CN-120073657-B - Power distribution network random scheduling method considering advanced adiabatic compressed air energy storage

CN120073657BCN 120073657 BCN120073657 BCN 120073657BCN-120073657-B

Abstract

The application relates to a random scheduling method of a power distribution network considering advanced adiabatic compressed air energy storage, which comprises the steps of sampling photovoltaic predicted force data by using a Latin hypercube sampling method to generate an uncertain output scene, determining a representative output scene, establishing an advanced adiabatic compressed air energy storage scheduling model objective function, establishing related constraint conditions of power distribution network alternating current trend constraint and advanced adiabatic compressed air energy storage scheduling model constraint 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 random scheduling result of the power distribution network considering advanced adiabatic compressed air energy storage. Therefore, the problem that the provided scheduling strategy cannot meet the actual operation requirement of the power distribution network due to the influence of uncertain output force of renewable energy sources on the scheduling operation of the power distribution network containing advanced adiabatic compressed air energy storage in the related technology is solved.

Inventors

Chang Sizhe
TAN WEI
GAO SHUO
JIA YUNPENG
WANG HONG
MEI SHENGWEI
Teng Zhichu
CAO DONGMEI
CHEN RENFENG
MU CHAO
Shen Chengzhang
WANG YUE
Jin Xuewei

Assignees

中国长江三峡集团有限公司
中国三峡新能源(集团)股份有限公司
国水集团化德风电有限公司
上海勘测设计研究院有限公司
清华大学
安徽佑赛科技股份有限公司

Dates

Publication Date: 20260508
Application Date: 20241226

Claims (12)

1. The power distribution network random scheduling method considering advanced adiabatic compressed air energy storage is characterized by comprising the following steps of: Sampling the photovoltaic predicted output data by using a Latin hypercube sampling method to generate an uncertain output scene, reducing the uncertain scene by using a scene elimination method, and determining a representative output scene; Establishing a scheduling model objective function of an advanced adiabatic compressed air energy storage comprising at least one of electricity purchasing cost, abandoned wind abandoned light cost and advanced adiabatic compressed air energy storage operation cost based on the representative output scenario; Establishing related constraint conditions of power distribution network alternating current power flow constraint and advanced adiabatic compressed air energy storage scheduling model constraint based on a second order cone model; based on the scheduling model objective function and the related constraint conditions, a mixed integer linear programming model is established, and the mixed integer linear programming model is solved and optimized to obtain a random scheduling result of the power distribution network considering advanced adiabatic compressed air energy storage; the establishment of the related constraint conditions of the power distribution network alternating current power flow constraint and the advanced adiabatic compressed air energy storage scheduling model constraint based on the second order cone model comprises the following steps: Establishing a second order cone model-based power distribution network alternating current power flow constraint comprising at least one of active power and reactive power balance constraint, apparent power constraint and photovoltaic power upper and lower limit constraint; establishing advanced adiabatic compressed air energy storage scheduling model constraints comprising at least one of upper limit and lower limit constraints of gas storage pressure, compression state, power generation state and relation constraint of heat storage state; The related constraint conditions are formed by the power distribution network alternating current power flow constraint based on the second order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint; The formula of the active power and reactive power balance constraint is as follows: , , , , Wherein, the And The lower limit and the upper limit of the active power of the generator are respectively, And The lower limit and the upper limit of the reactive power of the generator are respectively, And The upper and lower limits of the square of the voltage amplitude at node i, And Respectively the upper limit of wind power and photovoltaic power at the moment tsuper under the scene s, And The actual grid-connected wind power and photovoltaic power at the moment t under the scene s are respectively, And Respectively the fluctuation amounts of wind power and photovoltaic power at the moment tstare under the scene s, For the moment ttenerator reactive power in said scenario s, For the remaining amount of reactive load, Is the reactive power load of node j at time t, For the reactive power of the AA-CAES system at time t, Square the voltage magnitude of the node i; the apparent power constraint formula is: , , , Wherein, the And The rated active power and the rated reactive power of the line ij under the scene s are respectively, And Respectively the active power and the reactive power of the line ij at the moment t under the scene s, Is the square of the current amplitude on line ij, For the resistance of said line ij at said time t, Is the reactance of the line ij at the time t.
2. The method of claim 1, wherein the reducing the uncertainty scene using scene subtraction method to determine a representative output scene comprises: And iteratively reducing the photovoltaic output scene by using the probability of the photovoltaic output scene and the scene distance until the photovoltaic output scene reaches a preset iteration stop condition, and determining the representative output scene.
3. The method of claim 1, wherein the formulation of the advanced adiabatic compressed air energy storage scheduling model objective function is: , Wherein, the 、、 The electricity purchasing cost from the main power grid, the wind and light discarding cost and the compressed air energy storage operation cost of the scene s are respectively, And (3) storing energy for CAES compressed air connected with a main power grid.
4. A method according to claim 3, wherein the formula of the main grid electricity purchase cost is: , Wherein, the In order to purchase power from the main grid, To purchase electricity from the main grid, 、 Respectively a time set and a node set connected with a main power grid; the formula of the wind and light discarding cost is as follows: , Wherein, the And The air discarding quantity and the light discarding quantity at the moment t under the scene s are respectively, Punishment coefficients for wind and light abandoning; the formula of the compressed air energy storage operation cost is as follows: , Wherein s is the scene, And The starting costs for the compressed air energy storage compression side and the expansion side respectively, For a compressed air energy storage collection connected to a main grid, Is a set of times connected to the main grid.
5. The method as claimed in claim 1, wherein the upper and lower limit constraints of the gas storage pressure are formulated as: , Wherein, the For the air pressure in the air reservoir at time t, For the initial air pressure in the air reservoir, For the time of the air storage chamber The air pressure in the tank at the time of the process, And The upper limit and the lower limit of the air pressure in the air storage chamber are respectively, For a length of time to be scheduled, The change rate of the air pressure in the air storage chamber at the moment t is the time; The formula of the relation constraint of the compression state, the power generation state and the heat storage state is as follows: , Wherein, the And The binary variables are the compression working condition and the power generation working condition of the compressed air energy storage system respectively.
6. A power distribution network random scheduling device that accounts for advanced adiabatic compressed air energy storage, comprising: The determining module is used for sampling and processing the photovoltaic predicted output data by using a Latin hypercube sampling method to generate an uncertain output scene, reducing the uncertain scene by using a scene elimination method and determining a representative output scene; The first building module is used for building a scheduling model objective function of advanced adiabatic compressed air energy storage, which comprises at least one of electricity purchasing cost, wind discarding and light discarding cost and advanced adiabatic compressed air energy storage operation cost, based on the representative output scene; the second establishing module is used for establishing related constraint conditions of the power distribution network alternating current power flow constraint based on the second order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint; The scheduling module is used for establishing a mixed integer linear programming model based on the scheduling model objective function and the related constraint conditions, solving and optimizing the mixed integer linear programming model, and obtaining a random scheduling result of the power distribution network considering advanced adiabatic compressed air energy storage; the second establishing module includes: the first establishing unit is used for establishing a second order cone model-based power distribution network alternating current power flow constraint comprising at least one of active power and reactive power balance constraint, apparent power constraint and photovoltaic power upper and lower limit constraint; the second establishing unit is used for establishing advanced adiabatic compressed air energy storage scheduling model constraints comprising at least one of gas storage pressure upper limit and lower limit constraints, compression state, power generation state and heat storage state relation constraints; The composition unit is used for composing the related constraint conditions by using the power distribution network alternating current power flow constraint based on the second order cone model and the advanced adiabatic compressed air energy storage scheduling model constraint; The formula of the active power and reactive power balance constraint is as follows: , , , , Wherein, the And The lower limit and the upper limit of the active power of the generator are respectively, And The lower limit and the upper limit of the reactive power of the generator are respectively, And The upper and lower limits of the square of the voltage amplitude at node i, And Respectively the upper limit of wind power and photovoltaic power at the moment tsuper under the scene s, And The actual grid-connected wind power and photovoltaic power at the moment t under the scene s are respectively, And Respectively the fluctuation amounts of wind power and photovoltaic power at the moment tstare under the scene s, For the moment ttenerator reactive power in said scenario s, For the remaining amount of reactive load, Is the reactive power load of node j at time t, For the reactive power of the AA-CAES system at time t, Square the voltage magnitude of the node i; the apparent power constraint formula is: , , , Wherein, the And The rated active power and the rated reactive power of the line ij under the scene s are respectively, And Respectively the active power and the reactive power of the line ij at the moment t under the scene s, Is the square of the current amplitude on line ij, For the resistance of said line ij at said time t, Is the reactance of the line ij at the time t.
7. The apparatus of claim 6, wherein the means for determining comprises: and the determining unit is used for iteratively reducing the photovoltaic output scene by using the probability of the photovoltaic output scene and the scene distance until the photovoltaic output scene reaches a preset iteration stop condition, and determining the representative output scene.
8. The apparatus of claim 7, wherein the advanced adiabatic compressed air energy storage scheduling model objective function is formulated as: , Wherein, the 、、 The electricity purchasing cost from the main power grid, the wind and light discarding cost and the compressed air energy storage operation cost of the scene s are respectively, And (3) storing energy for CAES compressed air connected with a main power grid.
9. The apparatus of claim 8, wherein the formula for the electricity purchase cost of the main grid is: , Wherein, the In order to purchase power from the main grid, To purchase electricity from the main grid, 、 Respectively a time set and a node set connected with a main power grid; the formula of the wind and light discarding cost is as follows: , Wherein, the And The air discarding quantity and the light discarding quantity at the moment t under the scene s are respectively, Punishment coefficients for wind and light abandoning; the formula of the compressed air energy storage operation cost is as follows: , Wherein s is the scene, And The starting costs for the compressed air energy storage compression side and the expansion side respectively, For a compressed air energy storage collection connected to a main grid, Is a set of times connected to the main grid.
10. The apparatus according to claim 9, wherein the upper and lower limit constraints of the gas storage pressure are formulated as: , Wherein, the For the air pressure in the air reservoir at time t, For the initial air pressure in the air reservoir, For the time of the air storage chamber The air pressure in the tank at the time of the process, And The upper limit and the lower limit of the air pressure in the air storage chamber are respectively, For a length of time to be scheduled, The change rate of the air pressure in the air storage chamber at the moment t is the time; The formula of the relation constraint of the compression state, the power generation state and the heat storage state is as follows: , Wherein, the And The binary variables are the compression working condition and the power generation working condition of the compressed air energy storage system respectively.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement a power distribution network random dispatch method accounting for advanced adiabatic compressed air energy storage as claimed in any one of claims 1-5.
12. A computer readable storage medium having stored thereon a computer program, the program being executable by a processor for implementing a power distribution network random scheduling method according to any one of claims 1-5 taking into account advanced adiabatic compressed air energy storage.

Description

Power distribution network random scheduling method considering advanced adiabatic compressed air energy storage Technical Field The application relates to the technical field of energy storage systems, in particular to a random scheduling method of a power distribution network taking advanced adiabatic compressed air energy storage into account. Background Due to the increasingly serious problems of environmental crisis, resource shortage and the like, the development and utilization of renewable energy sources such as wind power, photovoltaics and the like are greatly becoming strategic consensus of governments of various countries. Along with the fact that new energy power stations such as photovoltaic and wind power and energy storage devices are integrated into a power distribution network in a large number, how to reasonably utilize flexible resources to achieve optimal scheduling of the power distribution network, and improvement of operation economy and safety of the power distribution network become important problems to be solved in the present. In the related technology, large-scale energy storage is an important method for stabilizing the fluctuation of new energy output and solving the problem of large-scale utilization of wind power and photovoltaic. Compressed air energy storage and pumped storage are mature large-scale energy storage technologies at present, and compared with pumped storage, the compressed air energy storage has low requirements on geographic positions and can be more flexibly applied to various scenes. The advanced adiabatic compressed air energy storage is used for recovering compression heat to replace fuel after-combustion based on the traditional compressed air energy storage technology, so that the operation efficiency and economy of the system are further improved, no combustion and zero carbon emission are realized in the operation process, and therefore, the advanced adiabatic compressed air energy storage is also considered as one of the large-scale energy storage technologies with the most development potential at present. Compared with a transmission grid, the capacity of the power distribution network at the tail end of the power system for coping with uncertain weather is obviously insufficient, however, the influence of uncertain output force of renewable energy sources on the dispatching operation of the power distribution network with advanced adiabatic compressed air energy storage is not considered in the research aiming at the economic dispatching problem of the power storage system with advanced adiabatic compressed air, the inherent fluctuation and intermittence of new energy sources such as wind energy, solar energy and the like can cause the outstanding problems of reversing power flow, overcurrent, node voltage out-of-limit and the like of the power distribution network, technical challenges are brought to the safe dispatching operation of the power distribution network, the proposed dispatching strategy can not meet the actual operation requirement of the power distribution network, and improvement is needed. Disclosure of Invention The application provides a random scheduling method of a power distribution network taking into account advanced adiabatic compressed air energy storage, which aims to solve the problem of economic scheduling of an advanced adiabatic compressed air energy storage system in the related technology, and solves the problems that the influence of uncertain output of renewable energy sources on scheduling operation of the power distribution network containing advanced adiabatic compressed air energy storage, inherent fluctuation and intermittence of new energy sources such as wind energy, solar energy and the like possibly cause outstanding problems of power flow reversal, overcurrent, node voltage out-of-limit and the like of the power distribution network, technical challenges are brought to safe scheduling operation of the power distribution network, and the proposed scheduling strategy cannot meet actual operation demands of the power distribution network. The embodiment of the first aspect of the application provides a random scheduling method of a power distribution network considering advanced adiabatic compressed air energy storage, which comprises the following steps of sampling photovoltaic predicted force data by using a Latin hypercube sampling method to generate an uncertain output scene, reducing the uncertain scene by using a scene elimination method to determine a representative output scene, establishing a scheduling model objective function of the advanced adiabatic compressed air energy storage, comprising at least one of electricity purchasing cost, wind abandoning cost and advanced adiabatic compressed air energy storage operation cost, based on the representative output scene, establishing related constraint conditions of power distribution network alternating current flow constraint and advanced adiabati