CN-122000915-A - Feasible region modeling method and device for power distribution system, system and storage medium

CN122000915ACN 122000915 ACN122000915 ACN 122000915ACN-122000915-A

Abstract

The embodiment of the application provides a feasible region modeling method, a feasible region modeling device, a feasible region modeling system and a feasible region modeling storage medium for a power distribution system, and relates to the technical field of feasible region modeling of the power distribution system. The method comprises the steps of constructing an autonomous optimization model of the power distribution system, carrying out decision optimization by taking economy as a target, solving and obtaining an energy state reference value of the energy storage device in a scheduling time domain, supplementing energy decoupling constraint in an original feasible domain based on the energy state reference value, constructing a time decoupling feasible domain of the original feasible domain, realizing Gao Weijiang dimensions of the original feasible domain, and carrying out parallel calculation on vertexes of all sub-feasible domains on the time decoupling feasible domain after decoupling and dimension reduction by adopting a vertex search method, and polymerizing to obtain an aggregation equivalent feasible domain of the power distribution system. By obtaining cooperation of reference, decoupling dimension reduction and parallel solution, on the premise of maintaining feasibility, calculation efficiency and feasibility of the feasible domain representation of the power distribution system with time coupling constraint are improved.

Inventors

YU PI
SUN SHUMIN
WANG YUEJIAO
GUAN YIFEI
ZHAO CHENYU
LIU YIYUAN
LI JIANXIU
XING JIAWEI
ZUO XINBIN
CHENG YAN
LIU MINGLIN
GE YU
ZHANG ZHIGANG
FANG MU
YANG SONG

Assignees

国网山东省电力公司电力科学研究院
国家电网有限公司

Dates

Publication Date: 20260508
Application Date: 20251208

Claims (16)

1.A method of modeling a viable area for a power distribution system, comprising: Constructing an autonomous optimization model of the power distribution system, carrying out decision optimization with economy as a target, and solving to obtain an energy state reference value of the energy storage equipment in a scheduling time domain; based on the energy state reference value, supplementing energy decoupling constraint in the original feasible domain, constructing a time decoupling feasible domain of the original feasible domain, and realizing Gao Weijiang dimensions of the original feasible domain; and (3) for the time decoupling feasible domains after decoupling and dimension reduction, adopting a vertex search method to calculate the vertices of all the sub-feasible domains in parallel, and aggregating to obtain an aggregate equivalent feasible domain of the power distribution system.
2. The method of claim 1, wherein constructing an autonomous optimization model of the power distribution system, performing decision optimization with economy as a target, and solving to obtain an energy state reference value of the energy storage device in a scheduling time domain comprises: Constructing an autonomous optimization model taking the total running cost of the minimized power distribution system as an objective function; Setting constraint conditions of an autonomous optimization model, wherein the constraint conditions comprise operation constraint of all distributed resources in a power distribution system, network security constraint based on a linearization alternating current power flow model and/or system power balance constraint; And solving an autonomous optimization model, and outputting a charge state optimal value of the energy storage equipment in each scheduling period as an energy state reference value.
3. The method of claim 2, wherein the total operating cost of the power distribution system comprises a superior grid purchase cost, and wherein the calculation of the superior grid purchase cost comprises: acquiring active power input at a public coupling node of the power distribution system connected with an upper power grid; and multiplying the active power by the preset unit electricity purchasing cost to obtain the electricity purchasing cost of the upper power grid.
4. The method of claim 2, wherein the total operating cost of the power distribution system comprises a power rejection penalty cost of the distributed photovoltaic, and wherein calculating the power rejection penalty cost of the distributed photovoltaic comprises: Acquiring the predicted daily power and the actual output power of the distributed photovoltaic; Calculating the difference between the predicted power and the actual output power; and multiplying the difference value with a preset unit electricity discarding penalty cost to obtain the electricity discarding penalty cost of the distributed photovoltaic.
5. The method of claim 2, wherein the total operating cost of the power distribution system comprises an operating cost of the energy storage device, and wherein calculating the operating cost of the energy storage device comprises: acquiring charging power and discharging power of energy storage equipment; Calculating the sum of absolute values of the charging power and the discharging power; And multiplying the sum of the absolute values by a preset unit charge and discharge cost to obtain the operation cost of the energy storage equipment.
6. The method of claim 2, wherein the operational constraints of all distributed resources in the power distribution system include operational constraints of distributed photovoltaics, the operational constraints of distributed photovoltaics comprising: The actual output power of the distributed photovoltaic does not exceed its predicted power before day.
7. The method of claim 2, wherein the operational constraints of all distributed resources in the power distribution system comprise operational constraints of the energy storage device, and wherein the operational constraints of the energy storage device comprise: the charging power of the energy storage device does not exceed its upper charging power limit, and/or, The discharge power of the energy storage device does not exceed its upper discharge power limit, and/or, The state of charge of the energy storage device is between its lower and upper state of charge limits, and/or, The energy evolution of the energy storage devices between adjacent scheduling periods satisfies an energy conservation relationship determined by the charging efficiency, the discharging efficiency, the charging power and/or the discharging power.
8. The method of any of claims 1 to 7, wherein supplementing energy decoupling constraints in the original feasible region based on the energy state reference values, constructing a time-decoupled feasible region of the original feasible region, implementing Gao Weijiang dimensions of the original feasible region, comprises: dividing the whole scheduling time domain into a plurality of continuous time period windows; For each time period window, supplementing energy decoupling constraint in an original feasible domain, and constructing a low-dimensional time decoupling feasible domain corresponding to the time period window, wherein the energy decoupling constraint fixes the charge state of the energy storage device at the starting moment of the time period window to be the value of an energy state reference value at the moment; And taking the low-dimensional time decoupling feasible domains corresponding to all the time period windows as time decoupling feasible domains.
9. The method of claim 8, wherein for each time period window, supplementing energy decoupling constraints in the original feasible region, constructing a low-dimensional time decoupling feasible region corresponding to the time period window, comprising: inheriting all constraint conditions belonging to the time period window in the original feasible domain; increasing energy decoupling constraint at the starting moment of the time window, wherein the energy decoupling constraint is that the state of charge of the energy storage device at the moment is equal to an energy state reference value; the inherited constraint conditions and the added energy decoupling constraint jointly form a low-dimensional time decoupling feasible domain corresponding to the time period window.
10. The method of claim 8, wherein supplementing energy decoupling constraints in the original feasible region based on the energy state reference values, constructing a time-decoupled feasible region of the original feasible region, implementing Gao Weijiang dimensions of the original feasible region, further comprises: and performing union operation on the low-dimensional time decoupling feasible domains corresponding to all the time period windows to obtain an equivalent inner approximate feasible domain of the whole scheduling time domain.
11. The method according to any one of claims 1 to 7, wherein for the time-decoupled feasible regions after decoupling the dimension reduction, vertex search is used to calculate vertices of each sub-feasible region in parallel, comprising: for each of the low-dimensional feasible domains of the time-decoupled feasible domains, a vertex search algorithm is performed in parallel to identify all vertices of the low-dimensional feasible domain.
12. The method of claim 11, wherein the step of determining the position of the probe is performed, The vertex search algorithm comprises an initialization stage, an inner loop optimization stage and a convergence judgment stage.
13. The method according to any one of claims 1 to 7, wherein aggregating results in an aggregate equivalent feasible region of the power distribution system, comprising: Projecting the vertex set of each low-dimensional feasible domain on the boundary variable space of the vertex set, wherein the boundary variables comprise active injection power and reactive injection power at a common coupling node; and aggregating the projection results to form a set of feasible ranges of the descriptive boundary variables as an aggregate equivalent feasible domain.
14. A feasible region modeling apparatus for a power distribution system comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the feasible region modeling method for a power distribution system of any of claims 1-13 when the program instructions are executed.
15. A system, comprising: system body, and The viable domain modeling apparatus for power distribution systems of claim 14, mounted to the system body.
16. A computer readable storage medium storing program instructions which, when executed, are to cause a computer to perform the feasible region modeling method for a power distribution system of any of claims 1 to 13.

Description

Feasible region modeling method and device for power distribution system, system and storage medium Technical Field The application relates to the technical field of feasible region modeling of power distribution systems, in particular to a feasible region modeling method, a device, a system and a storage medium for a power distribution system. Background At present, along with the rapid development of renewable energy sources, a large amount of resources such as distributed photovoltaic, energy storage and the like are accessed into a power distribution system, so that the flexible regulation potential of the system is remarkably enhanced. However, the dramatic increase in the number of distributed resources also makes operational management of power distribution systems a serious challenge, particularly how to accurately characterize the operational feasibility of the system to support optimal scheduling decisions, a fundamental problem that is currently in need of solution. To address this challenge, the related art mainly adopts two types of methods for feasible region characterization. One class is a vertex search algorithm based on an exact solution, whose convex hull representation is obtained by solving the vertices of a convex polyhedron. The other is a method based on approximate solution, and parameters of the geometric shapes such as hypercube, fixed convex polyhedron and the like of the outline structure of the aggregation feasible region are preset to fit so as to realize approximate characterization. In the process of implementing the embodiment of the application, the related art is found to have at least the following problems: With the access of the time coupling equipment for energy storage and the like, the feasible domain of the power distribution system presents high-dimensional time coupling characteristics, so that an accurate method based on vertex search faces a dimension disaster, and the problem of solving the feasible domain with more than 6 dimensions is difficult to process. While the method based on the approximate solution can process time coupling constraint, more approximate processing is introduced, so that the result accuracy is limited and tends to be too conservative, and the feasibility and accuracy of calculation are difficult to ensure at the same time. Disclosure of Invention The embodiment of the application provides a feasible region modeling method, a feasible region modeling device, a feasible region modeling system and a feasible region modeling storage medium for a power distribution system. In a first aspect of an embodiment of the present application, a feasible region modeling method for a power distribution system is provided, including: Constructing an autonomous optimization model of the power distribution system, carrying out decision optimization with economy as a target, and solving to obtain an energy state reference value of the energy storage equipment in a scheduling time domain; based on the energy state reference value, supplementing energy decoupling constraint in the original feasible domain, constructing a time decoupling feasible domain of the original feasible domain, and realizing Gao Weijiang dimensions of the original feasible domain; and (3) for the time decoupling feasible domains after decoupling and dimension reduction, adopting a vertex search method to calculate the vertices of all the sub-feasible domains in parallel, and aggregating to obtain an aggregate equivalent feasible domain of the power distribution system. In an optional embodiment of the present application, constructing an autonomous optimization model of a power distribution system, performing decision optimization with economy as a target, and solving to obtain an energy state reference value of an energy storage device in a scheduling time domain, where the method includes: Constructing an autonomous optimization model taking the total running cost of the minimized power distribution system as an objective function; Setting constraint conditions of an autonomous optimization model, wherein the constraint conditions comprise operation constraint of all distributed resources in a power distribution system, network security constraint based on a linearization alternating current power flow model and/or system power balance constraint; And solving an autonomous optimization model, and outputting a charge state optimal value of the energy storage equipment in each scheduling period as an energy state reference value. In an alternative embodiment of the application, the total running cost of the power distribution system comprises the purchase cost of the upper power grid, and the calculation of the purchase cost of the upper power grid comprises the following steps: acquiring active power input at a public coupling node of the power distribution system connected with an upper power grid; and multiplying the active power by the preset unit electricity purchasing cost to