A power distribution network stochastic dispatching method considering advanced adiabatic compressed air energy storage

Abstract

The application relates to a power distribution network random scheduling method considering advanced adiabatic compressed air energy storage, wherein the method comprises the following steps: sampling and processing photovoltaic predicted output data by using a Latin hypercube sampling method, generating uncertain output scenes, 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 power distribution network alternating current flow constraints and advanced adiabatic compressed air energy storage scheduling model constraints based on a second-order cone model; 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 the advanced adiabatic compressed air energy storage. Therefore, the problem that a related technology does not consider the influence of uncertain output of renewable energy on scheduling operation of a power distribution network containing advanced adiabatic compressed air energy storage, and the problem that a scheduling strategy cannot meet actual operation requirements of the power distribution network are solved.

Description

Technical Field

[0001] This application relates to the field of energy storage system technology, and in particular to a random dispatch method for distribution networks that takes into account advanced adiabatic compressed air energy storage. Background Technology

[0002] With the increasingly severe environmental crisis and resource shortages, the vigorous development and utilization of renewable energy sources such as wind power and solar power has become a strategic consensus among governments worldwide. As a large number of new energy power plants and energy storage devices, including solar and wind power, are integrated into the distribution network, how to rationally utilize flexible resources to achieve optimized dispatching of the distribution network and improve its operational economy and security has become a crucial issue that urgently needs to be addressed.

[0003] Among related technologies, large-scale energy storage is an important method for smoothing fluctuations in new energy output and solving the problem of large-scale utilization of wind and solar power. Compressed air energy storage and pumped hydro storage are currently relatively mature large-scale energy storage technologies. Compared with pumped hydro storage, compressed air energy storage has less demanding geographical requirements and can be more flexibly applied to various scenarios. Advanced adiabatic compressed air energy storage, based on traditional compressed air energy storage technology, recovers the heat of compression to replace fuel for supplemental combustion, further improving the system's operating efficiency and economy, achieving zero combustion and zero carbon emissions during operation. Therefore, advanced adiabatic compressed air energy storage is also considered one of the most promising large-scale energy storage technologies.

[0004] Compared to the transmission network, the distribution network, located at the end of the power system, is significantly less capable of coping with uncertain weather conditions. However, research on the economic dispatch problem of advanced adiabatic compressed air energy storage systems has not considered the impact of uncertain renewable energy output on the dispatch operation of distribution networks with advanced adiabatic compressed air energy storage. The inherent volatility and intermittency of new energy sources such as wind and solar power may cause prominent problems such as reverse power flow, overcurrent, and node voltage exceeding limits in the distribution network, posing technical challenges to the safe dispatch operation of the distribution network. This may result in the proposed dispatch strategies failing to meet the actual operational needs of the distribution network, and therefore requires urgent improvement. Summary of the Invention

[0005] This application provides a stochastic dispatching method for distribution networks that takes into account advanced adiabatic compressed air energy storage (AAC) to address the economic dispatching problem of related technologies that do not consider the impact of uncertain renewable energy output on the dispatching operation of distribution networks with AAC. The inherent volatility and intermittency of new energy sources such as wind and solar power may cause prominent problems such as reverse power flow, overcurrent, and node voltage exceeding limits in the distribution network, posing technical challenges to the safe dispatching operation of the distribution network and causing the proposed dispatching strategy to fail to meet the actual operating needs of the distribution network.

[0006] The first aspect of this application provides a method for stochastic dispatching of a distribution network that takes into account advanced adiabatic compressed air storage (AAS), comprising the following steps: sampling and processing photovoltaic (PV) predicted output data using the Latin hypercube sampling method to generate uncertain output scenarios, and reducing the uncertain scenarios using a scenario reduction method to determine representative output scenarios; establishing an objective function for a dispatching model of AAS based on the representative output scenarios, including at least one of the following: electricity purchase cost, wind and solar curtailment cost, and AAS operating cost; establishing relevant constraint conditions for AC power flow constraints of the distribution network and dispatching model constraints of AAS based on a second-order cone model; and establishing a mixed-integer linear programming model based on the dispatching model objective function and the relevant constraint conditions, solving and optimizing the mixed-integer linear programming model to obtain the stochastic dispatching result of the distribution network that takes AAS into account.

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

[0008] Optionally, in one embodiment of this application, the objective function of the scheduling model for advanced adiabatic compressed air energy storage is formulated as follows:

[0009] ,

[0010] in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

[0011] Optionally, in one embodiment of this application, the formula for the main grid electricity purchase cost is:

[0012] ,

[0013] in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid , These are the time sets and node sets connected to the main power grid, respectively.

[0014] The formula for the cost of wind and solar power curtailment is:

[0015] ,

[0016] in, and The scenarios are respectively s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment.

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

[0018] ,

[0019] in, s For the scenario ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

[0020] Optionally, in one embodiment of this application, the relevant constraint conditions for establishing 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 include: establishing distribution network AC power flow constraints based on the 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 advanced adiabatic compressed air energy storage scheduling model constraints, including at least one of upper and lower limits of gas storage pressure constraints, compression state, power generation state, and thermal storage state relationship constraints; and using 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 to form the relevant constraint conditions.

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

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026] in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodesi The lower and upper limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and The scenarios are respectively s Next moment t Actual grid-connected wind power and photovoltaic power and The scenarios are respectively s Next moment t Fluctuations in wind and solar power output For the scenario s Next moment t Generator reactive power For the remaining reactive load, for t Node of time j reactive power load, For the t Reactive power of the AA-CAES system at all times For the node i The square of the voltage amplitude;

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

[0028] ,

[0029] ,

[0030] ,

[0031] in, and The scenarios are respectively s Downline ij Rated active power and rated reactive power, and The scenarios are respectively s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on, For the line ij In the t Resistance at time, For the line ij In the t Reactance at any given moment.

[0032] Optionally, in one embodiment of this application, the formulas for the upper and lower limits of the gas storage pressure are as follows:

[0033] ,

[0034] in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For the time t The rate of change of air pressure in the gas storage chamber at any given time;

[0035] The formula constraining the relationship between the compression state, power generation state, and thermal storage state is as follows:

[0036] ,

[0037] in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

[0038] A second aspect of this application provides a distribution network stochastic dispatching device that considers advanced adiabatic compressed air storage (AAS), comprising: a determination module, used to sample and process photovoltaic predicted output data using the Latin hypercube sampling method to generate uncertain output scenarios, and to reduce the uncertain scenarios using a scenario reduction method to determine representative output scenarios; a first establishment module, used to establish a dispatching model objective function for AAS based on the representative output scenarios, including at least one of the following: electricity purchase cost, wind and solar curtailment cost, and AAS operating cost; a second establishment module, used to establish relevant constraint conditions for distribution network AC power flow constraints and AAS dispatching model constraints based on a second-order cone model; and a dispatching module, used to establish a mixed-integer linear programming model based on the dispatching model objective function and the relevant constraint conditions, solve and optimize the mixed-integer linear programming model, and obtain a distribution network stochastic dispatching result considering AAS.

[0039] Optionally, in one embodiment of this application, the determining module includes: a determining unit, used to iteratively reduce the photovoltaic output scenarios using the probability of photovoltaic output scenarios and the scenario distance, until the photovoltaic output scenarios reach a preset iteration stop condition, and determine the representative output scenario.

[0040] Optionally, in one embodiment of this application, the objective function of the scheduling model for advanced adiabatic compressed air energy storage is formulated as follows:

[0041] ,

[0042] in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

[0043] Optionally, in one embodiment of this application, the formula for the main grid electricity purchase cost is:

[0044] ,

[0045] in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid , These are the time sets and node sets connected to the main power grid, respectively.

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

[0047] ,

[0048] in, and The scenarios are respectively s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment.

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

[0050] ,

[0051] in, s For the scenario ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

[0052] Optionally, in one embodiment of this application, the second establishment module includes: a first establishment unit, used to establish distribution network AC power flow constraints 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; a second establishment unit, used to establish advanced adiabatic compressed air energy storage scheduling model constraints, including at least one of upper and lower limits of gas storage pressure constraints, compression state, power generation state, and thermal storage state relationship constraints; and a composition unit, used to compose the relevant constraint conditions with 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.

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

[0054] ,

[0055] ,

[0056] ,

[0057] ,

[0058] in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodes i The lower and upper limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and The scenarios are respectively s Next moment t Actual grid-connected wind power and photovoltaic power and The scenarios are respectively s Next moment t Fluctuations in wind and solar power output For the scenario s Next moment t Generator reactive power For the remaining reactive load, for t Node of time j reactive power load, For the tReactive power of the AA-CAES system at all times For the node i The square of the voltage amplitude;

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

[0060] ,

[0061] ,

[0062] ,

[0063] in, and The scenarios are respectively s Downline ij Rated active power and rated reactive power, and The scenarios are respectively s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on, For the line ij In the t Resistance at time, For the line ij In the t Reactance at any given moment.

[0064] Optionally, in one embodiment of this application, the formulas for the upper and lower limits of the gas storage pressure are as follows:

[0065] ,

[0066] in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For the time t The rate of change of air pressure in the gas storage chamber at any given time;

[0067] The formula constraining the relationship between the compression state, power generation state, and thermal storage state is as follows:

[0068] ,

[0069] in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

[0070] A third aspect of this application provides an electronic device, including: 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 dispatch method for a power distribution network that takes into account advanced adiabatic compressed air energy storage as described in the above embodiments.

[0071] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for random dispatching of a power distribution network incorporating advanced adiabatic compressed air energy storage.

[0072] This application's embodiments employ Latin hypercube sampling and scenario reduction methods to describe uncertain photovoltaic output. Secondly, it establishes a scheduling model objective function and sets constraints for AC power flow in the distribution network and advanced adiabatic compressed air storage (AAS) energy storage scheduling model based on a second-order cone model. Finally, it uses CPLEX to solve the optimized scheduling model, obtaining the optimal scheduling strategy that meets the actual operational needs of the distribution network. This addresses the problem of related technologies that, in their research on the economic scheduling of distribution networks containing AAS systems, failed to consider the impact of uncertain renewable energy output on the scheduling operation of distribution networks with AAS systems. The inherent volatility and intermittency of new energy sources such as wind and solar power can cause prominent problems such as reverse power flow, overcurrent, and node voltage exceeding limits in the distribution network, posing technical challenges to the safe scheduling operation of the distribution network and potentially leading to the proposed scheduling strategy failing to meet the actual operational needs of the distribution network.

[0073] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0075] Figure 1 This is a flowchart of a random dispatch method for a distribution network that takes into account advanced adiabatic compressed air energy storage, according to an embodiment of this application.

[0076] Figure 2 This is a flowchart of a random dispatch method for a distribution network that takes into account advanced adiabatic compressed air energy storage according to an embodiment of this application;

[0077] Figure 3 This is a schematic diagram of a random dispatching device for a distribution network that incorporates advanced thermally adiabatic compressed air energy storage, according to an embodiment of this application.

[0078] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0079] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein 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 intended to explain this application, and should not be construed as limiting this application.

[0080] The following description, with reference to the accompanying drawings, illustrates a stochastic dispatch method for distribution networks incorporating advanced adiabatic compressed air energy storage (AACES). Addressing the issue mentioned in the background art regarding the economic dispatch problem of distribution networks containing AACES, which fails to consider the impact of uncertain renewable energy output on the dispatch operation of distribution networks with AACES, leading to proposed dispatch strategies that cannot meet the actual operational needs of the distribution network, this application provides a stochastic dispatch method for distribution networks incorporating AACES. In this method, Latin hypercube sampling and scenario reduction methods can be used to describe the uncertain photovoltaic output. Next, a dispatch model objective function is established, along with AC power flow constraints and AACES dispatch model constraints based on a second-order cone model. Finally, CPLEX is used to solve the optimized dispatch model, obtaining the optimal dispatch strategy that meets the actual operational needs of the distribution network. This solves the problem in related art concerning the economic dispatch problem of distribution networks containing AACES, where the impact of uncertain renewable energy output on the dispatch operation of distribution networks with AACES was not considered, resulting in proposed dispatch strategies that cannot meet the actual operational needs of the distribution network.

[0081] Specifically, Figure 1 This is a flowchart illustrating a random dispatch method for a distribution network that incorporates advanced adiabatic compressed air energy storage, provided as an embodiment of this application.

[0082] like Figure 1 As shown, the random dispatch method for distribution networks that takes into account advanced adiabatic compressed air energy storage includes the following steps:

[0083] In step S101, the photovoltaic predicted power output data is sampled and processed using the Latin hypercube sampling method to generate uncertain power output scenarios. Then, the uncertain scenarios are reduced using the scenario reduction method to determine representative power output scenarios.

[0084] In practical implementation, the embodiments of this application can handle uncertain photovoltaic output by using the Latin hypercube sampling method to sample and process the predicted photovoltaic output data, generating... N We identified several uncertain output scenarios and used scenario reduction methods to shrink these scenarios, further improving the speed and accuracy of the calculations. The process for determining representative output scenarios is as follows:

[0085] Upon initialization, the probability of any photovoltaic power output scenario (sample) is equal, i.e.:

[0086] ,

[0087] in, i For nodes, N For scenarios with uncertain power output.

[0088] set up , To determine the number of photovoltaic power output scenarios in the calculation process, in In any two photovoltaic power output scenarios , The scene distance is calculated using the following formula:

[0089] ,

[0090] in, ( ) indicates scene distance.

[0091] For any scenario Find the scene closest to its scene. ,Right now And calculate the product using the following formula:

[0092] ,

[0093] exist In a photovoltaic power generation scenario, find the minimum... , recorded as :

[0094] ,

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

[0096] ,

[0097] in, Pi The probability of photovoltaic power output scenarios at initialization. Pj This represents the updated probability of photovoltaic power output scenarios.

[0098] Furthermore, Reduce by focusing on specific scenarios.

[0099] The updated number of photovoltaic power generation scenarios after reduction:

[0100] ,

[0101] in, The number of photovoltaic power output scenarios in the calculation process. Minimum The number of them may be more than one.

[0102] Determine if the target number of scenarios has been reached. =K, if so, then end the photovoltaic output scenario reduction; otherwise, proceed to the scenario distance calculation step for a new reduction.

[0103] The embodiments of this application can use Latin hypercube sampling and scenario reduction methods to describe the uncertain output of photovoltaics, thereby improving the feasibility of scheduling strategies.

[0104] Optionally, in one embodiment of this application, the scenario reduction method is used to reduce uncertain scenarios and determine representative output scenarios, including: using the probability of photovoltaic output scenarios and the scenario distance to iteratively reduce photovoltaic output scenarios until the photovoltaic output scenarios reach a preset iteration stop condition, thereby determining representative output scenarios.

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

[0106] In actual implementation, the embodiments of this application can use the probability of photovoltaic power output scenarios and the distance between scenarios to iteratively reduce photovoltaic power output scenarios until the photovoltaic power output scenarios are reduced to a preset value K. When a certain iteration stopping condition is reached, a representative power output scenario is determined, thereby further improving the feasibility of the scheduling strategy.

[0107] It should be noted that the preset iteration stopping conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0108] In step S102, based on representative power output scenarios, an objective function for the scheduling model of advanced adiabatic compressed air energy storage is established, which includes at least one of the following: electricity purchase cost, wind and solar curtailment cost, and advanced adiabatic compressed air energy storage operation cost.

[0109] As one possible approach, embodiments of this application can establish a scheduling model objective function that includes advanced adiabatic compressed air energy storage, comprising electricity purchase cost, wind and solar curtailment cost, and advanced adiabatic compressed air energy storage operation cost. The randomness of photovoltaic output mainly considers its output prediction error, which follows a normal distribution with a variance of 5% of photovoltaic output.

[0110] Optionally, in one embodiment of this application, the objective function of the scheduling model for advanced adiabatic compressed air energy storage is formulated as follows:

[0111] ,

[0112] in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

[0113] Specifically, embodiments of this application can utilize the following formula:

[0114] ,

[0115] The objective function of the scheduling model for advanced adiabatic compressed air energy storage is obtained, which improves the accuracy of the calculation and ensures reasonable optimization scheduling in the future, thereby meeting the actual operation needs of the distribution network.

[0116] Optionally, in one embodiment of this application, the formula for the main grid electricity purchase cost is:

[0117] ,

[0118] in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid , These are the time sets and node sets connected to the main power grid, respectively.

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

[0120] ,

[0121] in, and Scenes s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment.

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

[0123] ,

[0124] in, s For the scene ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

[0125] Specifically, embodiments of this application can utilize the following formula:

[0126] ,

[0127] ,

[0128] ,

[0129] The main grid power purchase cost, wind and solar curtailment cost, and compressed air energy storage operation cost were obtained respectively, which further improved the accuracy of the calculation and ensured that reasonable optimization and scheduling could be carried out in the future, thereby meeting the actual operation needs of the distribution network.

[0130] In step S103, relevant constraint conditions are established for 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 dispatch model.

[0131] In actual implementation, the embodiments of this application can establish relevant constraint conditions for distribution network AC power flow constraints and advanced adiabatic compressed air energy storage scheduling model constraints based on the second-order cone model, thereby providing support for obtaining subsequent distribution network stochastic scheduling results that take into account advanced adiabatic compressed air energy storage.

[0132] Optionally, in one embodiment of this application, relevant constraint conditions are established for 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. This includes: establishing distribution network AC power flow constraints based on the second-order cone model that include at least one of active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power constraints; establishing advanced adiabatic compressed air energy storage scheduling model constraints that include at least one of upper and lower limits of gas storage pressure constraints, compression state, power generation state, and thermal storage state relationship constraints; and using 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 to form relevant constraint conditions.

[0133] As one possible implementation method, embodiments of this application can establish relevant constraints for a distribution network dispatch model containing advanced adiabatic compressed air energy storage. These constraints mainly include AC power flow constraints of the distribution network based on a second-order cone model and constraints of the advanced adiabatic compressed air energy storage dispatch model. The AC power flow constraints of the distribution network based on the second-order cone model mainly include node active and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power constraints. The constraints of the advanced adiabatic compressed air energy storage dispatch model mainly include upper and lower limits of gas storage pressure constraints and constraints on the relationship between compression state and power generation state.

[0134] The embodiments of this application can establish AC power flow constraints and advanced adiabatic compressed air energy storage scheduling model constraints for distribution networks based on the second-order cone model, further providing support for obtaining subsequent stochastic scheduling results of distribution networks that take into account advanced adiabatic compressed air energy storage.

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

[0136] ,

[0137] ,

[0138] ,

[0139] ,

[0140] in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodes i The lower and upper limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and Scenes s Next moment t Actual grid-connected wind power and photovoltaic power, and Scenes s Next moment t Fluctuations in wind and solar power output For the scene s Next moment t Generator reactive power For the remaining reactive load, fort Node of time j reactive power load, for t Reactive power of the AA-CAES system at all times For nodes i The square of the voltage amplitude;

[0141] The formula for apparent power constraint is:

[0142] ,

[0143] ,

[0144] ,

[0145] in, and Scenes s Downline ij Rated active power and rated reactive power, and Scenes s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on, For the line ij exist t Resistance at time, For the line ij exist t Reactance at any given moment.

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

[0147] ,

[0148] ,

[0149] in, and Scenes s Down t Timetable ij Active power and reactive power; and Scenes s Next moment t Actual grid-connected wind power and photovoltaic power; for t Time Node j The reactive power of the generator; , The lines are respectively [[ID=J24]]ij Resistance and reactance; For the line ij The square of the current amplitude; and They are respectively with nodes j Active and reactive power of connected lines; and They are respectively t Node of time j Active power load and reactive power load; This is the set of the number of load nodes in the power grid.

[0150] The forward pressure drop equation is constrained as follows:

[0151] ,

[0152] in, For nodes i The square of the voltage amplitude, , and The lines are respectively ij Resistance, reactance and impedance, and Scenes s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude.

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

[0154] ,

[0155] in, For the line ij The square of the current amplitude on, For nodes i The square of the voltage amplitude, and Scenes s Down t Timetable ij The active power and reactive power.

[0156] node j The power constraint is:

[0157] ,

[0158] in, For the remaining load, and The charging and discharging power of AA-CAES.

[0159] The formula for the balance constraint of active and reactive power is:

[0160] ,

[0161] ,

[0162] ,

[0163] ,

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

[0165] ,

[0166] ,

[0167] ,

[0168] The embodiments of this application can obtain the active and reactive power balance constraints and apparent power constraints of nodes according to the formula, which further improves the accuracy of the calculation and provides support for obtaining the optimal scheduling results in the future.

[0169] Optionally, in one embodiment of this application, the formulas for the upper and lower limits of the gas storage pressure are as follows:

[0170] ,

[0171] in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For time t The rate of change of air pressure in the gas storage chamber at any given time;

[0172] The formula for the constraint relationship between the compression state, power generation state, and thermal storage state is as follows:

[0173] ,

[0174] in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

[0175] Specifically, the advanced adiabatic compressed air energy storage scheduling model is constrained as follows, with the formulas for the upper and lower limits of the storage pressure being:

[0176] ,

[0177] in, For time t The air pressure in the gas storage chamber at all times; This is the initial air pressure in the gas storage chamber; For gas storage time The air pressure inside the tank at that time; , These are the upper and lower limits of the air pressure in the gas storage chamber; For one scheduling duration; For time t The rate of change of air pressure in the gas storage chamber at any given time.

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

[0179] ,

[0180] in, and These are the upper and lower limits of the AA-CAES voltage system power, respectively; and These are the upper and lower limits of the AA-CAES power generation capacity, respectively. , for t The compressor and expander output are constantly monitored. , These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

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

[0182] ,

[0183] The thermal storage constraints are as follows:

[0184] ,

[0185] in, , These are the power coefficients for the heat change rate of the compressor and expander, respectively. , Time periods t The heat production capacity of the compressor and the heat release capacity of the expander; , These are the initial heat storage capacity and time period of the thermal storage device. t Heat storage capacity; , These are the upper and lower limits of the heat storage capacity of the thermal storage device; This refers to the external heat output of the compressed air energy storage system.

[0186] According to the embodiments of this application, the upper and lower limits of gas storage pressure and the relationship between compression state and power generation state can be obtained based on the formula, which further improves the accuracy of the calculation and provides support for obtaining the optimal scheduling result in the future.

[0187] In step S104, based on the objective function of the scheduling model and relevant constraints, a mixed-integer linear programming model is established, and the mixed-integer linear programming model is solved and optimized to obtain the stochastic scheduling results of the distribution network taking into account advanced adiabatic compressed air energy storage.

[0188] In actual implementation, the embodiments of this application can linearize the nonlinear constraints in the above model, and then, in combination with the above objective function and related constraints, establish a mixed integer linear programming model for stochastic scheduling of distribution networks with advanced adiabatic compressed air energy storage, and finally call CPLEX to solve it to obtain the scheduling results.

[0189] In the above scheduling model:

[0190] ,

[0191] in, For the line ij The square of the current amplitude on, For nodes i The square of the voltage amplitude, and Scenes s Down t Timetable ij The active and reactive power are given by the above formula, which is a non-convex constraint and requires relaxation. Based on the second-order cone model, the inequality is as follows:

[0192] ,

[0193] Replace the above lines. ij The apparent power constraint at the starting point is transformed into a second-order cone form, as shown in the following expression:

[0194] ,

[0195] in, and Scenes s Downt Timetable ij Active power and reactive power; For the line ij The square of the current amplitude; For nodes i The square of the voltage amplitude.

[0196] The embodiments of this application can combine the above objective function and related constraints to establish a stochastic scheduling model of a distribution network containing advanced adiabatic compressed air energy storage, and then call CPLEX to solve it 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 of the present application is used to elaborate in detail the working principle of a random dispatch method for distribution networks that takes into account advanced adiabatic compressed air energy storage.

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

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

[0200] Step S202: Establish AC power flow constraints for the distribution network and advanced adiabatic compressed air energy storage scheduling model constraints 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 embodiments of this application, a stochastic dispatching method for distribution networks considering advanced adiabatic compressed air energy storage (AAS) can be proposed. Latin hypercube sampling and scenario reduction methods can be used to describe the uncertain output of photovoltaic power. Next, a dispatching model objective function is established, along with AC power flow constraints and AAS dispatching model constraints based on a second-order cone model. Finally, CPLEX is used to solve the optimized dispatching model, obtaining the optimal dispatching strategy that meets the actual operational needs of the distribution network. This solves the problem that related technologies, in their research on the economic dispatching of systems containing AAS, failed to consider the impact of uncertain renewable energy output on the dispatching operation of distribution networks containing AAS. The inherent volatility and intermittency of new energy sources such as wind and solar power can cause prominent problems such as reverse power flow, overcurrent, and node voltage exceeding limits in the distribution network, posing technical challenges to the safe dispatching operation of the distribution network and leading to the proposed dispatching strategy failing to meet the actual operational needs of the distribution network.

[0203] Next, referring to the accompanying drawings, a random dispatching device for a distribution network that incorporates advanced adiabatic compressed air energy storage is described according to an embodiment of this application.

[0204] Figure 3 This is a schematic diagram of the structure of a random dispatching device for a power distribution network that incorporates advanced adiabatic compressed air energy storage, according to an embodiment of this application.

[0205] like Figure 3 As shown, the random dispatching device 10 for a power distribution network that incorporates advanced adiabatic compressed air energy storage includes: a determination module 100, a first establishment module 200, a second establishment module 300, and a dispatching module 400.

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

[0207] The first module 200 is used to establish a scheduling model objective function for advanced adiabatic compressed air energy storage based on representative power output scenarios, including at least one of the following: power purchase cost, wind and solar curtailment cost, and advanced adiabatic compressed air energy storage operation cost.

[0208] The second module 300 is used to establish the relevant constraint conditions for 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 dispatch model.

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

[0210] Optionally, in one embodiment of this application, the determining module 100 includes: a determining unit.

[0211] The determining unit is used to iteratively reduce the photovoltaic output scenarios by using the probability of photovoltaic output scenarios and the scenario distance until the photovoltaic output scenarios reach the preset iteration stop condition, thereby determining the representative output scenarios.

[0212] Optionally, in one embodiment of this application, the objective function of the scheduling model for advanced adiabatic compressed air energy storage is formulated as follows:

[0213] ,

[0214] in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

[0215] Optionally, in one embodiment of this application, the formula for the main grid electricity purchase cost is:

[0216] ,

[0217] in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid , These are the time sets and node sets connected to the main power grid, respectively.

[0218] The formula for the cost of wind and solar power curtailment is:

[0219] ,

[0220] in, and Scenes s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment.

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

[0222] ,

[0223] in, s For the scene ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

[0224] Optionally, in one embodiment of this application, the second establishment module 300 includes: a first establishment unit, a second establishment unit, and a component unit.

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

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

[0227] The constituent units are used to form relevant constraint conditions based on 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 dispatch model.

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

[0229] ,

[0230] ,

[0231] ,

[0232] ,

[0233] in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodes i The lower and upper limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and Scenes s Next moment t Actual grid-connected wind power and photovoltaic power and Scenes s Next moment t Fluctuations in wind and solar power output For the scene s Next moment t Generator reactive power For the remaining reactive load, for t Node of time j reactive power load, for t Reactive power of the AA-CAES system at all times For nodes i The square of the voltage amplitude;

[0234] The formula for apparent power constraint is:

[0235] ,

[0236] ,

[0237] ,

[0238] in, and Scenes s Downline ij Rated active power and rated reactive power, and Scenes s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on, For the line ij exist t Resistance at time, For the line ij exist t Reactance at any given moment.

[0239] Optionally, in one embodiment of this application, the formulas for the upper and lower limits of the gas storage pressure are as follows:

[0240] ,

[0241] in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For time t The rate of change of air pressure in the gas storage chamber at any given time;

[0242] The formula for the constraint relationship between the compression state, power generation state, and thermal storage state is as follows:

[0243] ,

[0244] in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

[0245] It should be noted that the foregoing explanation of an embodiment of a random dispatch method for a distribution network that takes into account advanced adiabatic compressed air energy storage also applies to a random dispatch device for a distribution network that takes into account advanced adiabatic compressed air energy storage in this embodiment, and will not be repeated here.

[0246] According to the embodiments of this application, a random dispatching device for a distribution network considering advanced adiabatic compressed air energy storage (AAC) can be proposed. Latin hypercube sampling and scenario reduction methods can be used to describe the uncertain output of photovoltaic power. Next, a dispatching model objective function is established, along with AC power flow constraints and advanced AAC energy storage dispatching model constraints based on a second-order cone model. Finally, CPLEX is used to solve the optimized dispatching model, obtaining the optimal dispatching strategy that meets the actual operational needs of the distribution network. This solves the problem that related technologies, in their research on the economic dispatching of systems containing advanced AAC energy storage, did not consider the impact of uncertain renewable energy output on the dispatching operation of distribution networks containing advanced AAC energy storage. The inherent volatility and intermittency of new energy sources such as wind and solar power may cause prominent problems such as reverse power flow, overcurrent, and node voltage exceeding limits in the distribution network, posing technical challenges to the safe dispatching operation of the distribution network and leading to the proposed dispatching strategy failing to meet the actual operational needs of the distribution network.

[0247] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0248] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0249] When the processor 402 executes the program, it implements the random dispatching method for power distribution networks that takes into account advanced adiabatic compressed air energy storage provided in the above embodiments.

[0250] Furthermore, electronic devices also include:

[0251] Communication interface 403 is used for communication between memory 401 and processor 402.

[0252] The memory 401 is used to store computer programs that can run on the processor 402.

[0253] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0254] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0255] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0256] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0257] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for random dispatching of a distribution network that incorporates advanced adiabatic compressed air energy storage.

[0258] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0259] Furthermore, 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0260] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0261] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0262] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0263] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0264] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0265] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for stochastic dispatching of a distribution network that takes into account advanced adiabatic compressed air energy storage, characterized in that, Includes the following steps: The photovoltaic power output prediction data is sampled and processed using the Latin hypercube sampling method to generate uncertain power output scenarios. The uncertain scenarios are then reduced using the scenario reduction method to determine representative power output scenarios. Based on the aforementioned representative power output scenarios, an objective function for the scheduling model of advanced adiabatic compressed air energy storage is established, which includes at least one of the following: electricity purchase cost, wind and solar curtailment cost, and advanced adiabatic compressed air energy storage operation cost. Establish relevant constraint conditions for AC power flow constraints of distribution networks based on the second-order cone model and the advanced adiabatic compressed air energy storage dispatch model; Based on the objective function of the scheduling model and the relevant constraints, a mixed-integer linear programming model is established, and the mixed-integer linear programming model is solved and optimized to obtain the stochastic scheduling results of the distribution network taking into account advanced adiabatic compressed air energy storage. The relevant 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 dispatch model include: Establish AC power flow constraints for distribution networks based on a second-order cone model, including at least one of the following: active power and reactive power balance constraints, apparent power constraints, and upper and lower limits of photovoltaic power constraints. Establish constraints for an advanced adiabatic compressed air energy storage scheduling model that include at least one of the following: upper and lower limits of gas storage pressure, compression state, power generation state, and thermal storage state. The relevant constraint conditions are composed 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 formula for the active power and reactive power balance constraint is: ， ， ， ， in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodes i The upper and lower limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and The scenarios are respectively s Next moment t Actual grid-connected wind power and photovoltaic power and The scenarios are respectively s Next moment t Fluctuations in wind and solar power output For the scenario s Next moment t Generator reactive power For the remaining reactive load, for t Node of time j reactive power load, For the t Reactive power of the AA-CAES system at all times For the node i The square of the voltage amplitude; The formula for the apparent power constraint is: ， ， ， in, and The scenarios are respectively s Downline ij Rated active power and rated reactive power, and The scenarios are respectively s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on For the line ij In the t Resistance at time, For the line ij In the t Reactance at any given moment.

2. The method according to claim 1, characterized in that, The process of reducing uncertain scenarios using scenario reduction methods to determine representative output scenarios includes: The photovoltaic output scenarios are iteratively reduced by using the probability of photovoltaic output scenarios and the distance between scenarios until the photovoltaic output scenarios reach the preset iteration stop condition, thereby determining the representative output scenarios.

3. The method according to claim 1, characterized in that, The objective function of the scheduling model for the advanced adiabatic compressed air energy storage is formulated as follows: ， in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

4. The method according to claim 3, characterized in that, The formula for the main power grid's electricity purchase cost is: ， in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid. , These are the time sets and node sets connected to the main power grid, respectively. The formula for the cost of wind and solar power curtailment is: ， in, and The scenarios are respectively s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment. The formula for the operating cost of the compressed air energy storage is: ， in, s For the scenario ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

5. The method according to claim 1, characterized in that, The formulas for the upper and lower limits of the gas storage pressure are as follows: ， in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For the time t The rate of change of air pressure in the gas storage chamber at any given time; The formula constraining the relationship between the compression state, power generation state, and thermal storage state is as follows: ， in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

6. A random dispatching device for a distribution network that incorporates advanced adiabatic compressed air energy storage, characterized in that, include: The determination module is used to sample and process 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. The first module is used to establish an objective function for the scheduling model of advanced adiabatic compressed air energy storage based on the representative output scenario, including at least one of the following: electricity purchase cost, wind and solar curtailment cost, and advanced adiabatic compressed air energy storage operation cost. The second module is used to establish the relevant constraint conditions for 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 dispatch model. The scheduling module is used to establish a mixed-integer linear programming model based on the objective function of the scheduling model and the relevant constraints, solve and optimize the mixed-integer linear programming model, and obtain the stochastic scheduling result of the distribution network taking into account advanced adiabatic compressed air energy storage. The second establishment module includes: The first establishment unit is used to establish AC power flow constraints for the distribution network based on the second-order cone model, including at least one of the active power and reactive power balance constraints, apparent power constraints, and photovoltaic power upper and lower limit constraints. The second establishment unit is used to establish constraints for an advanced adiabatic compressed air energy storage scheduling model, including at least one of the constraints on the relationship between the upper and lower limits of gas storage pressure, compression state, power generation state, and thermal storage state. The constituent unit is used to form the relevant constraint conditions by combining 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 formula for the active power and reactive power balance constraint is: ， ， ， ， in, and These are the lower and upper limits of the generator's active power, respectively. and These are the lower and upper limits of the generator's reactive power, respectively. and They are nodes i The upper and lower limits of the square of the voltage amplitude. and Scenes s Next moment t Upper limits for wind and solar power capacity and The scenarios are respectively s Next moment t Actual grid-connected wind power and photovoltaic power and The scenarios are respectively s Next moment t Fluctuations in wind and solar power output For the scenario s Next moment t Generator reactive power For the remaining reactive load, for t Node of time j reactive power load, For the t Reactive power of the AA-CAES system at all times For the node i The square of the voltage amplitude; The formula for the apparent power constraint is: ， ， ， in, and The scenarios are respectively s Downline ij Rated active power and rated reactive power, and The scenarios are respectively s Down t Timetable ij Active power and reactive power, For the line ij The square of the current amplitude on, For the line ij In the t Resistance at time, For the line ij In the t Reactance at any given moment.

7. The apparatus according to claim 6, characterized in that, The determining module includes: The determination unit is used to iteratively reduce the photovoltaic output scenarios by using the probability of photovoltaic output scenarios and the scenario distance until the photovoltaic output scenarios reach the preset iteration stop condition, and determine the representative output scenario.

8. The apparatus according to claim 7, characterized in that, The objective function of the scheduling model for the advanced adiabatic compressed air energy storage is formulated as follows: ， in, , , Scenes s The costs of purchasing electricity from the main grid, the costs of curtailing wind and solar power, and the operating costs of compressed air energy storage. A CAES (Compressed Air Energy Storage System) collection connected to the main power grid.

9. The apparatus according to claim 8, characterized in that, The formula for the main power grid's electricity purchase cost is: ， in, In order to purchase electricity from the main grid, The price of electricity purchased from the main grid. , These are the time sets and node sets connected to the main power grid, respectively. The formula for the cost of wind and solar power curtailment is: ， in, and The scenarios are respectively s Down t Real-time wind and solar power curtailment This refers to the penalty coefficient for wind and solar power curtailment. The formula for the operating cost of the compressed air energy storage is: ， in, s For the scenario ， and These are the startup costs for the compression and expansion sides of compressed air energy storage, respectively. A compressed air energy storage system connected to the main power grid. The time set connected to the main power grid.

10. The apparatus according to claim 9, characterized in that, The formulas for the upper and lower limits of the gas storage pressure are as follows: ， in, For time t The air pressure in the gas storage chamber at all times. The initial air pressure in the gas storage chamber. For gas storage time The air pressure inside the tank at that time and These represent the upper and lower limits of air pressure in the gas storage chamber. For a scheduling duration, For the time t The rate of change of air pressure in the gas storage chamber at any given time; The formula constraining the relationship between the compression state, power generation state, and thermal storage state is as follows: ， in, and These are binary variables representing the compression and power generation conditions of the compressed air energy storage system, respectively.

11. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement a random dispatch method for a distribution network that takes into account advanced adiabatic compressed air energy storage as described in any one of claims 1-5.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement a random dispatch method for a distribution network that takes into account advanced adiabatic compressed air energy storage as described in any one of claims 1-5.

Images