CN114676921A - Method for calculating wind power receptibility of system by considering source load storage coordination optimization - Google Patents

Method for calculating wind power receptibility of system by considering source load storage coordination optimization Download PDF

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CN114676921A
CN114676921A CN202210328202.3A CN202210328202A CN114676921A CN 114676921 A CN114676921 A CN 114676921A CN 202210328202 A CN202210328202 A CN 202210328202A CN 114676921 A CN114676921 A CN 114676921A
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程瑜
陈熙
朱瑾
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Abstract

The invention discloses a method for calculating wind power receptibility of a system by considering source load storage coordination optimization, which comprises the following steps of: s1: collecting historical wind power and load data of a system, and predicting wind power and load output on a scheduling day; s2: considering source load storage coordination interaction, and determining a day-ahead scheduling strategy by taking the minimum system operation cost as a target; s3: calculating a wind power dispatchable domain through a self-adaptive constraint generation algorithm based on a current day-ahead dispatching strategy and a robust economic dispatching theory; s4: constructing a three-dimensional domain interval formed by integrating energy storage, flexible load and all feasible state points capable of receiving three-dimensional variables of the wind power fluctuation range; s5: and analyzing the relation between the energy storage configuration and the wind power accepting capacity of the system through the visual three-dimensional domain interval. According to the method, the cut plane constraint is generated, the possible wind power fluctuation interval is continuously reduced, the maximum wind power uncertain set which can be absorbed by the power grid is calculated, and the problem of flexibility evaluation of the power grid in response to new energy fluctuation in a real-time scheduling stage is solved.

Description

Method for calculating wind power receptibility of system by considering source load storage coordination optimization
Technical Field
The invention relates to the technical field of power system flexibility evaluation, in particular to a method for calculating wind power receptibility of a system by considering source load storage coordination optimization.
Background
Under the background of a 30.60 double-carbon target, in order to realize clean and efficient transformation of a power system, new energy with the characteristics of randomness, volatility and dispersibility is connected to a power grid in a large scale, renewable energy becomes an important support for power supply in China, and the power grid will present obvious bilateral randomness in the future. With the rapid development of new theory, new technology and new material, the power supply, the power grid and the load all have flexible characteristics, and the form, the response range and the interaction mode among the source, the power grid and the load are more complex than those of the current power grid. The energy storage system can realize the time-space migration of energy, has the capability of quick charge and discharge, can dynamically absorb the energy and timely release the energy, and is considered as an effective means for improving the access scale of the renewable energy sources.
The meaning of the economic optimization scheduling of the power system is that the power limit is distributed to different units, the state of balance of power demand and power supply is achieved, and in the distribution process, the economic requirement is achieved on the basis of comprehensively considering safe and reliable power supply. The source network load storage optimization scheduling calculation mostly adopts day-ahead optimization calculation, only the economy of day-ahead economic scheduling is considered when energy storage is configured, and the fluctuation and uncertainty of loads and wind-light power in a day-ahead range are ignored, so that the configured energy storage participates in the scheduling strategy after day-ahead economic scheduling, and the uncertainty of wind power fluctuation and the like of day-ahead real-time scheduling cannot be fully responded.
Disclosure of Invention
In view of the above, the present invention provides a method for calculating wind power receptibility of a system in consideration of source-load-storage coordination optimization, which solves the problem of flexibility evaluation that a power grid can cope with renewable energy fluctuation in a real-time scheduling stage, in view of the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for calculating wind power admissible capacity of a system in consideration of source load storage coordination optimization comprises the following steps:
s1: collecting historical data of renewable energy sources and loads of a system, and predicting the output and the loads of the renewable energy sources on a scheduling day to obtain a predicted output-load curve;
s2: considering source load storage coordination interaction, and determining a day-ahead scheduling strategy of the system by taking the minimum system operation cost as a target;
s3: calculating a wind power dispatchable domain through a self-adaptive constraint generation algorithm based on a determined day-ahead dispatching strategy and a robust economic dispatching model of the system;
s4: constructing a three-dimensional domain interval formed by integrating energy storage charging and discharging adjustment, flexible load increasing and decreasing load adjustment and all feasible state points capable of receiving three-dimensional variables of the wind power fluctuation range;
s5: and analyzing the relation between the energy storage configuration scheme and the wind power accepting capacity of the system through the visualization of the three-dimensional domain interval.
Further, in step S1, the predicted output-load curve specifically refers to wind power generation and load active output, and historical data is analyzed by a clustering algorithm to obtain an output-load curve of a typical scene.
Further, in step S2, with the objective of minimizing the system operation cost, the step of determining the day-ahead scheduling policy of the system is as follows:
1) under a typical wind power output scene, a target function with the minimum system operation cost comprises the output cost of a conventional unit, the wind abandoning cost of the wind power unit, the energy storage calling cost and the controllable load calling cost, and the specific calculation method comprises the following steps:
Figure BDA0003574243530000021
in the formula, T is the total time of the scheduling period; n is a radical ofG、Nw、Nes、NclThe number of the conventional units and the wind generation units in the system and the number of the stored energy and the load are respectively; c. C2、c1、c0Is a cost coefficient of a conventional unit; c. Cw、ces、cclRespectively adjusting cost coefficients for unit wind abandoning cost, energy storage efficiency loss cost and flexible load;
Figure BDA0003574243530000022
the discharging and charging power of the energy storage device k at the moment t; p is a radical of formulai,tThe generated power of the conventional unit i at the moment t is obtained; w is a group ofj,t、wj,tRespectively the predicted output and the actual on-line output of the wind turbine j at the moment t;
Figure BDA0003574243530000023
the response power of the flexible load q at the time t represents the load reduction amount when the flexible load is positive, and represents the load increase when the flexible load is negative.
2) The method comprises the following steps of constructing system operation constraints (including power balance constraints and line tide capacity constraints), conventional unit operation constraints (including unit climbing constraints and unit output constraints), wind power constraints, energy storage system operation constraints (including energy storage charge-discharge state constraints, charge state constraints and scheduling cycle initial and final electric capacity invariant constraints) and flexible load operation constraints (including flexible load power constraints and continuous response constraints).
a) Power balance constraint
Figure BDA0003574243530000024
In the formula (I), the compound is shown in the specification,
Figure BDA0003574243530000025
the actual force of the load q at the time t.
b) Line tidal current capacity constraint
Figure BDA0003574243530000026
In the formula: piil、πjl、πkl、πqlRespectively representing the load flow distribution factors of the conventional unit i, the fan j, the energy storage device k and the flexible load q to the line l; flThe maximum transmission power allowed for line l.
c) Unit operation constraint
Pi l≤pi,t≤Pi u
-Pi ramp≤pi,t-pi,t-1≤Pi ramp
In the formula: pi l、Pi u、Pi rampThe minimum output limit value and the maximum output limit value of the unit i and the climbing speed of the unit are respectively.
d) Wind power constraint
0≤wj,t≤Wj,t
e) Energy storage system operation constraints
ck,t+dk,t≤1
Figure BDA0003574243530000031
Figure BDA0003574243530000032
Figure BDA0003574243530000033
Figure BDA0003574243530000034
Figure BDA0003574243530000035
In the formula: c. Ck,tAnd dk,tRespectively representing the charging and discharging states of the energy storage device k at the time t, wherein 1 represents that the energy storage device k is in the charging or discharging state, and 0 represents that the energy storage device k is not in the charging or discharging state; ek,t、Ek,iniRespectively representing the storage capacity of the energy storage device k at the time t and the initial storage capacity in one day; gamma raycAnd gammadRespectively the charging and discharging efficiency of the energy storage system;Pkand
Figure BDA0003574243530000036
the rated power and the rated capacity of the energy storage device k are respectively.
f) Flexible load operating restraint
Figure BDA0003574243530000037
Figure BDA0003574243530000038
In the formula:
Figure BDA0003574243530000039
to the upper limit of the response power of the flexible load q, Δ t represents the response duration.
Further, in step S3, the specific steps of calculating the wind power dispatchable domain through the adaptive constraint generation algorithm are as follows:
1) establishing a rescheduling model added with a relaxation variable related to a day-ahead scheduling period model, wherein the specific expression is as follows:
Figure BDA0003574243530000041
in the formula: s is+And s-Respectively correspond to p in Y+、p-The constraint of the problem (26) is the conventional unit output related constraint, the flexible load operation constraint and the energy storage operation constraint which are added with the relaxation variable, and the corresponding coefficient matrix is A1、B1、D1、b1And constraint of no relaxation variable, corresponding to coefficient matrix of A2、B2、D2、b2；1TIs a vector with elements of 1, I is an identity matrix, the matrix dimension and A1The same is true.
2) Solving the problem based on a dual theory to obtain a converted mixed integer linear programming model, wherein the expression is as follows:
Figure BDA0003574243530000042
in the formula: xi is a dual variable of the linear programming problem; n is a radical ofstIs RActDimension of the medium matrix; m is a sufficiently large positive number; h and H are RActThe coefficient matrix of (2).
3) Iteratively solving the problem, generating constraints for making the objective function R (X) positive, and selecting the corresponding solution from the set RActSeparating until the objective function value is 0 to obtain a final set RActThe result is the result.
Further, in step S4, a three-dimensional domain interval formed by a set of all feasible state points of energy storage charging and discharging adjustment, flexible load increase and decrease adjustment, and receivable wind power fluctuation range three-dimensional variables is constructed, and the specific method refers to that the set R is combinedActThe inequality group establishes inequality relations among the three-dimensional variables, and a curved surface graph of the three variables is obtained through calculation and analysis, so that the visualization of a three-dimensional domain is realized;
further, in step S5, the specific steps of analyzing the relationship between the energy storage configuration scheme and the wind power receivable capability of the system are as follows:
1) selecting a series of energy storage different energy storage distribution points and capacity schemes and different storage capacity ratios, solving and constructing a three-dimensional domain interval formed by energy storage charging and discharging adjustment, flexible load increase and decrease load adjustment and all feasible state points capable of receiving wind power fluctuation range three-dimensional variables in a combined mode;
2) and analyzing the relation between the energy storage configuration scheme and the wind power capacity receivable by the system according to the result, and selecting the energy storage configuration scheme suitable for the target area according to the actual requirement to obtain the wind power capacity receivable by the corresponding system.
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FIG. 1 is a schematic overall flow diagram of the present invention.
Detailed Description
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
As shown in the figure, the invention provides a wind power dispatchable domain calculation method considering source load storage coordination optimization, which comprises the following steps:
s1: collecting historical data of renewable energy sources and loads of a system, predicting the output and the loads of the renewable energy sources on a scheduling day to obtain a predicted output-load curve, and the method comprises the following steps:
the predicted output-load curve specifically refers to wind power generation and load active output, and historical data are analyzed through a clustering algorithm to obtain an output-load curve of a typical scene.
S2: considering source load storage coordination interaction, and taking the minimum system operation cost as a target, determining a day-ahead scheduling strategy of the system, wherein the steps are as follows:
1) under a typical wind power output scene, a target function with the minimum system operation cost comprises the output cost of a conventional unit, the wind abandoning cost of the wind power unit, the energy storage calling cost and the controllable load calling cost, and the specific calculation method comprises the following steps:
Figure BDA0003574243530000051
in the formula, T is the total time of the scheduling period; n is a radical ofG、Nw、Nes、NclThe number of the conventional units and the wind turbine units in the system and the number of the stored energy and the load are respectively; c. C2、c1、c0Is a cost coefficient of a conventional unit; c. Cw、ces、cclRespectively adjusting cost coefficients for unit wind abandoning cost, energy storage efficiency loss cost and flexible load;
Figure BDA0003574243530000052
the discharging and charging power of the energy storage device k at the moment t; p is a radical ofi,tThe generated power of the conventional unit i at the moment t is obtained; wj,t、wj,tRespectively the predicted output and the actual on-line output of the wind turbine generator j at the moment t;
Figure BDA0003574243530000053
the response power of the flexible load q at the time t represents the load reduction amount when the flexible load is positive, and represents the load increase when the flexible load is negative.
2) The method comprises the following steps of constructing system operation constraints (including power balance constraints and line tide capacity constraints), conventional unit operation constraints (including unit climbing constraints and unit output constraints), wind power constraints, energy storage system operation constraints (including energy storage charge-discharge state constraints, charge state constraints and scheduling cycle initial and final electric capacity invariant constraints) and flexible load operation constraints (including flexible load power constraints and continuous response constraints).
a) Power balance constraint
Figure BDA0003574243530000061
In the formula (I), the compound is shown in the specification,
Figure BDA0003574243530000062
the actual force of the load q at the time t.
b) Line tidal current capacity constraint
Figure BDA0003574243530000063
In the formula: piil、πjl、πkl、πqlRespectively representing the load flow distribution factors of the conventional unit i, the fan j, the energy storage device k and the flexible load q to the line l; flThe maximum transmission power allowed for line i.
c) Unit operation constraint
Pi l≤pi,t≤Pi u
-Pi ramp≤pi,t-pi,t-1≤Pi ramp
In the formula: pi l、Pi u、Pi rampThe minimum output limit value and the maximum output limit value of the unit i and the climbing speed of the unit are respectively.
d) Wind power constraint
0≤wj,t≤Wj,t
e) Energy storage system operation constraints
ck,t+dk,t≤1
Figure BDA0003574243530000064
Figure BDA0003574243530000065
Figure BDA0003574243530000066
Figure BDA0003574243530000067
Figure BDA0003574243530000071
In the formula:ck,tand dk,tRespectively representing the charging and discharging states of the energy storage device k at the time t, wherein 1 represents that the energy storage device k is in the charging or discharging state, and 0 represents that the energy storage device k is not in the charging or discharging state; ek,t、Ek,iniThe storage capacity of the energy storage device k at the moment t and the initial storage capacity in one day are respectively; gamma raycAnd gammadThe charging efficiency and the discharging efficiency of the energy storage system are respectively obtained; p iskAnd
Figure BDA0003574243530000072
the rated power and the rated capacity of the energy storage device k are respectively.
f) Flexible load operating restraint
Figure BDA0003574243530000073
Figure BDA0003574243530000074
In the formula:
Figure BDA0003574243530000075
to the upper limit of the response power of the flexible load q, Δ t represents the response duration.
S3: based on a day-ahead scheduling strategy and a robust economic scheduling model which are determined by a system, a wind power schedulable domain is calculated through a self-adaptive constraint generation algorithm, and the method comprises the following steps:
1) establishing a rescheduling model added with a relaxation variable related to a day-ahead scheduling period model, wherein the specific expression is as follows:
Figure BDA0003574243530000076
in the formula: s+And s-Respectively correspond to p in Y+、p-The constraint of the problem (26) is the conventional unit output related constraint, the flexible load operation constraint and the energy storage operation constraint which are added with the relaxation variable, and the corresponding coefficientThe matrix is A1、B1、D1、b1And constraint of no relaxation variable, corresponding to coefficient matrix of A2、B2、D2、b2；1TIs a vector with elements of 1, I is an identity matrix, and the dimension of the matrix is equal to A1The same is true.
2) Solving the problem based on a dual theory to obtain a converted mixed integer linear programming model, wherein the expression is as follows:
Figure BDA0003574243530000077
in the formula: xi is a dual variable of the linear programming problem; n is a radical ofstIs RActDimension of the medium matrix; m is a sufficiently large positive number; h and H are RActThe coefficient matrix of (2).
3) Iteratively solving the problem by generating constraints which make the objective function R (X) positive, and selecting the corresponding solution from the set RActSeparating until the objective function value is 0 to obtain a final set RActThe result is obtained.
S4: constructing a three-dimensional domain interval formed by integrating energy storage charging and discharging adjustment, flexible load increasing and decreasing load adjustment and all feasible state points capable of receiving three-dimensional variables of the wind power fluctuation range, and comprising the following steps of:
will set RActThe inequality groups establish inequality relations among three-dimensional variables, and a curved surface graph of the three variables is obtained through calculation and analysis, so that the visualization of a three-dimensional domain is realized
S5: through the visualization of the three-dimensional domain interval, the relation between the energy storage configuration scheme and the wind power accepting capacity of the system is analyzed, and the method comprises the following steps:
1) selecting a series of energy storage different energy storage distribution points and capacity schemes and different storage capacity ratios, solving and constructing a three-dimensional domain interval formed by energy storage charging and discharging adjustment, flexible load increase and decrease load adjustment and all feasible state points capable of receiving wind power fluctuation range three-dimensional variables in a combined mode;
2) and analyzing the relation between the energy storage configuration scheme and the wind power capacity receivable by the system according to the result, and selecting the energy storage configuration scheme suitable for the target area according to the actual requirement to obtain the wind power capacity receivable by the corresponding system.

Claims (6)

1. A method for calculating wind power admissible capacity of a system in consideration of source load storage coordination optimization, the method comprising the steps of:
s1: collecting historical data of renewable energy sources and loads of the system, and predicting the output and the loads of the renewable energy sources on the scheduling days to obtain a predicted output-load curve;
s2: considering source load storage coordination interaction, and determining a day-ahead scheduling strategy of the system by taking the minimum system operation cost as a target;
s3: calculating a wind power dispatchable domain through a self-adaptive constraint generation algorithm based on a determined day-ahead dispatching strategy and a robust economic dispatching model of the system;
s4: and constructing a three-dimensional domain interval formed by integrating energy storage charging and discharging adjustment, flexible load increasing and decreasing load adjustment and all feasible state points capable of receiving three-dimensional variables of the wind power fluctuation range.
2. The method for calculating the wind power admissible capability of the system in consideration of source-load storage coordination optimization according to claim 1, wherein in the step S1, the predicted output-load curve specifically refers to wind power generation and load active output, and historical data are analyzed through a clustering algorithm to obtain an output-load curve of a typical scene.
3. The method for calculating the wind power receptibility of the system based on the consideration of the source load storage coordination optimization as claimed in claim 1, wherein in the step S2, the steps are as follows:
1) under a typical wind power output scene, the objective function with the minimum system operation cost comprises the output cost of a conventional unit, the wind abandoning cost of the wind power unit, the energy storage calling cost and the controllable load calling cost;
2) the method comprises the following steps of constructing system operation constraints (including power balance constraints and line tide capacity constraints), conventional unit operation constraints (including unit climbing constraints and unit output constraints), wind power constraints, energy storage system operation constraints (including energy storage charging and discharging state constraints, charge state constraints and constant storage capacity constraints at the beginning and the end of a scheduling period) and flexible load operation constraints (including flexible load power constraints and continuous response constraints).
4. The method for calculating the wind power receptibility of the system based on the consideration of the source load storage coordination optimization as claimed in claim 1, wherein in the step S3, the steps are as follows:
1) establishing a rescheduling model added with relaxation variables related to a day-ahead scheduling period model:
2) solving the problem based on a dual theory to obtain a converted mixed integer linear programming model;
3) and (4) carrying out iterative solution on the problem, and separating the corresponding solution from the set by generating a constraint condition which enables the objective function to be positive until the objective function value is 0 to obtain a final set, namely the solution.
5. The method for calculating the wind power receptibility of the system considering the source load and storage coordination optimization as claimed in claim 1, wherein in the step S4, a three-dimensional domain interval formed by a set of all feasible state points of energy storage charge-discharge adjustment, flexible load increase-decrease load adjustment and receivable wind power fluctuation range three-dimensional variables is constructed, and the specific method is to establish an inequality relation among the three-dimensional variables in an inequality group in a set RAct, obtain a surface map of the three variables by calculation and analysis, and realize the visualization of the three-dimensional domain.
6. The method for optimizing and configuring hydrogen energy storage capacity considering long-term energy storage application scenarios according to claim 1, wherein in the step S5, the specific steps of analyzing the relationship between the energy storage configuration scheme and the wind power capacity receivable by the system are as follows:
1) selecting a series of energy storage different energy storage distribution points and capacity schemes and different storage capacity ratios, solving and constructing a three-dimensional domain interval formed by energy storage charging and discharging adjustment, flexible load increase and decrease load adjustment and all feasible state points capable of receiving wind power fluctuation range three-dimensional variables in a combined mode;
2) and analyzing the relation between the energy storage configuration scheme and the wind power receivable capability of the system according to the result, and selecting the energy storage configuration scheme suitable for the target area according to the actual requirement to obtain the wind power receivable capability of the corresponding system.
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CN117195605A (en) * 2023-11-08 2023-12-08 山东理工大学 Electric power system bilinear model relaxation solving method based on linear interpolation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117195605A (en) * 2023-11-08 2023-12-08 山东理工大学 Electric power system bilinear model relaxation solving method based on linear interpolation
CN117195605B (en) * 2023-11-08 2024-01-26 山东理工大学 Electric power system bilinear model relaxation solving method based on linear interpolation

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