CN115271454A - Flexible resource aggregation feasible domain accurate modeling and approximation method, device and equipment - Google Patents

Flexible resource aggregation feasible domain accurate modeling and approximation method, device and equipment Download PDF

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CN115271454A
CN115271454A CN202210903146.1A CN202210903146A CN115271454A CN 115271454 A CN115271454 A CN 115271454A CN 202210903146 A CN202210903146 A CN 202210903146A CN 115271454 A CN115271454 A CN 115271454A
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胡泽春
文艺林
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Abstract

The application relates to the technical field of flexible resource aggregation, in particular to a flexible resource aggregation feasible domain accurate modeling and approximation method, device and equipment, wherein the method comprises the following steps: detecting the actual type of each flexible resource on the demand side of the power grid; constructing a monomer feasible region model of each flexible resource according to the actual type, and polymerizing all the monomer feasible region models to generate a polymerization feasible region model; screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregation feasible domain model, constructing an approximate feasible domain model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible domain model, and scheduling each flexible resource according to the actual distribution proportion. Therefore, the problems that the consideration of the aggregation feasible region is simple, the smooth operation of flexible resource allocation cannot be ensured, the calculation complexity is high and the like in the related technology are solved.

Description

Flexible resource aggregation feasible domain accurate modeling and approximation method, device and equipment
Technical Field
The application relates to the technical field of flexible resource aggregation, in particular to a flexible resource aggregation feasible domain accurate modeling and approximation method, device and equipment.
Background
The high proportion infiltration of intermittent renewable energy sources such as wind power, photovoltaic and the like increases the demand on the regulation capacity of the power grid, and under the background, the power grid can not rely on the regulation function of the traditional unit any more, and the regulation capacity of flexible resources on the demand side is brought into play. Some typical demand side flexible resources include distributed power generation, electric vehicles, distributed energy storage, thermal control loads, and the like. The demand side flexible resources participate in power grid regulation and control, safe and stable operation of the power grid can be guaranteed, new energy consumption is promoted, and the power utilization cost of users is reduced.
Because parameters of all demand side resources are considered to be too complex during power grid dispatching and are not beneficial to protecting the privacy of power users, the demand side flexible resources need to be aggregated and then participate in power grid regulation and control. In this mode, the flexible resource aggregators submit feasible domains of total power to the power grid, after clearing or scheduling calculation of the power grid operators is completed, the total power of each aggregator is determined, and the aggregators distribute the power to each resource to execute the power distribution. Therefore, it is necessary to construct feasible domains of aggregated resources accurately to ensure smooth allocation and should not result in excessive computational complexity.
In the current auxiliary service market rules, consideration of the aggregated feasible domain is simple. For example, the PJM power line market is a top and bottom regulatory boundary that only considers power. In North China, power and energy capacity are considered for energy storage and V2G charging piles, and only power regulation capacity is considered for controllable loads such as common charging piles and electric heating. The feasible regions described by these methods are generally far from the actual aggregate feasible region.
However, the consensus in the related art has been reached on an aggregate feasible domain of computationally flexible resources, i.e., this is a problem of minkowski-summing of high-dimensional spatial polyhedrons, generally without efficient algorithms; meanwhile, the related art often focuses on approximating such a feasible region from the inside or the outside, and there is no modeling method of accurately aggregating feasible regions.
Disclosure of Invention
The application provides a method, a device and equipment for accurately modeling and approximating a flexible resource aggregation feasible region, and aims to solve the problems that the consideration of the aggregation feasible region in the related technology is simple, the smooth operation of flexible resource allocation cannot be ensured, the calculation complexity is high and the like.
The embodiment of the first aspect of the application provides a flexible resource aggregation feasible domain accurate modeling and approximation method, which comprises the following steps: detecting the actual type of each flexible resource on the demand side of the power grid; constructing a monomer feasible region model of each flexible resource according to the actual type, and polymerizing all the monomer feasible region models to generate a polymerization feasible region model; screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregated feasible region model, constructing an approximate feasible region model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible region model, and scheduling each flexible resource according to the actual distribution proportion.
Optionally, in an embodiment of the present application, the building the single feasible domain model of each flexible resource according to the actual type includes: dividing a preset time window into a plurality of time periods according to a preset time interval; calculating power and energy boundary information of each time period according to the actual type of each flexible resource; and constructing the single feasible domain model according to the power and energy boundary information of each time segment.
Alternatively, in an embodiment of the present application, the polymerizing the all-monomer feasible domain model to obtain a polymerization feasible domain model includes: polymerizing the accumulated consumed energy or power of all monomer feasible region models to obtain a decision variable; generating a complete binary tree according to the energy consumed by each time period, and taking all path sets of the complete binary tree as constraint parameter sets; and constructing the aggregation feasible domain model according to the decision variables and the constraint parameter set.
Optionally, in an embodiment of the present application, the preset type of approximate feasible region model includes any one of a k-order approximate feasible region model, a long-and-short-time-period approximate feasible region model, and a time-period grouping approximate feasible region model.
Optionally, in an embodiment of the application, when the approximate feasible region model of the preset type is the approximate feasible region model of order k, the constructing the approximate feasible region model of the preset type based on the at least one constraint parameter includes: according to the actual order of the k-order approximate feasible domain modelSelecting a constraint branch in the at least one constraint parameter; constructing an approximate feasible domain model corresponding to the actual order based on the constraint branch, wherein the k-th order approximate model is
Figure BDA0003771622130000021
Wherein E represents a column vector formed by variables E (T) from T to 1,
Figure BDA0003771622130000022
representing the set of all paths of a complete binary tree of height T +1,
Figure BDA0003771622130000023
vla vector representing the composition of data in all nodes on path l (.)TRepresenting a vector transpose; lφand
Figure BDA0003771622130000024
each represents a boundary value corresponding to l.
Optionally, in an embodiment of the application, when the preset type of approximate feasible region model is the long and short time period approximate feasible region model, the constructing the preset type of approximate feasible region model based on the at least one constraint parameter includes: dividing time periods according to different time intervals, wherein the length of the minimum time period is equal to the preset time interval; and constructing the approximate feasible domain model of the long time period and the short time period according to the constraint parameters in each time period.
Optionally, in an embodiment of the present application, when the approximate feasible region model of the preset type is the time period grouping approximate feasible region model, the constructing the approximate feasible region model of the preset type based on the at least one constraint parameter includes: dividing a preset time window into a plurality of time periods according to a preset time interval; and dividing the time periods into a plurality of time period groups, and constructing the time period grouping approximate feasible domain model according to the constraint parameters in each time period group.
The embodiment of the second aspect of the present application provides a flexible resource aggregation feasible domain accurate modeling and approximation apparatus, including: the detection module is used for detecting the actual type of each flexible resource on the demand side of the power grid; the generating module is used for constructing the monomer feasible region model of each flexible resource according to the actual type and polymerizing all the monomer feasible region models to generate a polymerization feasible region model; and the scheduling module is used for screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregated feasible region model, constructing an approximate feasible region model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible region model, and scheduling each flexible resource according to the actual distribution proportion.
Optionally, in an embodiment of the application, the generating module is further configured to divide a preset time window into a plurality of time periods according to a preset time interval, calculate power and energy boundary information of each time period according to the actual type of each flexible resource, and construct the single feasible domain model according to the power and energy boundary information of each time period.
Optionally, in an embodiment of the application, the generating module is further configured to aggregate accumulated consumed energy or power of all monomer feasible domain models to obtain a decision variable, generate a complete binary tree according to energy consumed in each time period, use all path sets of the complete binary tree as a constraint parameter set, and construct the aggregated feasible domain model according to the decision variable and the constraint parameter set.
Optionally, in an embodiment of the present application, the preset type of approximate feasible region model includes any one of a k-order approximate feasible region model, a long-and-short-time-period approximate feasible region model, and a time-period grouping approximate feasible region model.
Optionally, in an embodiment of the present application, when the preset type of approximate feasible region model is the k-th order approximate feasible region model, the scheduling module is further configured to select a constrained branch in the at least one constrained parameter according to an actual order of the k-th order approximate feasible region model, and based on the constrained branch, select a constrained branch in the at least one constrained parameterConstructing an approximate feasible domain model corresponding to the actual order, wherein the k-th order approximate model is
Figure BDA0003771622130000031
Wherein E represents a column vector formed by a variable E (T) from T to 1,
Figure BDA0003771622130000032
representing the set of all paths of a complete binary tree of height T +1,
Figure BDA0003771622130000033
vla vector representing the composition of data in all nodes on path l (.)TRepresenting a vector transpose; lφand with
Figure BDA0003771622130000034
Each represents a boundary value corresponding to l.
Optionally, in an embodiment of the application, when the preset type of approximate feasible region model is the long-short time period approximate feasible region model, the scheduling module is further configured to divide time periods according to different time intervals, where a minimum time period length is equal to a preset time interval, and the long-short time period approximate feasible region model is constructed according to a constraint parameter in each time period.
Optionally, in an embodiment of the application, when the approximate feasible region model of the preset type is the time segment grouped approximate feasible region model, the scheduling module is further configured to divide a preset time window into a plurality of time segments according to a preset time interval, divide the plurality of time segments into a plurality of time segment groups, and construct the time segment grouped approximate feasible region model according to a constraint parameter in each time segment group.
An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the flexible resource aggregation feasible domain accurate modeling and approximation method as described in the above embodiments.
A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor, and is used to implement the flexible resource aggregation feasible domain accurate modeling and approximation method as described in the foregoing embodiments.
Therefore, the application has at least the following beneficial effects:
the method comprises the steps of constructing a single feasible region model of each flexible resource according to the actual type of each flexible resource on the demand side of a power grid, polymerizing all the single feasible region models to generate a polymerized feasible region model, extracting part of constraints from the polymerized feasible region model to form a series of approximate feasible region models, determining the actual distribution proportion of each flexible resource by using the approximate feasible region models, scheduling each flexible resource according to the actual distribution proportion, and ensuring the lowest operation cost or the highest profit of a flexible resource aggregator while ensuring the feasibility of power distribution as much as possible. Therefore, the problems that the consideration of the aggregation feasible region is simple, the smooth operation of flexible resource allocation cannot be ensured, the calculation complexity is high and the like in the related technology are solved.
Additional aspects and advantages of the present 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 the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart of a flexible resource aggregation feasible domain accurate modeling and approximation method provided in an embodiment of the present application;
FIG. 2 shows vectors v corresponding to all paths l provided according to an embodiment of the present applicationlA schematic diagram;
FIG. 3 is a block diagram illustrating an apparatus for accurately modeling and approximating a flexible resource aggregation feasible domain according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of the reference numerals: the device comprises a detection module-100, a generation module-200, a scheduling module-300, a memory-401, a processor-402 and a communication interface-403.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The flexible resource aggregation feasible domain accurate modeling and approximation method, apparatus, electronic device, and storage medium according to embodiments of the present application are described below with reference to the accompanying drawings. In view of the problems mentioned in the background art, the present application provides a flexible resource aggregation feasible domain accurate modeling and approximation method, in which the actual type of each flexible resource on the demand side of the power grid is detected; constructing a monomer feasible region model of each flexible resource according to the actual type, and polymerizing all the monomer feasible region models to generate a polymerization feasible region model; screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregation feasible domain model, constructing an approximate feasible domain model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible domain model, and scheduling each flexible resource according to the actual distribution proportion. Therefore, the problems that the consideration of the aggregation feasible region is simple, the smooth operation of flexible resource allocation cannot be ensured, the calculation complexity is high and the like in the related technology are solved.
Specifically, fig. 1 is a schematic flowchart of a flexible resource aggregation feasible domain accurate modeling and approximation method provided in an embodiment of the present application.
As shown in FIG. 1, the flexible resource aggregation feasible domain accurate modeling and approximation method comprises the following steps:
in step S101, the actual type of each flexible resource on the grid demand side is detected.
Because parameters of all demand side resources are considered to be too complex during power grid dispatching and are not beneficial to protecting the privacy of power users, the demand side flexible resources need to be aggregated and then participate in power grid regulation and control. According to the method, on the premise that a unified monomer model of the flexible resources is known, an accurate aggregation feasible domain model of the large-scale flexible resources is calculated, and part of constraints are extracted from an accurate feasible domain to form a series of approximate feasible domain models. Thereby maximizing the expected revenue of the aggregator while guaranteeing the feasibility of power distribution. Therefore, before constructing the single feasible domain model of each flexible resource, the embodiment of the present application may first detect the actual type of each flexible resource on the demand side of the power grid.
In step S102, a monomer feasible region model of each flexible resource is constructed according to the actual type, and all the monomer feasible region models are aggregated to generate an aggregated feasible region model.
According to the embodiment of the application, the accurate aggregation feasible domain model of the large-scale flexible resources can be calculated according to the unified monomer models of different types of flexible resources, and the accuracy of the accurate aggregation feasible domain is as follows: any power curve within the precise feasible region can be necessarily allocated as the power curve of each flexible resource which meets the monomer power energy boundary of each flexible resource; any power curve that is not within the exact polymerization feasible region must not be successfully distributed.
In one embodiment of the present application, constructing a single feasible domain model for each flexible resource according to actual type includes: dividing a preset time window into a plurality of time periods according to a preset time interval; calculating power and energy boundary information of each time period according to the actual type of each flexible resource; and constructing a single feasible domain model according to the power and energy boundary information of each time segment.
Specifically, the method for constructing the single feasible domain model of each flexible resource according to the actual type includes the following steps:
1) The time is discretized and the time window considered for optimization is divided into a total of T time segments at time intervals.
2) Computing power and energy boundary information for each flexible resource, the flexible resourceThe lower power bound, the upper power bound, the lower energy bound and the upper energy bound of i in the t-th period are respectively ip(t),
Figure BDA0003771622130000061
ie(t),
Figure BDA0003771622130000062
For different types of flexible resources, the boundary calculation methods are different, and the specific method is as follows:
for the electric automobile, the arrival time, the departure time, the initial electric quantity, the expected electric quantity, the maximum capacity of the battery and the rated charge-discharge power of the charging pile of a single electric automobile are input. The power boundary represents a power range of the electric automobile allowed to be charged in each time interval, if the condition that the electric automobile discharges to a power grid is not considered, if the electric automobile is connected to the charging pile, the power range in the time interval is 0 to rated power, if the electric automobile is not connected to the charging pile, the power can only be 0, and when the electric automobile discharges to the power grid is considered, the lower power boundary is negative rated discharge power when the electric automobile is connected to the charging pile, and the rest time is 0. The energy boundary represents a possible charging schedule for an electric vehicle, the basic principle of which is that the charging process is to reach the desired charge before leaving without exceeding the battery capacity.
For the energy storage device, the battery capacity, the initial electric quantity, the rated charging power and the rated discharging power are input. The upper and lower power boundaries are respectively the rated charging power and the rated discharging power, the energy upper boundary corresponds to the energy storage equipment, the energy storage equipment starts to be charged to full with the rated charging power at the initial moment, and then the energy upper boundary is kept unchanged until the energy track of the initial electric quantity is recovered by discharging at the last moment; the energy lower bound corresponds to the energy storage equipment which starts to discharge with rated discharge power at the initial moment until the electric quantity is empty, and then the energy lower bound is maintained until the last moment, the charging is recovered to the energy track of the initial electric quantity.
And inputting rated heating or cooling power, an allowable temperature range, an environment temperature prediction curve and initial internal temperature for the thermal control load. The upper and lower power limits are rated heating or cooling power respectively. In the case of refrigeration, the upper energy bound is the energy consumption trajectory for maintaining the temperature at the lowest allowable temperature, and the lower energy bound is the energy consumption trajectory for maintaining the temperature at the highest allowable temperature. The upper and lower energy bounds for the heating case are opposite to the definition for the cooling case.
The single feasible domain of all the flexible resources on the demand side can be uniformly described as:
Figure BDA0003771622130000063
wherein e isi(t) is a decision variable representing the cumulative energy consumed by the flexible resource i over a period of t;
Figure BDA0003771622130000064
representing the set of all flexible resources.
In one embodiment of the present application, polymerizing all of the monomer feasible domain models results in a polymerization feasible domain model, comprising: polymerizing the accumulated consumed energy or power of all monomer feasible region models to obtain a decision variable; generating a complete binary tree according to the energy consumed by each time period, and taking all path sets of the complete binary tree as constraint parameter sets; and constructing an aggregation feasible domain model according to the decision variables and the constraint parameter set.
Specifically, the decision variable in the monomer feasible region model is the total accumulated energy consumed E (t) of the flexible resource aggregates, which satisfies
Figure BDA0003771622130000065
E (0) =0; or aggregate total power P (T), satisfying P (T) = (E (T) -E (T-1))/Δ T, the flexible resource precise aggregation feasible region can be succinctly expressed as:
Figure BDA0003771622130000066
wherein E represents a column vector formed by a variable E (T) from T to 1,
Figure BDA0003771622130000071
all ways of a complete binary tree representing a height of T +1The set of paths is then set up,
Figure BDA0003771622130000072
vla vector representing the data composition in all nodes on the path l, (·)TRepresenting a vector transpose; lφand
Figure BDA0003771622130000073
each represents a boundary value corresponding to l. The definitions of which are explained in detail below.
The definition of the data in the complete binary tree is as follows:
(1) The data in the root node is 0;
(2) The data in the right child of a node is 1 or-1, and the data in the left child is 0;
(3) When a node is the right child of its parent and the data in all its ancestors is 0, the data of the node is 1;
(4) When a node is the right child of its parent and it has an ancestor that is not 0, the data for that node has a sign opposite to the sign of its nearest non-zero ancestor.
According to the above definition, the vectors v corresponding to all paths l in the embodiment of the present applicationlAs shown in fig. 2.
The embodiment of the application discloses a T-dimensional column vector ulTo satisfy
Figure BDA0003771622130000074
Then lφAnd
Figure BDA0003771622130000075
can be defined by the following equation:
Figure BDA0003771622130000076
Figure BDA0003771622130000077
wherein the operator<x,y>[a:b]Representing the inner product of vectors x and y from the a-th component to the b-th component; slAnd rlThe index of the first and last non-zero element on l respectively,
Figure BDA00037716221300000710
ipand
Figure BDA0003771622130000078
are respectively defined as:
Figure BDA0003771622130000079
according to ulIn the above polymerization feasible domain
Figure BDA0003771622130000081
Is equivalent to
Figure BDA0003771622130000082
The expression of the feasible field can also be written as:
Figure BDA0003771622130000083
ulmay be either 0 or 1. The meaning of the above-mentioned exact aggregate feasible region formula is: the sum of the energy consumed over each subset of the set of all time periods is limited by an upper and lower bound, which correspond to the fastest and slowest energy consumption trajectories, respectively, over the subset of time periods.
In step S103, at least one constraint parameter satisfying a preset condition in the constraint parameters of the aggregation feasible domain model is screened, an approximate feasible domain model of a preset type is constructed based on the at least one constraint parameter, an actual allocation proportion of each flexible resource is determined by using the approximate feasible domain model, and each flexible resource is scheduled according to the actual allocation proportion.
The preset type of approximate feasible region model comprises any one of a k-order approximate feasible region model, a long-time period approximate feasible region model and a time period grouping approximate feasible region model.
It will be appreciated that since the binary tree has a total of 2TA bar path, except that the leftmost path makes vl＝ul=0, resulting in a constraint ineffectiveness, all the remaining paths correspond to a valid pair of upper and lower bounds constraints, so the precision aggregate feasible field contains a total of 2 (2)T-1) a constraint. When T is large (for example, T =24 or 96), the number of constraints is too large, and it is difficult to apply the constraints in scheduling, so an approximate feasible domain model is needed to reduce the number of constraints, thereby reducing the computational complexity, an actual allocation proportion of each flexible resource is determined by using the approximate feasible domain model, and each flexible resource is scheduled according to the actual allocation proportion, so that the expected profit of an aggregator can be maximized while the feasibility of power allocation is guaranteed as much as possible.
It should be noted that the basic idea of aggregating the number of constraints in the feasible domain model and all the approximate models is to extract paths satisfying specific conditions from the binary tree of the exact feasible domain, instead of considering all the paths, so that the number of constraints is reduced at the expense of accuracy.
In an embodiment of the application, when the approximate feasible region model of the preset type is an approximate feasible region model of order k, constructing the approximate feasible region model of the preset type based on at least one constraint parameter includes: selecting a constraint branch in at least one constraint parameter according to the actual order of the k-order approximate feasible domain model; constructing an approximate feasible domain model corresponding to the actual order based on the constraint branch, wherein the k-th order approximate model is
Figure BDA0003771622130000084
Wherein E represents a column vector formed by variables E (T) from T to 1,
Figure BDA0003771622130000085
representing the set of all paths of a complete binary tree of height T +1,
Figure BDA0003771622130000086
vla vector representing the composition of data in all nodes on path l (.)TRepresenting a vector transpose; phi is a unit oflAnd
Figure BDA0003771622130000087
each represents a boundary value corresponding to l.
It is understood that the k-th order approximation of the embodiment of the present application selects the constraint to be considered according to the number of non-zero elements on the branches, and specifically, those branches selected by the k-th order approximation model are
Figure BDA0003771622130000091
Therefore, the kth order approximation model can be written as:
Figure BDA0003771622130000092
wherein, when k =1, the constraint may be specifically written as:
Figure BDA0003771622130000093
i.e. a first order approximation contains only the boundary of the total energy, and this boundary is exactly equal to the sum of the energy boundaries of all flexible resources.
When k =2, the above constraint may be specifically written as
Figure BDA0003771622130000094
Figure BDA0003771622130000095
I.e. the second order approximation contains the boundary of the amount of change of the total energy between any two time periods, while the upper and lower boundaries correspond to the fastest and slowest energy change trajectories between these two time periods, respectively.
In an embodiment of the present application, when the preset type of approximate feasible region model is an approximate feasible region model with a long and short time period, constructing the preset type of approximate feasible region model based on at least one constraint parameter includes: dividing time periods according to different time intervals, wherein the minimum time period length is equal to a preset time interval; and constructing an approximate feasible domain model of the long and short time periods according to the constraint parameters in each time period.
Specifically, the preset time interval may be Δ T, and the embodiment of the present application divides the time segments according to different time intervals, where a minimum time segment is equal to Δ T, and on this basis, time segments of medium length and large length may also be added. When the total time window length is fixed, the longer the time period length, the fewer the number of divided time periods, and thus the more accurate the aggregate feasible region model can be used to establish the constraint.
For example, for 24 hours, 96 time periods can be obtained at intervals of 15min, and the variables of the time periods are constrained by a second-order approximate feasible domain model; 12 time segments can be divided at 2 hour intervals, and the variables of these segments are constrained using a precision feasible domain model. And combining the constraints in the feasible region models used under different partitions to obtain the constraints in the long and short time period approximation, thereby constructing the long and short time period approximation feasible region model.
In one embodiment of the application, when the approximate feasible domain model of the preset type is the time period grouping approximate feasible domain model, constructing the approximate feasible domain model of the preset type based on at least one constraint parameter includes: dividing a preset time window into a plurality of time periods according to a preset time interval; and dividing the time periods into a plurality of time period groups, and constructing a time period grouping approximate feasible domain model according to the constraint parameters in each time period group.
Specifically, the time slots are divided into a plurality of groups, and each group contains fewer time slots, so that more accurate constraint can be used. For example, 96 periods divided by 24 hours are divided into 12 groups: 1-8,8-16, \8230, 88-96, constraints can be built using the exact feasible domain model for each group, and the number of constraints is not too large. The last period of the previous group should be the same as the first period of the next group in order to ensure coupling between the groups. On the basis, the hierarchy can be increased continuously, for example, the 24 hours are divided into 96 periods and 6 groups (each group comprises 16 periods), and each group establishes constraint by using a second-order feasible region model; and dividing the 24 hours into 12 time intervals, dividing the time intervals into 1 group, and establishing constraint by using an accurate feasible domain model.
According to the flexible resource aggregation feasible region accurate modeling and approximation method provided by the embodiment of the application, a monomer feasible region model of each flexible resource is constructed according to the actual type of each flexible resource on the power grid demand side, all the monomer feasible region models are aggregated to generate an aggregation feasible region model, part of constraints are extracted from the aggregation feasible region model to form a series of approximate feasible region models, the actual distribution proportion of each flexible resource is determined by using the approximate feasible region models, each flexible resource is scheduled according to the actual distribution proportion, and the operating cost or the profit of a flexible resource aggregator is minimized or maximized while the power distribution feasibility is guaranteed as much as possible. Therefore, the problems that the consideration of the aggregation feasible region in the related technology is simple, the smooth proceeding of flexible resource allocation cannot be ensured, the calculation complexity is high and the like are solved.
Next, a flexible resource aggregation feasible domain accurate modeling and approximation apparatus proposed according to an embodiment of the present application is described with reference to the drawings.
FIG. 3 is a block diagram illustrating an apparatus for precise modeling and approximation of a flexible resource aggregation feasible domain according to an embodiment of the present application.
As shown in fig. 3, the flexible resource aggregation feasible domain accurate modeling and approximating apparatus 10 includes: a detection module 100, a generation module 200 and a scheduling module 300.
The detection module 100 is configured to detect an actual type of each flexible resource on a demand side of a power grid; the generating module 200 is configured to construct a monomer feasible region model of each flexible resource according to an actual type, and aggregate all the monomer feasible region models to generate an aggregate feasible region model; the scheduling module 300 is configured to screen at least one constraint parameter that meets a preset condition from among the constraint parameters of the aggregation feasible domain model, construct an approximate feasible domain model of a preset type based on the at least one constraint parameter, determine an actual allocation proportion of each flexible resource by using the approximate feasible domain model, and schedule each flexible resource according to the actual allocation proportion.
Optionally, in an embodiment of the present application, the generating module 200 is further configured to divide a preset time window into a plurality of time periods according to a preset time interval, calculate power and energy boundary information of each time period according to each actual type of the flexible resource, and construct a single feasible domain model according to the power and energy boundary information of each time period.
Optionally, in an embodiment of the present application, the generating module 200 is further configured to aggregate accumulated consumed energy or power of all monomer feasible region models to obtain a decision variable, generate a complete binary tree according to energy consumed in each time period, use all path sets of the complete binary tree as constraint parameter sets, and construct an aggregated feasible region model according to the decision variable and the constraint parameter sets.
Optionally, in an embodiment of the present application, the preset type of approximate feasible region model includes any one of a k-order approximate feasible region model, a long-and-short time period approximate feasible region model, and a time period grouping approximate feasible region model.
Optionally, in an embodiment of the present application, when the preset type of approximate feasible region model is a k-order approximate feasible region model, the scheduling module 300 is further configured to select a constraint branch in at least one constraint parameter according to an actual order of the k-order approximate feasible region model, and construct an approximate feasible region model corresponding to the actual order based on the constraint branch, where the k-th order approximate model is
Figure BDA0003771622130000111
Wherein E represents a column vector formed by variables E (T) from T to 1,
Figure BDA0003771622130000112
representing the set of all paths of a complete binary tree of height T +1,
Figure BDA0003771622130000113
vla vector representing the composition of data in all nodes on path l (.)TRepresenting a vector transpose; lφand
Figure BDA0003771622130000114
each represents a boundary value corresponding to l.
Optionally, in an embodiment of the present application, when the preset type of approximate feasible region model is an approximate feasible region model with a long time period and a short time period, the scheduling module 300 is further configured to divide the time periods according to different time intervals, where a minimum time period is equal to the preset time interval, and construct the approximate feasible region model with a long time period and a short time period according to a constraint parameter in each time period.
Optionally, in an embodiment of the present application, when the preset type of approximate feasible region model is a time segment grouping approximate feasible region model, the scheduling module 300 is further configured to divide a preset time window into a plurality of time segments according to a preset time interval, divide the plurality of time segments into a plurality of time segment groups, and construct a time segment grouping approximate feasible region model according to a constraint parameter in each time segment group.
It should be noted that the explanation of the foregoing flexible resource aggregation feasible region accurate modeling and approximation method embodiment is also applicable to the flexible resource aggregation feasible region accurate modeling and approximation apparatus of this embodiment, and details are not repeated here.
According to the flexible resource aggregation feasible region accurate modeling and approximation device provided by the embodiment of the application, a monomer feasible region model of each flexible resource is constructed according to the actual type of each flexible resource on the power grid demand side, all the monomer feasible region models are aggregated to generate an aggregation feasible region model, part of constraints are extracted from the aggregation feasible region model to form a series of approximate feasible region models, the actual distribution proportion of each flexible resource is determined by using the approximate feasible region models, each flexible resource is scheduled according to the actual distribution proportion, and the operating cost or the profit of a flexible resource aggregator is minimized or maximized while the power distribution feasibility is guaranteed as much as possible. Therefore, the problems that the consideration of the aggregation feasible region in the related technology is simple, the smooth proceeding of flexible resource allocation cannot be ensured, the calculation complexity is high and the like are solved.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 401, processor 402, and computer programs stored on memory 401 and executable on processor 402.
The processor 402, when executing a program, implements the flexible resource aggregation feasible domain accurate modeling and approximation methods provided in the embodiments described above.
Further, the electronic device further includes:
a communication interface 403 for communication between the memory 401 and the processor 402.
A memory 401 for storing computer programs executable on the processor 402.
The Memory 401 may include a high-speed RAM (Random Access Memory) Memory, and may also include a non-volatile Memory, such as at least one disk Memory.
If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 may complete mutual communication through an internal interface.
Processor 402 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the above flexible resource aggregation feasible domain accurate modeling and approximation method.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a programmable gate array, a field programmable gate array, or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.

Claims (10)

1. A flexible resource aggregation feasible domain accurate modeling and approximation method is characterized by comprising the following steps:
detecting the actual type of each flexible resource on the demand side of the power grid;
constructing a monomer feasible region model of each flexible resource according to the actual type, and polymerizing all the monomer feasible region models to generate a polymerization feasible region model;
screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregated feasible region model, constructing an approximate feasible region model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible region model, and scheduling each flexible resource according to the actual distribution proportion.
2. The method of claim 1, wherein the building the single feasible domain model for each flexible resource according to the actual type comprises:
dividing a preset time window into a plurality of time periods according to a preset time interval;
calculating power and energy boundary information of each time period according to the actual type of each flexible resource;
and constructing the single feasible domain model according to the power and energy boundary information of each time segment.
3. The method of claim 2, wherein the polymerizing the all-monomer feasible domain model to obtain the polymerization feasible domain model comprises:
polymerizing the accumulated consumed energy or power of all monomer feasible region models to obtain a decision variable;
generating a complete binary tree according to the energy consumed by each time period, and taking all path sets of the complete binary tree as constraint parameter sets;
and constructing the aggregation feasible region model according to the decision variables and the constraint parameter set.
4. The method according to claim 1, wherein the preset type of approximate feasible region model comprises any one of a k-order approximate feasible region model, a long-time period approximate feasible region model and a time period grouping approximate feasible region model.
5. The method according to claim 4, wherein when the approximate feasible region model of the preset type is the approximate feasible region model of order k, the constructing the approximate feasible region model of the preset type based on the at least one constraint parameter comprises:
selecting a constraint branch in the at least one constraint parameter according to the actual order of the k-order approximate feasible domain model;
establishing an approximate feasible domain model corresponding to the actual order based on the constraint branch, wherein the k-th order approximate model is
Figure FDA0003771622120000011
Wherein E represents a column vector formed by variables E (T) from T to 1,
Figure FDA0003771622120000012
Figure FDA0003771622120000013
representing the set of all paths of a complete binary tree of height T +1,
Figure FDA0003771622120000014
vla vector representing the data composition in all nodes on the path l, (·)TRepresenting a vector transpose; phi is alAnd
Figure FDA0003771622120000015
each represents a boundary value corresponding to l.
6. The method according to claim 4, wherein when the approximate feasible region model of the preset type is the approximate feasible region model with the long and short time periods, the constructing the approximate feasible region model of the preset type based on the at least one constraint parameter comprises:
dividing time periods according to different time intervals, wherein the length of the minimum time period is equal to the preset time interval;
and constructing the approximate feasible domain model of the long and short time periods according to the constraint parameters in each time period.
7. The method according to claim 4, wherein when the approximate feasible region model of the preset type is the approximate feasible region model of the time period grouping, the constructing the approximate feasible region model of the preset type based on the at least one constraint parameter comprises:
dividing a preset time window into a plurality of time periods according to a preset time interval;
and dividing the time periods into a plurality of time period groups, and constructing the time period grouping approximate feasible domain model according to the constraint parameters in each time period group.
8. An apparatus for flexible resource aggregation feasible domain accurate modeling and approximation, comprising:
the detection module is used for detecting the actual type of each flexible resource on the demand side of the power grid;
the generating module is used for constructing the monomer feasible region model of each flexible resource according to the actual type and polymerizing all the monomer feasible region models to generate a polymerization feasible region model;
and the scheduling module is used for screening at least one constraint parameter meeting preset conditions in the constraint parameters of the aggregated feasible region model, constructing an approximate feasible region model of a preset type based on the at least one constraint parameter, determining the actual distribution proportion of each flexible resource by using the approximate feasible region model, and scheduling each flexible resource according to the actual distribution proportion.
9. 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 the flexible resource aggregation feasible domain accurate modeling and approximation method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program, characterized in that the program is executable by a processor for implementing the flexible resource aggregation feasible domain exact modeling and approximation method according to any of claims 1-7.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611669A (en) * 2023-07-17 2023-08-18 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611669A (en) * 2023-07-17 2023-08-18 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains
CN116611669B (en) * 2023-07-17 2023-09-19 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains

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