and the AC power flow constraints of the distribution network based on the second-order cone model and the scheduling model constraints of the advanced adiabatic compressed air energy storage are established. Finally, CPLEX is used to solve the optimal scheduling model to obtain the optimal scheduling strategy, which meets the actual operation requirements of the distribution network. Thus, the problem of economic scheduling for a distribution network with an advanced adiabatic compressed air energy storage system in the related technology is solved. In the research, the impact of the uncertain output of renewable energy on the scheduling operation of a distribution network with an advanced adiabatic compressed air energy storage is not considered. The inherent volatility and intermittency of new energy such as wind energy and solar energy may cause prominent problems such as reverse power flow, overcurrent, and node voltage violation in the distribution network, bringing technical challenges to the safe scheduling operation of the distribution network and resulting in problems such as the proposed scheduling strategy being unable to meet the actual operation requirements of the distribution network.

[0073] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0075] Figure 1Flow chart of a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage according to an embodiment of the present application;

[0076] Figure 2 Flow chart of a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage according to an embodiment of the present application;

[0077] Figure 3 Structural schematic diagram of a distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage according to an embodiment of the present application;

[0078] Figure 4 Structural schematic diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0079] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0080] A distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage according to an embodiment of the present application is described below with reference to the accompanying drawings. In view of the problem that the relevant technology in the above-mentioned background technology does not consider the impact of the uncertain output of renewable energy on the scheduling operation of the distribution network with advanced adiabatic compressed air energy storage in the research on the economic scheduling problem of the system with advanced adiabatic compressed air energy storage, which may lead to the problem that the proposed scheduling strategy cannot meet the actual operation requirements of the distribution network, the present application provides a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage. In this method, the Latin hypercube sampling and scenario reduction method can be used to describe the photovoltaic uncertain output situation. Secondly, the objective function of the scheduling model is established, and the AC power flow constraint of the distribution network and the scheduling model constraint of the advanced adiabatic compressed air energy storage based on the second-order cone model are established. Finally, CPLEX is used to solve the optimal scheduling model to obtain the optimal scheduling strategy, which meets the actual operation requirements of the distribution network. Thus, the problems that the relevant technology does not consider the impact of the uncertain output of renewable energy on the scheduling operation of the distribution network with advanced adiabatic compressed air energy storage in the research on the economic scheduling problem of the system with advanced adiabatic compressed air energy storage, which may lead to the problem that the proposed scheduling strategy cannot meet the actual operation requirements of the distribution network, etc. are solved.

[0081] Specifically, Figure 1 Schematic flow diagram of a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage provided by an embodiment of the present application.

[0082] As Figure 1As shown in the figure, a stochastic scheduling method for a distribution network considering advanced adiabatic compressed air energy storage includes the following steps:

[0083] In step S101, the Latin hypercube sampling method is used to sample the predicted photovoltaic output data to generate uncertain output scenarios, and the scenario reduction method is used to reduce the uncertain scenarios to determine the representative output scenarios.

[0084] In the actual execution process, the embodiment of the present application can process the uncertain photovoltaic output. The Latin hypercube sampling method is used to sample the predicted photovoltaic output data to generate N uncertain output scenarios, and the scenario reduction method is used to reduce the uncertain scenarios to further improve the calculation speed and accuracy and determine the representative output scenarios. The process is as follows:

[0085] Initialize, and the probability of any photovoltaic output scenario (sample) is equal, that is:

[0086]

[0087] where i is the node and N is the uncertain output scenario.

[0088] Set n * = N, n * is the number of photovoltaic output scenarios in the calculation process. Among the n * photovoltaic output scenarios, for any two scenarios X i , X j (1 ≤ i ≤ j ≤ n * ), calculate their scenario distance according to the following formula:

[0089] d k (X i , X j ) = |X i - X j |,

[0090] where d k () represents the scenario distance.

[0091] For any scenario X i , find the scenario X j with the closest scenario distance to it, that is, min{d k (X i , X j ) i ≠ j}, and calculate the product according to the following formula:

[0092] P KDi = min{d k (X i , X j ) i ≠ j} × p i ,

[0093] Among n * photovoltaic output scenarios, find the minimum P KD , denoted as P KDs :

[0094] P KDs = min{P KDi | 1 ≤ i ≤ n *}},

[0095] Secondly, update the probabilities of photovoltaic output scenarios:

[0096] p j = p j + p i ,

[0097] where Pi is the probability of the photovoltaic output scenario at initialization, and Pj is the probability of the updated photovoltaic output scenario.

[0098] Furthermore, cut X i from the scenario set.

[0099] Update the number of photovoltaic output scenarios after cutting:

[0100] n * = n * - n i ,

[0101] where n * is the number of photovoltaic output scenarios in the calculation process, and n i is the number of the minimum P KDs , which may be more than one.

[0102] Judge whether the target number of scenarios n * = K is reached. If so, end the reduction of photovoltaic output scenarios; otherwise, transfer to the calculation step of scenario distance for new reduction.

[0103] The embodiments of this application can use Latin hypercube sampling and scenario reduction method to describe the uncertain output of photovoltaic, improving the feasibility of the scheduling strategy.

[0104] Optionally, in an embodiment of this application, the scenario reduction method is used to reduce the uncertain scenarios to determine the representative output scenarios, including: iteratively reducing the photovoltaic output scenarios by using the probabilities of photovoltaic output scenarios and scenario distances until the photovoltaic output scenarios reach the preset iteration stop condition, and determining the representative output scenarios.

[0105] It can be understood that the preset iteration stop condition in the embodiments of this application can be the condition when the generated photovoltaic output scenarios are reduced to the preset value K.

[0106] In the actual implementation process, the embodiments of the present application can iteratively reduce the photovoltaic output scenarios by using the probabilities and distances of the photovoltaic output scenarios until the photovoltaic output scenarios are reduced to a preset value K for the generated photovoltaic output scenarios. When it is determined that a certain iteration stop condition is reached, a representative output scenario is determined, further improving the feasibility of the scheduling strategy.

[0107] It should be noted that the preset iteration stop condition can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0108] In step S102, based on the representative output scenario, a scheduling model objective function for the advanced adiabatic compressed air energy storage is established, including at least one of the electricity purchase cost, the curtailment cost of wind and light, and the operation cost of the advanced adiabatic compressed air energy storage.

[0109] As a possible implementation manner, the embodiments of the present application can establish a scheduling model objective function for the advanced adiabatic compressed air energy storage, including the electricity purchase cost, the curtailment cost of wind and light, and the operation cost of the advanced adiabatic compressed air energy storage. Among them, the randomness of the photovoltaic output mainly considers its output prediction error, and the error follows a normal distribution with a variance of 5% of the photovoltaic output.

[0110] Optionally, in an embodiment of the present application, the formula of the scheduling model objective function for the advanced adiabatic compressed air energy storage is:

[0111]

[0112] Among them, are respectively the electricity purchase cost from the main power grid, the curtailment cost of wind and light, and the operation cost of the compressed air energy storage for scenario s, and Ω S is the set of CAES compressed air energy storage connected to the main power grid.

[0113] Specifically, the embodiments of the present application can use the formula:

[0114]

[0115] to obtain the scheduling model objective function for the advanced adiabatic compressed air energy storage, improving the calculation accuracy, ensuring reasonable optimal scheduling in the subsequent process, and further meeting the actual operation requirements of the distribution network.

[0116] Optionally, in an embodiment of the present application, the formula of the electricity purchase cost from the main power grid is:

[0117]

[0118] Among them, is the electricity quantity purchased from the main power grid, and ρ L is the electricity price for purchasing electricity from the main power grid, and Ω T 、ΩU The sets of time and nodes connected to the main power grid respectively;

[0119] The formula for the cost of curtailment of wind and solar power is:

[0120]

[0121] Among them, and are the amounts of curtailed wind and curtailed solar power at time t under scenario s respectively, and ρ re is the penalty coefficient for curtailment of wind and solar power;

[0122] The formula for the operating cost of compressed air energy storage is:

[0123]

[0124] Among them, s is the scenario, and are the start-up costs of the compression side and the expansion side of the compressed air energy storage respectively, Ω C is the set of compressed air energy storage connected to the main power grid, and Ω T is the set of time connected to the main power grid.

[0125] Specifically, the embodiments of the present application can use the formula:

[0126]

[0127]

[0128]

[0129] The purchase cost of electricity from the main power grid, the cost of curtailment of wind and solar power, and the operating cost of compressed air energy storage are obtained respectively, further improving the accuracy of the calculation, further ensuring reasonable optimal scheduling in the subsequent process, and thus meeting the actual operation requirements of the distribution network.

[0130] In step S103, relevant constraint conditions based on the second-order cone model for the AC power flow constraint of the distribution network and the scheduling model constraint of the advanced adiabatic compressed air energy storage are established.

[0131] In the actual execution process, the embodiments of the present application can establish relevant constraint conditions based on the second-order cone model for the AC power flow constraint of the distribution network and the scheduling model constraint of the advanced adiabatic compressed air energy storage, so as to provide support for obtaining the stochastic scheduling result of the distribution network considering the advanced adiabatic compressed air energy storage subsequently.

[0132] Optionally, in an embodiment of the present application, relevant constraint conditions for establishing a distribution network AC power flow constraint based on a second-order cone model and an advanced adiabatic compressed air energy storage scheduling model constraint are established, including: establishing a distribution network AC power flow constraint based on a second-order cone model including at least one of active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power constraints; establishing an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of upper and lower limits of gas storage pressure constraints, compression state, power generation state, and heat storage state relationship constraints; and forming relevant constraint conditions with the distribution network AC power flow constraint based on a second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint.

[0133] As a possible implementation manner, the embodiment of the present application can establish relevant constraint conditions for a distribution network scheduling model including advanced adiabatic compressed air energy storage, mainly including a distribution network AC power flow constraint based on a second-order cone model and an advanced adiabatic compressed air energy storage scheduling model constraint. Among them, the distribution network AC power flow constraint based on a second-order cone model mainly includes node active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power constraints, etc. The advanced adiabatic compressed air energy storage scheduling model constraint mainly includes upper and lower limits of gas storage pressure constraints, compression state and power generation state relationship constraints, etc.

[0134] The embodiment of the present application can establish a distribution network AC power flow constraint based on a second-order cone model and an advanced adiabatic compressed air energy storage scheduling model constraint, further providing support for obtaining the random scheduling result of the distribution network considering advanced adiabatic compressed air energy storage subsequently.

[0135] Optionally, in an embodiment of the present application, the formula for the active power and reactive power balance constraint is:

[0136]

[0137]

[0138]

[0139]

[0140] Among them, and are respectively the lower and upper limits of the active power of the generator, and are respectively the lower and upper limits of the reactive power of the generator, and are respectively the lower and upper limits of the square of the voltage amplitude of node i, and are respectively the upper limits of the wind power and photovoltaic power at time t under scenario s, and They are the actual grid-connected wind power and photovoltaic power at time t in scenario s respectively. and They are the fluctuations of wind power and photovoltaic power at time t in scenario s respectively. It is the reactive power of the generator at time t in scenario s. It is the remaining reactive load. It is the reactive power load of node j at time t. It is the reactive power of the AA-CAES system at time t, U i,t,s It is the square of the voltage amplitude of node i.

[0141] The formula for the apparent power constraint is:

[0142]

[0143]

[0144]

[0145] Among them, and They are the rated active power and rated reactive power of line ij in scenario s respectively. and They are the active power and reactive power of line ij at time t in scenario s respectively. It is the square of the current amplitude on line ij. It is the resistance of line ij at time t. It is the reactance of line ij at time t.

[0146] Specifically, the AC power flow constraints of the distribution network based on the second-order cone model are as follows. Among them, the line power constraint is:

[0147]

[0148]

[0149] Among them, and They are the active power and reactive power of line ij at time t in scenario s respectively. and They are the actual grid-connected wind power and photovoltaic power at time t in scenario s respectively. It is the reactive power of the generator of node j at time t. They are the resistance and reactance of line ij respectively. It is the square of the current amplitude on line ij. and They are the active power and reactive power of the lines connected to node j respectively. and The active power load and reactive power load of node j at time t, respectively; is the set of grid load node numbers.

[0150] The forward voltage drop equation constraint is:

[0151]

[0152] where U i,t,s is the square of the voltage amplitude of node i, and are the resistance, reactance and impedance of line ij, respectively, and are the active power and reactive power of line ij at time t under scenario s, respectively, is the square of the current amplitude on line ij.

[0153] The apparent power constraint at the starting point of line ij is:

[0154]

[0155] where is the square of the current amplitude on line ij, U i,t,s is the square of the voltage amplitude of node i, and are the active power and reactive power of line ij at time t under scenario s, respectively.

[0156] The power constraint of node j is:

[0157]

[0158] where is the remaining load, and are the charging and discharging powers of AA-CAES.

[0159] The formulas for the active power and reactive power balance constraints are:

[0160]

[0161]

[0162]

[0163]

[0164] The apparent power constraint of the line and the constraints of several protection requirements are:

[0165]

[0166]

[0167]

[0168] In the embodiments of the present application, the active power and reactive power balance constraints and apparent power constraints of the nodes can be obtained according to the formula, further improving the accuracy of the calculation and providing support for obtaining the optimal scheduling result subsequently.

[0169] Optionally, in an embodiment of the present application, the formulas for the upper and lower limits of the gas storage pressure constraint are:

[0170]

[0171] where p st,t is the air pressure in the gas storage chamber at time t, Δp st,0 is the initial air pressure change rate of the gas storage chamber, P st,t is the air pressure in the tank of the gas storage chamber at time t of the gas storage chamber, P stmin and P stmax are the upper and lower limits of the air pressure in the gas storage chamber respectively, Δt is a scheduling period, and Δp st,t is the air pressure change rate in the gas storage chamber at time t;

[0172] The formulas for the relationship constraints of the compression state, power generation state and heat storage state are:

[0173] u CAESc,t +u CAESg,t ≤1,

[0174] where u CAESc,t and u CAESg,t are binary variables of the compression working condition and power generation working condition of the compressed air energy storage system respectively.

[0175] Specifically, the advanced adiabatic compressed air energy storage scheduling model is constrained as follows. The formulas for the upper and lower limits of the gas storage pressure constraint are:

[0176]

[0177] where p st,t is the air pressure in the gas storage chamber at time t; Δp st,0 is the initial air pressure change rate of the gas storage chamber; p st,t is the air pressure in the tank of the gas storage chamber at time t of the gas storage chamber; p stmin , p stmax are the upper and lower limits of the air pressure in the gas storage chamber; Δt is a scheduling period; Δp st，t is the air pressure change rate in the gas storage chamber at time t.

[0178] The compressor power range and the turbine generator set range are constrained as follows:

[0179]

[0180] Among them, P CAEScmin and P CAEScmax are the upper and lower limit values of the AA-CAES compression power respectively; P CAESgmax and P CAESgmin are the upper and lower limit values of the AA-CAES power generation power respectively; P CAESc,t , P CAESg,t are the outputs of the compressor and the expander at time t; u CAESc,t , u CAESg,t are the binary variables of the compression condition and the power generation condition of the compressed air energy storage system respectively.

[0181] The constraint formula for the relationship between the compression state, the power generation state and the heat storage state is:

[0182] u CAESc,t +u CAESg，t ≤1,

[0183] Among them, the heat storage constraint is as follows:

[0184]

[0185] Among them, H c , H g are the power coefficients of the heat change rates of the compressor and the expander respectively; P Qc，t , P Qg，t are the heat generation power of the compressor and the heat release power of the expander at time t respectively; Q TES.0 , Q TES,t are the initial heat storage amount of the heat storage device and the heat storage amount at time t respectively; Q TES,max , Q TES,min are the upper and lower limit values of the heat storage amount of the heat storage device; P Qout，τ is the heat supply value of the compressed air energy storage system to the outside.

[0186] The embodiment of the present application can obtain the upper and lower limit constraints of the gas storage pressure and the relationship constraints between the compression state and the power generation state according to the formula, further improving the calculation accuracy and providing support for obtaining the optimal scheduling result subsequently.

[0187] In step S104, based on the objective function of the scheduling model and the relevant constraint conditions, a mixed integer linear programming model is established, solved and optimized to obtain the stochastic scheduling result of the distribution network considering advanced adiabatic compressed air energy storage.

[0188] In the actual implementation process, the embodiments of the present application can linearize the non-linear constraints in the above model, and then combine the above objective function and related constraint conditions to establish a stochastic scheduling mixed-integer linear programming model for an advanced adiabatic compressed air energy storage distribution network. Finally, CPLEX is called for solution to obtain the scheduling result.

[0189] In the above scheduling model:

[0190]

[0191] Among them, is the square of the current amplitude on line ij, and U i,t,s is the square of the voltage amplitude of node i. and are the active power and reactive power of line ij at time t under scenario s respectively. The above formula is a non-convex constraint and needs to be relaxed. Based on the second-order cone model, the inequality:

[0192]

[0193] is used for replacement to transform the apparent power constraint at the starting point of line ij into the second-order cone form. The specific expression is shown as follows:

[0194]

[0195] Among them, and are the active power and reactive power of line ij at time t under scenario s respectively; is the square of the current amplitude on line ij; U i,t,s is the square of the voltage amplitude of node i.

[0196] The embodiments of the present application can combine the above objective function and related constraint conditions to establish a stochastic scheduling model for an advanced adiabatic compressed air energy storage distribution network and then call CPLEX for solution to obtain the scheduling result, ensuring that the scheduling strategy meets the actual operation requirements of the distribution network.

[0197] Specifically, it can be combined with Figure 2 As shown, a specific embodiment is used to elaborate in detail the working principle of a stochastic scheduling method for a distribution network considering advanced adiabatic compressed air energy storage in the embodiments of the present application.

[0198] As Figure 2 shown, the embodiments of the present application may include the following steps:

[0199] Step S201: Establish the objective function of the distribution network scheduling model with advanced adiabatic compressed air energy storage.

[0200] Step S202: Establish the AC power flow constraints of the distribution network and the scheduling model constraints of the advanced adiabatic compressed air energy storage based on the second-order cone model.

[0201] Step S203: Establish and solve the mixed-integer linear programming problem to obtain the optimal scheduling result.

[0202] According to the distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage proposed in the embodiments of the present application, the Latin hypercube sampling and scenario reduction method can be used to describe the uncertain output situation of photovoltaic power. Secondly, the objective function of the scheduling model is established, and the AC power flow constraints of the distribution network and the scheduling model constraints of the advanced adiabatic compressed air energy storage based on the second-order cone model are established. Finally, CPLEX is used to solve the optimal scheduling model to obtain the optimal scheduling strategy, which meets the actual operation requirements of the distribution network. Thus, the problem of the economic scheduling of the distribution network containing the advanced adiabatic compressed air energy storage system in the related technology is solved. In the research, the impact of the uncertain output of renewable energy on the scheduling operation of the distribution network containing the advanced adiabatic compressed air energy storage is not considered. The inherent volatility and intermittency of new energy such as wind energy and solar energy may cause prominent problems such as reverse power flow, overcurrent, and node voltage violation in the distribution network, bringing technical challenges to the safe scheduling operation of the distribution network and resulting in the problem that the proposed scheduling strategy cannot meet the actual operation requirements of the distribution network.

[0203] Secondly, a distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage proposed in the embodiments of the present application is described with reference to the accompanying drawings.

[0204] Figure 3 It is a schematic structural diagram of a distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage according to an embodiment of the present application.

[0205] As Figure 3 shown, the distribution network stochastic scheduling device 10 considering advanced adiabatic compressed air energy storage includes: a determination module 100, a first establishment module 200, a second establishment module 300, and a scheduling module 400.

[0206] Specifically, the determination module 100 is configured to sample the photovoltaic predicted output data by using the Latin hypercube sampling method to generate uncertain output scenarios, and reduce the uncertain scenarios by using the scenario reduction method to determine the representative output scenarios.

[0207] The first establishment module 200 is configured to establish an objective function of the scheduling model of the advanced adiabatic compressed air energy storage including at least one of the power purchase cost, the wind and light abandonment cost, and the operation cost of the advanced adiabatic compressed air energy storage based on the representative output scenarios.

[0208] The second establishment module 300 is used to establish relevant constraint conditions based on the AC power flow constraints of the distribution network and the scheduling model constraints of the advanced adiabatic compressed air energy storage.

[0209] The scheduling module 400 is used to establish a mixed-integer linear programming model based on the scheduling model objective function and relevant constraint conditions, solve and optimize the mixed-integer linear programming model, and obtain the stochastic scheduling result of the distribution network considering the advanced adiabatic compressed air energy storage.

[0210] Optionally, in an embodiment of the present application, the determination module 100 includes: a determination unit.

[0211] Among them, the determination unit is used to iteratively reduce the photovoltaic output scenarios by using the photovoltaic output scenario probability and the scenario distance until the photovoltaic output scenarios reach the preset iteration stop condition, and determine the representative output scenarios.

[0212] Optionally, in an embodiment of the present application, the formula of the scheduling model objective function of the advanced adiabatic compressed air energy storage is:

[0213]

[0214] Among them, are respectively the power purchase cost from the main grid, the curtailment cost of wind and light, and the operation cost of the compressed air energy storage for scenario s, and Ω S is the set of CAES compressed air energy storage connected to the main grid.

[0215] Optionally, in an embodiment of the present application, the formula of the power purchase cost from the main grid is:

[0216]

[0217] Among them, is the electricity quantity purchased from the main grid, and ρ L is the electricity price for purchasing electricity from the main grid, and Ω T , Ω U are respectively the time set and the node set connected to the main grid;

[0218] The formula of the curtailment cost of wind and light is:

[0219]

[0220] Among them, and are respectively the wind curtailment quantity and the light curtailment quantity at time t under scenario s, and ρ re is the curtailment penalty coefficient of wind and light;

[0221] The formula of the operation cost of the compressed air energy storage is:

[0222]

[0223] where s is the scenario, and are the start-up costs of the compression side and the expansion side of the compressed air energy storage respectively, Ω C is the set of compressed air energy storage connected to the main power grid, Ω T is the set of times connected to the main power grid.

[0224] Optionally, in an embodiment of the present application, the second establishment module 300 includes: a first establishment unit, a second establishment unit, and a composition unit.

[0225] Among them, the first establishment unit is used to establish a distribution network AC power flow constraint based on a second-order cone model including at least one of active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power.

[0226] The second establishment unit is used to establish an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of upper and lower limits of gas storage pressure, compression state, power generation state, and heat storage state relationship constraints.

[0227] The composition unit is used to form relevant constraint conditions with the distribution network AC power flow constraint based on the second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint.

[0228] Optionally, in an embodiment of the present application, the formula for the active power and reactive power balance constraint is:

[0229]

[0230] where and are respectively the lower and upper limits of the active power of the generator, and are respectively the lower and upper limits of the reactive power of the generator, and are respectively the lower and upper limits of the square of the voltage amplitude at node i, and are respectively the upper limits of the wind power and photovoltaic power at time t under scenario s, and are respectively the actual grid-connected wind power and photovoltaic power at time t under scenario s, and are respectively the fluctuations of the wind power and photovoltaic power at time t under scenario s, is the reactive power of the generator at time t under scenario s, is the remaining reactive load, is the reactive power load of node j at time t, is the reactive power of the AA-CAES system at time t, Ui,t,s is the square of the voltage amplitude of node i;

[0231] The formula for the apparent power constraint is:

[0232]

[0233]

[0234]

[0235] where and are the rated active power and rated reactive power of line ij under scenario s, respectively, and are the active power and reactive power of line ij at time t under scenario s, respectively, is the square of the current amplitude on line ij, is the resistance of line ij at time t, is the reactance of line ij at time t.

[0236] Optionally, in an embodiment of the present application, the formula for the upper and lower limits of the gas storage pressure constraint is:

[0237]

[0238] where p st,t is the air pressure in the gas storage chamber at time t, Δp st,0 is the initial air pressure change rate of the gas storage chamber, P st,t is the air pressure in the tank of the gas storage chamber at time t, P stmin and P stmax are the upper and lower limits of the air pressure in the gas storage chamber, respectively, Δt is a scheduling period, and Δp st,t is the air pressure change rate in the gas storage chamber at time t;

[0239] The formula for the relationship constraint between the compression state, power generation state, and heat storage state is:

[0240] u CAESc,t +u CAESg,t ≤1,

[0241] where u CAESc,t and u CAESg,t are binary variables for the compression condition and power generation condition of the compressed air energy storage system, respectively.

[0242] It should be noted that the foregoing explanation of the embodiments of a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage also applies to a distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage in this embodiment, and will not be repeated here.

[0243] A distribution network stochastic scheduling device considering advanced adiabatic compressed air energy storage according to an embodiment of the present application can use Latin hypercube sampling and scenario reduction method to describe the uncertain output of photovoltaic power. Secondly, the objective function of the scheduling model is established, and the AC power flow constraint of the distribution network based on the second-order cone model and the scheduling model constraint of advanced adiabatic compressed air energy storage are established. Finally, CPLEX is used to solve the optimal scheduling model to obtain the optimal scheduling strategy, which meets the actual operation requirements of the distribution network. Thus, the problem of economic scheduling for the system containing advanced adiabatic compressed air energy storage in the related technology is solved. In the research carried out, the impact of the uncertain output of renewable energy on the scheduling operation of the distribution network containing advanced adiabatic compressed air energy storage is not considered. The inherent volatility and intermittency of new energy such as wind energy and solar energy may cause prominent problems such as reverse power flow, overcurrent, and node voltage violation in the distribution network, bringing technical challenges to the safe scheduling operation of the distribution network and resulting in the problem that the proposed scheduling strategy cannot meet the actual operation requirements of the distribution network.

[0244] Figure 4 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0245] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0246] When the processor 402 executes the program, it implements a distribution network stochastic scheduling method considering advanced adiabatic compressed air energy storage provided in the above embodiment.

[0247] Furthermore, the electronic device further includes:

[0248] A communication interface 403 for communication between the memory 401 and the processor 402.

[0249] The memory 401 is used to store a computer program executable on the processor 402.

[0250] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0251] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 it is only represented by a thick line in Figure 4 , but it does not mean that there is only one bus or one type of bus.

[0252] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a single chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0253] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0254] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a random scheduling method for a distribution network involving advanced adiabatic compressed air energy storage as described above.

[0255] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or N embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0256] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0257] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of this application includes additional implementations, where functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of this application pertain.

[0258] The logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered as a sequenced list of executable instructions for implementing a logical function, and may be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0259] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0260] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0261] In addition, in each embodiment of the present application, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0262] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A random dispatching method for distribution network taking into account advanced adiabatic compressed air energy storage, characterized in that: The following steps are involved: The Latin hypercube sampling method is used to sample and process the photovoltaic predicted output data to generate uncertain output scenarios, and the scenario reduction method is used to reduce the uncertain scenarios to determine representative output scenarios; Based on the representative output scenario, an objective function of a scheduling model of advanced adiabatic compressed air energy storage is established, which includes at least one of the electricity purchase cost, the wind and solar power abandonment cost, and the advanced adiabatic compressed air energy storage operation cost; Establish the related constraints of the AC power flow constraints of the distribution network based on the second-order cone model and the constraints of the advanced adiabatic compressed air energy storage scheduling model; Based on the dispatch model objective function and the related constraints, a mixed integer linear programming model is established, and the mixed integer linear programming model is solved and optimized to obtain a random dispatch result of the distribution network taking into account advanced adiabatic compressed air energy storage.

2. The method according to claim 1, characterized in that The method of reducing the uncertain scenarios by using the scenario reduction method to determine the representative output scenario includes: The photovoltaic output scene probability and the scene distance are used to iteratively reduce the photovoltaic output scene until the photovoltaic output scene reaches a preset iteration stop condition, and the representative output scene is determined.

3. The method according to claim 1, characterized in that The formula of the objective function of the scheduling model of the advanced adiabatic compressed air energy storage is: in, are the electricity purchase cost from the main grid, the wind and solar curtailment cost, and the compressed air energy storage operation cost of scenario s, respectively. S It is a CAES compressed air energy storage assembly connected to the main power grid.

4. The method according to claim 3, characterized in that The formula for the main grid electricity purchase cost is: in, is the amount of electricity purchased from the main power grid, ρ L is the price of electricity purchased from the main power grid Ω T ,Ω U They are the time set and node set connected to the main power grid respectively; The formula for the cost of curtailing wind and solar power is: in, and are the wind curtailment and solar curtailment at time t under the scenario s, respectively, re is the penalty coefficient for abandoning wind and solar power; The formula for the compressed air energy storage operating cost is: Where s is the scene, and are the startup costs of the compression side and expansion side of the compressed air energy storage, Ω C is the compressed air energy storage connected to the main grid, Ω T is the time set for connection to the main power grid.

5. The method according to claim 1, characterized in that The related constraints for establishing the AC power flow constraints of the distribution network based on the second-order cone model and the constraints of the advanced adiabatic compressed air energy storage scheduling model include: Establishing a distribution network AC power flow constraint based on a second-order cone model, including at least one of active power and reactive power balance constraint, apparent power constraint, and photovoltaic power upper and lower limit constraint; Establishing an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of an upper and lower limit constraint on gas storage pressure, a compression state, a power generation state, and a heat storage state relationship constraint; The relevant constraints are composed of the distribution network AC power flow constraints based on the second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraints.

6. The method according to claim 5, characterized in that The formula for the active power and reactive power balance constraint is: in, and are the lower and upper limits of the generator active power, respectively. and are the lower and upper limits of the reactive power of the generator, and U i are the lower and upper limits of the square of the voltage amplitude at node i, and are the upper limits of wind power and photovoltaic power at time t under scenario s, and are the actual grid-connected wind power and photovoltaic power at time t under the scenario s, and are the wind power and photovoltaic power fluctuations at time t under the scenario s, is the reactive power of the generator at time t in the scenario s, is the remaining reactive load, is the reactive power load of node j at time t, is the reactive power of the AA-CAES system at time t, U i,t,s is the square of the voltage amplitude of the node i; The formula for the apparent power constraint is: in, and are the rated active power and rated reactive power of line ij under the scenario s, respectively. and are respectively the active power and reactive power of line ij at time t under the scenario s, is the square of the current amplitude on line ij, is the resistance of the line ij at the time t, is the reactance of the line ij at the time t.

7. The method according to claim 6, characterized in that The formulas for the upper and lower limits of the gas storage pressure are: Among them, p st,t is the air pressure in the air storage chamber at time t, Δp st,0 is the rate of change of initial air pressure in the air storage chamber, P st,t is the air pressure in the tank at time t in the air storage chamber, P stmin and P stmax are the upper and lower limits of the air pressure in the air storage chamber, Δt is a scheduling duration, Δp st,t is the rate of change of air pressure in the air storage chamber at time t; The formula for the relationship constraint between the compression state, power generation state and heat storage state is: in CAESc,t +in CAESg,t ≤1, Among them, u CAESc,t and u CAESg,t They are binary variables for the compression condition and power generation condition of the compressed air energy storage system respectively.

8. A random dispatching device for distribution network taking into account advanced adiabatic compressed air energy storage, characterized in that: include: A determination module is used to sample and process the photovoltaic predicted output data using the Latin hypercube sampling method to generate uncertain output scenarios, and to reduce the uncertain scenarios using the scenario reduction method to determine representative output scenarios; A first establishing module is used to establish, based on the representative output scenario, an objective function of a scheduling model of advanced adiabatic compressed air energy storage including at least one of a power purchase cost, a wind and solar power abandonment cost, and an advanced adiabatic compressed air energy storage operation cost; The second establishment module is used to establish the related constraints of the AC power flow constraints of the distribution network based on the second-order cone model and the constraints of the advanced adiabatic compressed air energy storage scheduling model; The scheduling module is used to establish a mixed integer linear programming model based on the scheduling model objective function and the relevant constraints, solve and optimize the mixed integer linear programming model, and obtain a random scheduling result of the distribution network taking into account advanced adiabatic compressed air energy storage.

9. The device according to claim 8, characterized in that The determination module comprises: The determination unit is used to iteratively reduce the photovoltaic output scene by using the photovoltaic output scene probability and the scene distance until the photovoltaic output scene reaches a preset iteration stop condition, and determine the representative output scene.

10. The device according to claim 9, characterized in that The formula of the objective function of the scheduling model of the advanced adiabatic compressed air energy storage is: in, are the electricity purchase cost from the main grid, the wind and solar curtailment cost, and the compressed air energy storage operation cost of scenario s, respectively. S It is a CAES compressed air energy storage assembly connected to the main power grid.

11. The device according to claim 10, characterized in that The formula for the main grid electricity purchase cost is: in, is the amount of electricity purchased from the main power grid, ρ L is the price of electricity purchased from the main power grid Ω T ,Ω U They are the time set and node set connected to the main power grid respectively; The formula for the cost of curtailing wind and solar power is: in, and are the wind curtailment and solar curtailment at time t under the scenario s, respectively, re is the penalty coefficient for abandoning wind and solar power; The formula for the compressed air energy storage operating cost is: Where s is the scene, and are the startup costs of the compression side and expansion side of the compressed air energy storage, Ω C is the compressed air energy storage connected to the main grid, Ω T is the time set for connection to the main power grid.

12. The device according to claim 8, characterized in that The second establishing module comprises: A first establishing unit is used to establish a distribution network AC power flow constraint based on a second-order cone model, including at least one of an active power and reactive power balance constraint, an apparent power constraint, and a photovoltaic power upper and lower limit constraint; A second establishing unit is used to establish an advanced adiabatic compressed air energy storage scheduling model constraint including at least one of an upper limit constraint and a lower limit constraint of a gas storage pressure, a compression state, a power generation state, and a heat storage state relationship constraint; A composition unit is used to form the relevant constraint conditions with the distribution network AC power flow constraint based on the second-order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint.

13. The device according to claim 12, characterized in that The formula for the active power and reactive power balance constraint is: in, and are the lower and upper limits of the generator active power, respectively. and are the lower and upper limits of the reactive power of the generator, and U i are the lower and upper limits of the square of the voltage amplitude at node i, and are the upper limits of wind power and photovoltaic power at time t under scenario s, and are the actual grid-connected wind power and photovoltaic power at time t under the scenario s, and are the wind power and photovoltaic power fluctuations at time t under the scenario s, is the reactive power of the generator at time t in the scenario s, is the remaining reactive load, is the reactive power load of node j at time t, is the reactive power of the AA-CAES system at time t, U i,t,s is the square of the voltage amplitude of the node i; The formula for the apparent power constraint is: in, and are the rated active power and rated reactive power of line ij under the scenario s, respectively. and are respectively the active power and reactive power of line ij at time t under the scenario s, is the capacity of line ij, is the square of the current amplitude on line ij, is the resistance of the line ij at the time t, is the reactance of the line ij at the time t.

14. The device according to claim 13, characterized in that The formulas for the upper and lower limits of the gas storage pressure are: Among them, p st,t is the air pressure in the air storage chamber at time t, Δp st,0 is the rate of change of initial air pressure in the air storage chamber, P st,t is the air pressure in the tank at time t in the air storage chamber, P stmin and P stmax are the upper and lower limits of the air pressure in the air storage chamber, Δt is a scheduling duration, Δp st,t is the rate of change of air pressure in the air storage chamber at time t; The formula for the relationship constraint between the compression state, power generation state and heat storage state is: in CAESc,t +in CAESg,t ≤1, Among them, u CAESc,t and u CAESg,t They are binary variables for the compression condition and power generation condition of the compressed air energy storage system respectively.

15. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a random dispatching method for a distribution network taking into account advanced adiabatic compressed air energy storage as described in any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a random dispatching method for a distribution network taking into account advanced adiabatic compressed air energy storage as described in any one of claims 1 to 7.

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