CN115411777A - Power distribution network flexibility evaluation and resource allocation method and system - Google Patents

Power distribution network flexibility evaluation and resource allocation method and system Download PDF

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CN115411777A
CN115411777A CN202211221233.5A CN202211221233A CN115411777A CN 115411777 A CN115411777 A CN 115411777A CN 202211221233 A CN202211221233 A CN 202211221233A CN 115411777 A CN115411777 A CN 115411777A
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flexibility
distribution network
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power distribution
power
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杨新婷
叶圣永
魏俊
李婷
李达
刘立扬
龙川
刘旭娜
柏昊阳
王子峣
潘一凡
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State Grid Sichuan Economic Research Institute
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Abstract

The invention discloses a method and a system for flexibility evaluation and resource allocation of a power distribution network, which consider the characteristics presented under different time scales in the operation process of the power distribution network and respectively solve by adopting a specific uncertainty method to obtain corresponding operation scene sets; secondly, various types of flexibility resources such as a micro gas turbine, an energy storage device, a demand response load and the like are considered comprehensively, and respective flexibility capability models are established; and finally, designing a flexible resource optimal configuration method combining planning and operation, decoupling and information transmission of a planning layer and an operation layer by adopting a target cascade method, and realizing flexible configuration of various resources under the condition of meeting the balance of flexibility supply and demand through repeated iteration.

Description

Power distribution network flexibility evaluation and resource allocation method and system
Technical Field
The invention relates to the technical field of power distribution network resource allocation, in particular to a method and a system for flexibility evaluation and resource allocation of a power distribution network.
Background
With the aggravation of global energy crisis, climate change and environmental pollution problems, the society of all countries accelerates the transformation of propulsion power systems to cleanness and intellectualization. However, the renewable distributed energy represented by wind turbines and photovoltaic systems has intermittency, randomness and volatility, and the renewable distributed energy is superposed with terminal loads after grid connection, so that the net loads present larger climbing amplitude at adjacent moments. The uncertainty of the operation of the power distribution network is aggravated by the superposition of net load fluctuation, renewable energy output prediction errors and load prediction errors, and a severe test is provided for the flexible regulation capacity of the power distribution network. The flexibility of the power system refers to the capability of quickly responding to the random fluctuation of power on the power generation side or the load side by scheduling various response resources (such as a conventional generator set with deep peak shaving capability) under the constraints of economy and operating conditions. The flexible adjusting capacity of the traditional power distribution network is provided by an upper main network through a substation node, and is difficult to respond timely and effectively to net load fluctuation of nodes far away from the electrical distance under the influence of node voltage and grid transmission capacity. Therefore, research needs to be carried out on a multi-type flexible resource optimization configuration method in the power distribution network.
At present, research aiming at the flexibility resource allocation of the power distribution network can be divided into energy storage allocation and demand side resource allocation according to an allocation main body, and can be divided into configuration based on a scene and configuration based on robustness according to an uncertain time scale. A distribution network flexibility local constraint and overall calculation model is established by the Luguo micro-computer, and an energy storage configuration method considering flexibility resource interaction is provided; 5363 a scholars such as Li Zhihao quantize the improvement effect of demand response configuration on the flexibility of the power distribution network based on a multi-stage elasticity evaluation model of mechanical mapping; ren Zhijun and other scholars establish a double-layer model combining planning and operation, introduce the insufficient flexibility rate as an evaluation index in an operation layer, introduce a comprehensive safety index in a planning layer to evaluate the system safety, and construct a configuration model of a micro gas turbine, energy storage equipment and demand response load aiming at a single scene; 5363 and the like, students such as Zhu Xiaorong and the like fully consider the multi-time scale characteristic of the flexibility pair of the power distribution network, establish a power distribution network uncertainty operation scene set by adopting different processing methods aiming at long-time scales and short-time scales, and research on an energy storage optimization configuration method of the power distribution network on the basis.
Most of the existing researches are configured for a specific flexible resource, the complementary characteristics of multiple types of resources are ignored, and when multiple types of flexible resources are configured in a coordinated manner, uncertainty of a power distribution network under a single time scale is often considered for simplifying a model or solving a process, so that the power supply reliability of a configuration scheme is low or the resource redundancy is overhigh.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing power distribution network flexibility resource allocation method is limited to uncertainty of a single main body or a single time scale, so that the power supply reliability of an allocation scheme is low or the resource redundancy is high. The method and the system for evaluating the flexibility and allocating resources of the power distribution network are provided, the characteristics presented under different time scales in the operation process of the power distribution network are considered, and a specific uncertainty method is adopted to respectively solve and obtain corresponding operation scene sets; secondly, various types of flexibility resources such as a micro gas turbine, an energy storage device, a demand response load and the like are considered comprehensively, and respective flexibility capability models are established; and finally, designing a flexible resource optimal configuration method combining planning and operation, decoupling and information transmission of a planning layer and an operation layer by adopting a target cascade method, and realizing flexible configuration of various resources under the condition of meeting the balance of flexibility supply and demand through repeated iteration.
The invention is realized by the following technical scheme:
on one hand, the invention provides a method for evaluating the flexibility and allocating resources of a power distribution network, which comprises the following steps:
acquiring historical annual day operation data of a power distribution network, and clustering original scenes of the historical annual day operation data by adopting a K-means clustering algorithm to obtain a typical operation scene set; establishing a typical combined scene set of a fan, a photovoltaic and a load curve according to the typical operation scene set; representing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set;
establishing a flexibility demand model, and establishing a flexibility regulation capacity model according to the flexibility demand model;
establishing a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model;
and carrying out distribution network flexible resource allocation according to the distribution network flexible resource allocation model.
Further, the model expression of the typical operation scene set is as follows:
Figure BDA0003878274960000021
in the formula (1), S 1 Representing the number of typical operating scenarios, n being the iterationThe number of clusters in the process; l. the n Loss indexes caused by clustering; m is a clustering number ending value; s is the number of the original scene; p s Is the power vector of the original scene s; p s Is P s The cluster center of the cluster;
the model expression of the typical combined scene set is as follows:
Figure BDA0003878274960000022
in the formula (2), the reaction mixture is,
Figure BDA0003878274960000023
and the power vectors of the fan, the photovoltaic and the load curve in the original scene s at all times are obtained.
Further, the model expression of the power distribution network operation scene set is as follows:
Figure BDA0003878274960000031
in the formula (3), Σ | a | represents information of each period in one scheduling cycle,
Figure BDA0003878274960000032
respectively representing the actual power of a fan, a photovoltaic and a load under the s-th typical scene,
Figure BDA0003878274960000033
respectively representing the predicted output of the fan, the photovoltaic and the load in the s-th typical scene,
Figure BDA0003878274960000034
Figure BDA0003878274960000035
respectively representing the worst uncertain ranges allowed by the wind light and load prediction errors in the s-th typical scene; gamma-shaped ζ The index of conservation is shown.
Further, the expression of the flexibility requirement model is as follows:
Figure BDA0003878274960000036
Figure BDA0003878274960000037
Figure BDA0003878274960000038
in the formula (4), F D (t, τ) is the flexibility requirement of the distribution network, P NL (t + T) and P NL (t) actual net load power, P, of the distribution network at times t and t + T, respectively NL (t+τ)-P NL (t) is the net load fluctuation quantity of the power distribution network at the moment from t to t + tau; equation (5) represents the upward flexibility requirement created by the distribution network; equation (6) represents the downward flexibility requirement created by the distribution network.
Further, the flexibility adjustment capability model includes: a flexibility adjustment capability model of the micro gas turbine, a flexibility adjustment capability model of the interruptible load and a flexibility adjustment capability model of the energy storage device;
the expression of the flexibility regulation capability model of the micro gas turbine is as follows:
Figure BDA0003878274960000039
in the formula (7), the reaction mixture is,
Figure BDA00038782749600000310
and
Figure BDA00038782749600000311
respectively representing the flexibility up-and down-regulation capabilities provided by the gas turbine.
Figure BDA00038782749600000312
And
Figure BDA00038782749600000313
respectively representing the maximum and minimum output, P, of the gas turbine gt,t Representing real-time output of a gas turbineThe power of the electric motor is controlled by the power controller,
Figure BDA00038782749600000314
and
Figure BDA00038782749600000315
representing the gas turbine ramp up and ramp down rates, respectively;
the expression of the flexibility regulation capability model of the micro gas turbine is as follows:
Figure BDA00038782749600000316
Figure BDA00038782749600000317
in the formula (8), P il,t And
Figure BDA00038782749600000318
respectively representing the output force and the maximum output force of the controllable load at the current moment,
Figure BDA00038782749600000319
and
Figure BDA00038782749600000320
respectively representing the maximum limits of the system for cutting off the controllable load and recovering the load;
the expression of the flexibility adjusting capacity model of the energy storage device is as follows:
Figure BDA0003878274960000041
Figure BDA0003878274960000042
in the formula (9), the reaction mixture is,
Figure BDA0003878274960000043
respectively representing the flexibility up-and down-regulation capabilities of the energy storage device.
Figure BDA0003878274960000044
Respectively, representing the real-time charging/discharging power of the energy storage device.
Figure BDA0003878274960000045
Respectively, the maximum charge/discharge power of the energy storage device. E ess,t Representing a real-time electrical quantity of the energy storage device;
Figure BDA0003878274960000046
it indicates the minimum and maximum stored charge of the energy storage device, respectively.
Further, after the flexibility adjustment capability model is established, the method comprises the following steps: establishing a flexible resource adequacy model and a branch flexible response capability model according to the flexible adjustment capability model;
the expression of the flexible resource adequacy model is as follows:
Figure BDA0003878274960000047
Figure BDA0003878274960000048
in the formula (10), I fl Representing an abundance level of distribution network flexibility resources during an operating cycle;
the expression of the branch flexibility response capability model is as follows:
Figure BDA0003878274960000049
in, L ij,t For real-time load margins of branch transmission channels, I br And the power flow balance degree of the power distribution network line in the whole operation period is represented.
Further, the objective function of the distribution network flexible resource allocation model is as follows: minF = C inv +C ope (12) In formula (12), C inv Planning investment of equal-year valued output, C ope Representing equal-year valued output operation;
Figure BDA00038782749600000410
Figure BDA00038782749600000411
Figure BDA00038782749600000412
C p,t ＝P p,t π e,t (16)，
C wt,t ＝P we,t π wt (17)，
C pv,t ＝P pv,t π pv (18)，
C il,t ＝P il,t π il (19)，
Figure BDA0003878274960000051
Figure BDA0003878274960000052
C loss,t ＝P loss,t π e,t (22)，
in formulae (13) to (22), τ a Representing the equivalent annual coefficient of the device a, r being the discount rate, y a Is the economic life of the device a. c. C gt Investment costs for micro gas turbines; c. C il 、c ess The unit investment costs of the interruptible load contract electric quantity and the energy storage configuration capacity are respectively. x is a radical of a fluorine atom gt, i、x il,i 、x ess,i Are investment decision variables of the gas turbine, the interruptible load and the energy storage equipment respectively. Ψ gt 、Ψ il 、Ψ ess And respectively configuring nodes to be selected for the gas turbine, the interruptible load and the energy storage equipment. P p,t Purchasing electric quantity from a superior power grid for the power distribution network; p wt,t 、P pv,t Actual output of wind and solar power generation; p loss,t Is the network tidal current transmission loss. Pi e,t 、π g,t Real-time electricity prices and gas prices, respectively; pi wt 、π pv The unit cost of wind and light power generation is saved; pi il For interruptible loadsCalling cost;
Figure BDA0003878274960000053
the unit cost of charging/discharging the energy storage device is respectively. Eta gt Generating power efficiency for the gas turbine; q lhv Is the heat value of natural gas.
Further, the constraint conditions of the distribution network flexible resource allocation model include: the method comprises the following steps of power distribution network safe operation constraint, wind-solar output constraint, gas turbine constraint, energy storage device constraint, interruptible load constraint, reactive power compensation device constraint and flexibility constraint.
The expression of the power distribution network safe operation constraint is as follows:
Figure BDA0003878274960000054
Figure BDA0003878274960000055
Figure BDA0003878274960000056
Figure BDA0003878274960000057
Figure BDA0003878274960000058
Figure BDA0003878274960000059
in the formulas (23) to (28), pi (j) and delta (j) respectively represent the first node and the last node set of the branch connected with the node j; b represents a node set; r is ij And x ij Respectively representing the resistance and reactance of branch ij; p ij,t And Q ij,t To representBranch power flow of t time period; v j,t Is the node voltage; q cb,j,t Representing the reactive power provided by the switchable capacitors. tan theta ij A power factor that is an interruptible load;
the expression of the wind-solar output constraint is as follows:
Figure BDA0003878274960000061
Figure BDA0003878274960000062
in the formulae (29) and (30),
Figure BDA0003878274960000063
and
Figure BDA0003878274960000064
respectively the upper limit of the output of the fan and the upper limit of the output of the photovoltaic unit;
the expression for the gas turbine constraint is:
Figure BDA0003878274960000065
Figure BDA0003878274960000066
Figure BDA0003878274960000067
in the formulae (31) to (33),
Figure BDA0003878274960000068
and
Figure BDA0003878274960000069
respectively representing the upward climbing rate and the downward climbing rate of the gas turbine;
the energy storage device constraint expression is:
Figure BDA00038782749600000610
E ess,T ＝E ess,0 (35)，
Figure BDA00038782749600000611
Figure BDA00038782749600000612
Figure BDA00038782749600000613
Figure BDA00038782749600000614
equation (34) is a charge-discharge model of the energy storage device; equation (35) indicates that the state of charge of the stored energy remains unchanged before and after a scheduling period; equations (36), (37), and (38) represent a charge power constraint, a discharge power constraint, and a charge quantity constraint, respectively; in formulae (34) to (39), n loss The self-loss rate of the energy storage device is represented, and the inherent electric quantity loss of the stored energy is described;
Figure BDA00038782749600000615
and
Figure BDA00038782749600000616
respectively representing the charging efficiency and the discharging efficiency of the energy storage device. E ess,0 Indicating the initial state of charge of the energy storage device, E ess,T Indicating the state of charge of the energy storage device after the end of the scheduling period. c. C ess,t The charge and discharge identification bit of the energy storage device is represented as a 0-1 type variable; x is the number of ess Decision-making variables indicating whether or not energy storage devices are configuredAmount, being an integer variable;
the interruptible load constraint is expressed as:
Figure BDA0003878274960000071
the expression for the reactive compensation device constraint is:
Figure BDA0003878274960000072
n cb,j,t ≤N cb,j (42)，
Figure BDA0003878274960000073
in formulae (41) to (43), n cb,j,t The number of groups of capacitors are switched on for each group,
Figure BDA0003878274960000074
reactive compensation power provided for each group of capacitors; n is a radical of cb,j The upper limit of the number of groups of capacitors which can be put into operation;
Figure BDA0003878274960000075
the upper limit of the switchable operation times of the capacitor in one operation period is set;
the expression of the flexibility constraint is:
Figure BDA0003878274960000076
Figure BDA0003878274960000077
further, the power distribution network safe operation constraints include: node voltage constraints and line flow constraints.
In another aspect, the present invention provides a system for flexibility evaluation and resource allocation of a power distribution network, including:
the first scene set building module is used for clustering original scenes of the historical annual operating data by adopting a K-means clustering algorithm to obtain a typical combined scene set of a fan, a photovoltaic and a load curve;
the second scene set construction module is used for representing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set;
the first model building module is used for building a flexibility demand model;
the second model building module is used for building a flexibility adjusting capacity model according to the flexibility demand model and building a flexibility evaluation index;
the third model building module is used for building a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model;
and the resource allocation module is used for allocating the flexible resources of the power distribution network according to the flexible resource allocation model of the power distribution network.
Compared with the prior art, the invention has the following advantages and beneficial effects: the characteristics and the multi-type flexible resources presented under different time scales in the operation process of the power distribution network are considered, the configuration of the multi-type resources is realized under the condition of meeting the balance of flexibility supply and demand by establishing the power distribution network operation scene set, the flexible adjusting capacity model and the power distribution network flexible resource configuration model, and the defect that the current power distribution network flexible resource configuration method is limited to a single main body or single time scale uncertainty is overcome. In addition, flexibility of the power distribution network is evaluated by using flexibility resource adequacy and branch flexibility response capability, and flexibility attack is guaranteed to be always in an adequate level relative to requirements in the operation process.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of a robust optimization uncertain set in different forms provided in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a power distribution network uncertainty scene calculation process provided in embodiment 1 of the present invention;
fig. 3 is a schematic diagram of an uncertain operation scene of a power distribution network according to embodiment 2 of the present invention;
fig. 4 is a schematic diagram of an IEEE33 node test system according to embodiment 2 of the present invention;
FIG. 5 is a schematic diagram of time-of-use electricity prices and gas prices provided in example 2 of the present invention;
fig. 6 is a schematic diagram of a flexible resource allocation situation of scheme 1 according to embodiment 2 of the present invention;
fig. 7 is a schematic diagram of a flexible resource allocation situation of scheme 2 according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and the accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not used as limiting the present invention.
Example 1
At present, most of distribution network flexibility resource configuration methods are configured for a specific flexibility resource, and the complementary characteristics of multiple types of resources are ignored, but when the existing distribution network flexibility resource configuration methods are used for configuring multiple types of flexibility resources, in order to simplify a model or solve a process, only the uncertainty of a distribution network under a single time scale is usually considered, so that the power supply reliability of a configuration scheme is low or the resource redundancy is too high.
In view of the above technical problems, the present embodiment provides a method for allocating flexible resources of a power distribution network in consideration of long-term and short-term uncertainties.
Firstly, the uncertainty of the operation of the power distribution network is analyzed and processed. The uncertainty of the power distribution network is represented as output difference influenced by seasonal weather change in a long time scale, and is represented as error influenced by prediction accuracy in a short time scale. Aiming at seasonal differences, a scene analysis method is introduced to perform clustering processing on historical data to obtain a typical operation scene; aiming at the prediction error, a polyhedral uncertain model is introduced to perform robust optimization on each typical scene, and a power distribution network operation scene set comprehensively considering long-term and short-term uncertainty is obtained.
Then, starting from net load fluctuation caused by uncertainty of the power distribution network, a directional flexibility demand model is constructed; aiming at the problem of single flexible resource of the power distribution network, from the source load storage perspective, flexible adjusting capacity models of a micro gas turbine, an interruptible load and an energy storage device are respectively established; in addition, in order to ensure that the flexibility supply is always in a sufficient level relative to the requirement in the operation process, a flexibility evaluation index is constructed.
And finally, aiming at the lowest sum of the annual construction total cost and the distribution network operation cost, considering the safe operation of the distribution network, the output of various resources, the climbing and the balance constraint of flexibility supply and demand, providing a flexible resource overall configuration method combining planning and operation, and solving a very-integral linear planning problem by using Gurobi.
Specifically, the method for evaluating flexibility and configuring resources of a power distribution network provided by this embodiment includes the following steps:
step 1: acquiring historical annual daily operation data of the power distribution network, and clustering original scenes of the historical annual daily operation data by adopting a K-means clustering algorithm to obtain a typical operation scene set.
Step 2: and establishing a typical combined scene set of a fan, a photovoltaic and a load curve according to the typical operation scene set.
And step 3: and characterizing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set.
The following is specifically described for steps 1 to 3:
the uncertainty of the power distribution network mainly comes from the random characteristics presented by the actual operation state of components in the network, and mainly comprises the uncertainty of terminal load and the uncertainty of distributed renewable energy sources. The load or the distributed renewable energy represented by the wind and the light is influenced by the environment such as climate resources and the like, and has the characteristic of random fluctuation under different time scales. For output difference along with seasonal variation shown by a long time scale, a non-time sequence uncertainty research method based on probability density is often adopted, and probability characteristics of a research object with statistical significance are analyzed according to historical operating data. And aiming at errors which are shown in a short time scale and are influenced by prediction accuracy, a time sequence characteristic research method is mainly adopted to simulate a research object according to a time sequence simulation mode. In the embodiment, the uncertainty of wind power and load under two time scales is respectively processed by adopting a scene analysis method and a robust optimization theory.
1. Typical scene generation based on clustering algorithm
The optimized configuration of the flexible resources is a random planning problem, and a scene analysis method is often adopted in the process to solve the random planning problem. The ideal configuration method is established on the basis of analyzing all operation scenes of the power distribution network, so that the accuracy of simulating an actual power distribution system is best, and the economical efficiency of the operation of the configuration scheme is best. However, if all original scenes are simulated in the configuration model, huge calculation cost is undoubtedly generated due to the increase of decision variables and constraint conditions, and even dimension disaster occurs. In order to effectively solve the uncertainty under the long-time scale, a typical scene generation method based on a clustering algorithm is proved to be an efficient and accurate uncertainty planning method on the basis of comprehensively considering the solving efficiency of a distribution network full-time scene and a configuration model.
The climate conditions in the same region are regular each year. The future wind and light conditions of the power distribution area and the energy using behaviors of the users are assumed to be unchanged at the same time compared with the previous year, namely, the future wind and light output and the user load are considered to have the same distribution with historical data, so that the counted historical annual daily operation data is directly used as an original scene. Then, the original operation scene of the historical data is subjected to K-means clustering algorithmClustering is carried out to obtain S 1 A typical scenario is shown in equation (1). Meanwhile, the probability p corresponding to the typical scene is calculated according to the number of elements in each cluster of the clustering result s1 。
The model expression of a typical operating scenario set is:
Figure BDA0003878274960000101
in the formula (1), S 1 Representing the number of typical operation scenes, wherein n is the clustering number in the iteration process; l n Loss indexes caused by clustering; m is a clustering number ending value; s is the number of the original scene; p s Is the power vector of the original scene s;
Figure BDA0003878274960000102
is P s Cluster center of the cluster.
In the actual operation process, the wind-solar output and the load curve are considered to have the same seasonal characteristics, namely, a certain correlation is shown under the long-time scale. Therefore, a typical joint scene set for directly establishing wind turbine, photovoltaic and load curves can be expressed as:
Figure BDA0003878274960000103
in the formula (2), the reaction mixture is,
Figure BDA0003878274960000104
and the power vectors of the fan, the photovoltaic and the load curve in the original scene s at all times are obtained.
2. Robust optimization based prediction error handling
Under the influence of wind and light conditions, real-time change of user behaviors and prediction accuracy, a typical combined scene generated based on a K-means clustering algorithm is difficult to describe uncertainty of wind and load when the power distribution network runs under a short time scale, namely prediction error. If the predicted output of the wind and light load of a typical scene is directly used as operation data, a planning configuration scheme of the power distribution network flexibility resource is likely to have large deviation, and an uncertainty optimization theory under a short time scale needs to be introduced to reduce the influence of a prediction error.
Robust optimization is one of effective means for processing prediction errors, and the key point of the robust optimization is to establish an uncertainty set containing all uncertain parameters under a short time scale on the basis of the aforementioned typical scene. The uncertain parameters refer to error influence factor information including prediction precision, and reflect the conservative degree of the decision result of the corresponding configuration model, and the more accurate uncertainty set reflects the decision result of the configuration scheme with lower conservative degree. The uncertainty set mainly comprises box-type, ellipsoid-shaped and polyhedron forms, and compared with box-type sets with over-conservative degree and ellipsoid-shaped sets with non-linear structures, polyhedron sets are favored by researchers through moderate conservative degree and unique linear structures, and the form of polyhedron sets is shown in figure 1, C 1 C 5 C 3 C 7 A unique polyhedral linear structure is enclosed.
And characterizing the typical joint scene prediction error by adopting a polyhedron set to obtain a power distribution network operation scene set containing the worst uncertain range under a short time scale, wherein the operation scene set can be expressed as follows:
Figure BDA0003878274960000105
Figure BDA0003878274960000111
in the equation (3), all variables are power at a certain operation time, the time vector t is omitted for convenience, and Σ | a | represents the sum of information of each time period in one scheduling cycle.
Figure BDA0003878274960000112
Respectively representing the actual power of a fan, a photovoltaic and a load under the s-th typical scene; p s WT,f 、P s PV,f 、P s L,f Respectively representing the predicted output of the fan, the photovoltaic and the load of the s-th typical scene, namely the power value of the typical scene generated based on the clustering algorithm;
Figure BDA0003878274960000113
respectively representing the worst uncertain ranges allowed by the s-th typical scene wind-solar and load prediction errors；Γ ζ The index of conservation degree is represented, and the strength of robustness and the conservation degree of scene information are reflected.
3. Uncertain operation scene set
In order to guarantee the robustness of the flexible resource allocation scheme and guarantee the faster calculation rate, the end point scene method is adopted to screen the multiple scenes generated by the polyhedron set, so that the selected scenes contain more extreme points. The calculation flow of the distribution network operation scene set considering the long-term and short-term uncertainties is shown in fig. 2, and the generated scene set is shown in fig. 3, wherein the robust scene set is shown by taking a typical scene 3 as an example.
And 4, step 4: and establishing a flexibility requirement model.
And 5: and establishing a flexibility adjusting capacity model according to the flexibility requirement model.
The following is specifically described for step 4 and step 5:
NERC defines flexibility as the ability of power system resources to meet the change in net load. The flexible adjusting capacity of the traditional power distribution network is provided by an upper main network through a substation node, and is difficult to respond timely and effectively to net load fluctuation of nodes far away from the electrical distance under the influence of node voltage and grid transmission capacity.
1. Flexibility requirement model
Distribution network flexibility requirements arise from changes in the state of components within the distribution area, such as troubleshooting of supply and consumer equipment and source load fluctuations and uncertainties. The present embodiment mainly considers the flexibility requirement caused by the net load fluctuation, and can be expressed as:
Figure BDA0003878274960000114
in formula (4), P NL (t+τ)、P NL (t) actual payload power at system times t and t + T, respectively, P NL (t+τ)-P NL (t) is the net load fluctuation amount from t to t + tau. F D (t, τ) is the flexibility requirement of the distribution network, and its positive and negative characteristics reflect the directionality of the flexibility requirement, and it is just the upward flexibility requirement of the distribution network, otherwise it is the downward flexibility requirement of the distribution networkAnd (6) obtaining. Can be expressed as:
Figure BDA0003878274960000115
Figure BDA0003878274960000116
2. flexible resource model
The flexible resources considered in the embodiment are node-side resources, and mainly comprise a micro gas turbine, a demand response load and an energy storage device which are installed on each node.
(1) Micro gas turbine
Gas turbines belong to conventional power plants which can regulate the output power at a certain rate (ramp rate) before a maximum output and a minimum output. In other words, a gas turbine may provide flexibility in terms of a proportion of its installed capacity, which may be expressed as:
Figure BDA0003878274960000121
in the formula (7), the reaction mixture is,
Figure BDA0003878274960000122
respectively representing the flexibility up-and down-regulation capabilities provided by the gas turbine.
Figure BDA0003878274960000123
Representing the maximum and minimum output of the gas turbine, respectively. P gt,t Representing the real-time output power of the gas turbine.
Figure BDA0003878274960000124
Representing the upward and downward ramp rates of the gas turbine, respectively.
(2) Interruptible load
The demand response refers to that a user changes the inherent electricity consumption mode and responds to the change of the price signal of the power market or under an incentive mechanism sent by a power supply side so as to ensure the stable operation of a power grid, and is generally divided into demand response based on price and demand response based on incentive. The interruptible load has the advantages of more timely and stable response capability, high user selectivity and the like, and is often used as one of user-side flexibility resources. Its flexible adjustment capability can be expressed as:
Figure BDA0003878274960000125
in the formula (8), P il,t 、
Figure BDA0003878274960000126
Respectively controlling the output and the maximum output of the load at the current moment;
Figure BDA0003878274960000127
maximum limits for controllable load and recovery load are respectively removed for the system.
(3) Energy storage device
At present, the most widely applied energy storage technology in the power distribution network layer is storage battery energy storage, and the energy storage system has the advantages of flexible configuration, quick response, and higher energy density and power density. The flexibility adjustment capability of the energy storage system may be expressed as:
Figure BDA0003878274960000128
in the formula (9), the reaction mixture is,
Figure BDA0003878274960000129
respectively representing the flexibility up-and down-regulation capabilities of the energy storage device.
Figure BDA00038782749600001210
Respectively representing the real time charging/discharging power of the energy storage device.
Figure BDA00038782749600001211
Respectively, the maximum charge/discharge power of the energy storage device. E ess,t Representing a real-time electrical quantity of the energy storage device;
Figure BDA00038782749600001212
it indicates the minimum and maximum stored charge of the energy storage device, respectively.
3. Distribution network flexibility evaluation index
In order to keep the flexibility supply and demand balance state all the time in the operation process of the power distribution network, the requirement on the supply of flexibility resources is sufficient, and meanwhile, the grid structure of the power distribution network is required to provide enough transmission capacity margin so as to ensure that the flexibility resources can be timely and quickly called when needed. Therefore, two flexibility evaluation indexes of flexibility resource adequacy and branch flexibility response capability are defined.
(1) Flexible resource adequacy
Both the flexibility requirement and the adjustability of the flexibility resource are directional, so the defined flexibility adequacy consists of two parts, which can be expressed as:
Figure BDA0003878274960000131
in the formula (10), I fl The method represents the abundant level of the flexibility resources of the power distribution network in an operation period, and the value of the abundant level reflects the resource response capability of the power distribution network to net load fluctuation.
(2) Branch flexibility response capability
The response of various flexibility resources to the flexibility requirements of the distribution network requires sufficient network frame transmission channels. The branch circuit is used as a support platform for power supply and demand balance and flexible supply and demand balance, and a certain power flow margin needs to be reserved so as to deal with uncertain changes of net load fluctuation. The branch flexibility response capability is thus defined as the standard deviation of the branch load margin, which can be expressed as follows:
Figure BDA0003878274960000132
in the formula (11), L ij,t The real-time load margin of the branch transmission channel reflects the possibility of the grid channel blocking due to the tide flow. I is br The method represents the power flow balance degree of the power distribution network line in the whole operation period, and reflects the channel support capability of the power distribution network on net load fluctuation.
Step 6: and establishing a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model.
The details of step 6 are as follows:
on the basis of the uncertain operation scene of the power distribution network constructed through the clustering algorithm and the robust optimization theory, a flexible resource allocation model of the power distribution network is established, and the optimal allocation of the micro gas turbine, the interruptible load and the energy storage device and the simulation operation in a scene set are comprehensively considered.
1. Objective function
The flexible resource optimization configuration model takes the minimum annual comprehensive cost of the power distribution network in a planning period as a target function, and planning investment cost C comprising equal annual valued output inv And operating cost C ope It can be expressed as: minF = C inv +C ope (12) In formula (12), C inv Planning investment of equal annual valued contribution, C ope Representing equal-year valued output operation; wherein,
Figure BDA0003878274960000133
Figure BDA0003878274960000141
Figure BDA0003878274960000142
C p,t ＝P p,t π e,t (16)，
C wt,t ＝P we,t π wt (17)，
C pv,t ＝P pv,t π pv (18)，
C il,t ＝P il,t π il (19)，
Figure BDA0003878274960000143
Figure BDA0003878274960000144
C loss,t ＝P loss,t π e,t (22)，
in formulae (13) to (22), τ a Representing the equivalent annual coefficient of the device a, r being the discount rate, y a Is the economic life of the device a. c. C gt Investment costs for micro gas turbines; c. C il 、c ess The unit investment costs of the interruptible load contract electric quantity and the energy storage configuration capacity are respectively. x is the number of gt,i 、x il,i 、x ess,i Are investment decision variables of the gas turbine, the interruptible load and the energy storage equipment respectively. Ψ gt 、Ψ il 、Ψ ess And respectively configuring candidate nodes for the gas turbine, the interruptible load and the energy storage equipment. P p,t Purchasing electric quantity from a superior power grid for the power distribution network; p wt,t 、P pv,t Actual output of wind and solar power generation; p loss,t Is the network tidal current transmission loss. Pi e,t 、π g,t Real-time electricity prices and gas prices, respectively; pi wt 、π pv The unit cost of wind and light power generation is saved; pi il Invoking a cost for the interruptible load;
Figure BDA0003878274960000145
the unit cost of charging/discharging the energy storage device respectively. Eta gt Generating power efficiency for the gas turbine; q lhv Is the heat value of natural gas.
2. Constraint conditions
The constraint conditions of the flexible resource configuration model considered in this embodiment include power distribution network safe operation constraint, photovoltaic power generation constraint, energy storage device operation constraint, gas turbine operation constraint, and flexibility constraint.
(1) Power distribution network safe operation constraint
The technology adopts a branch power flow model with a second-order cone relaxation, and the constraint conditions are as follows:
Figure BDA0003878274960000146
Figure BDA0003878274960000147
Figure BDA0003878274960000151
Figure BDA0003878274960000152
Figure BDA0003878274960000153
Figure BDA0003878274960000154
in the formulas (23) to (28), pi (j) and delta (j) respectively represent the first and last node sets of the branch connected with the node j; b represents a node set; r is a radical of hydrogen ij 、x ij Respectively representing the resistance and reactance of the branch ij; p ij,t 、Q ij,t A branch power flow representing a t period; v j,t Is the node voltage; q cb,j,t Representing the reactive power provided by the switchable capacitors. tan theta ij Is the power factor of the interruptible load.
In addition, the grid safe operation constraints also include node voltage constraints, line current constraints and other conventional constraints, which are not shown here.
(2) Wind and solar output constraint
The power distribution network can control the output of the distributed power supply, and the related constraints are established as follows:
Figure BDA0003878274960000155
Figure BDA0003878274960000156
formula (29) and formula(30) In (1),
Figure BDA0003878274960000157
and
Figure BDA0003878274960000158
respectively the upper limit of the output of the fan and the upper limit of the output of the photovoltaic unit.
(3) Gas turbine constraints
The output of the gas turbine is influenced by technical parameters such as rated capacity, maximum and minimum technical output, inherent climbing rate and the like, and relevant constraint conditions are as follows:
Figure BDA0003878274960000159
Figure BDA00038782749600001510
Figure BDA00038782749600001511
in the formulae (31) to (33),
Figure BDA00038782749600001512
and
Figure BDA00038782749600001513
respectively representing the uphill slope rate and the downhill slope rate of the gas turbine.
(4) Energy storage device restraint
The energy storage device is influenced by the capacity, the charge state and the charge/discharge speed, and the corresponding constraint conditions are as follows:
Figure BDA00038782749600001514
E ess,T ＝E ess,0 (35)，
Figure BDA0003878274960000161
Figure BDA0003878274960000162
Figure BDA00038782749600001611
Figure BDA0003878274960000163
equation (34) is a charge-discharge model of the energy storage device; equation (35) indicates that the state of charge of the stored energy remains unchanged before and after a scheduling period; equations (36), (37), and (38) represent a charge power constraint, a discharge power constraint, and a charge quantity constraint, respectively; in formulae (34) to (39), n loss The self-loss rate of the energy storage device is represented, and the inherent electric quantity loss of the stored energy is described;
Figure BDA0003878274960000164
respectively, representing the charge/discharge efficiency of the energy storage device. E ess,0 Indicating the initial state of charge of the energy storage device, E ess,T Indicating the state of charge of the energy storage device after the end of the scheduling period. c. C ess,t The charge and discharge identification bit of the energy storage device is represented as a 0-1 type variable; x is the number of ess And the decision variable for representing whether the energy storage device is configured is an integer variable.
(5) Interruptible load constraints
Interruptible loads are primarily constrained by contractual capacity during operation:
Figure BDA0003878274960000165
(6) Reactive compensation device constraints
In the operation process, the reactive power compensation device is restricted by the maximum technical output:
Figure BDA0003878274960000166
n cb,j,t ≤N cb,j (42)，
Figure BDA0003878274960000167
in formulae (41) to (43), n cb,j,t The number of groups of capacitors to be switched in groups,
Figure BDA0003878274960000168
reactive compensation power provided for each group of capacitors; n is a radical of cb,j The upper limit of the number of groups of capacitors which can be put into operation;
Figure BDA0003878274960000169
is the upper limit of the number of times the capacitor can be switched during one operating cycle.
(7) Flexibility constraints
Besides the above power distribution network safe operation constraint and intra-network component configuration and operation constraint, the flexibility constraint of the power distribution network also needs to be considered, which can be expressed as:
Figure BDA00038782749600001610
Figure BDA0003878274960000171
in the process of considering the operation of the power distribution network, the characteristics presented under different time scales are solved by adopting a specific uncertainty method to obtain corresponding operation scene sets respectively; then comprehensively considering various types of flexibility resources such as a micro gas turbine, an energy storage device, a demand response load and the like, and establishing respective flexibility capability models; and finally, designing a flexible resource optimal configuration method combining planning and operation, decoupling and information transmission of a planning layer and an operation layer by adopting a target cascade method, and realizing flexible configuration of various resources under the condition of meeting the balance of flexibility supply and demand through repeated iteration.
Example 2
In order to verify the effectiveness of the distribution network flexibility resource allocation method considering long-term and short-term uncertainty given in embodiment 1, the patent adopts an IEEE33 node test system as an example for analysis.
1. Scene description
The wind and load curve prediction data adopts the set of the operation uncertain scenes which are established as shown in figure 3. The wind turbine access position in the test system is 9/16/22/24/30/31, the photovoltaic unit access position is 14/17/20/23/27, the reactive compensation device is accessed at a node No. 15/29, and the node 1 is a substation node and is connected with a superior power grid, as shown in FIG. 4. The time of use electricity and gas prices are shown in fig. 5. The relevant parameters of the flexible resource to be configured in the test system are shown in table 1.
Figure BDA0003878274960000172
Table 1 related parameters of flexible resources
Two comparison schemes are set for simulation test, and the effectiveness of the method is verified:
scheme 1: only typical operating scenarios are considered, and prediction errors are not considered;
scheme 2: the method provided by the technology.
2. Results and analysis
(1) Flexible resource configuration results
Fig. 6 and fig. 7 show the flexible resource configuration results of the two schemes, respectively. In the scheme 1, the access position of the gas turbine is 3/7/18, the access position of the energy storage device is 2/6/13/29, and the access position of the interruptible load is 6/12. And in the scheme 2, the access position of the gas turbine is 3/8/11/26, the access position of the energy storage device is 6/12/14/21/23/28, and the access position of the interruptible load is 12/20/25/31. The specific configuration is shown in table 2.
Figure BDA0003878274960000181
Table 2 flexible resource allocation results for two schemes
2. Flexibility index comparison
Table 3 shows the comparison of the configuration cost and flexibility index for the two schemes.
Comparison term Total cost (Wanyuan) Flexible resource adequacy Branch flexibility support capability
Scheme
1 4631.07 0.1264 0.5783
Scheme 2 5427.43 0.3018 0.7766
TABLE 3 flexibility index comparison
As can be seen from table 3, compared to scheme 2, scheme 1 has better economy since it does not take into account short-term uncertainty in configuring flexible resources. As can also be seen from the configuration nodes and the number of flexible resources, since the scheme 1 does not consider the influence of prediction errors, the amount of configured resources is relatively small. As such, in the actual operation process, the level of the flexibility resource adequacy of the scheme 1 is low, and the situation of insufficient flexibility resources may occur in the face of some emergency situations. In addition, as the scheme 2 has more uniform flexibility resource allocation, especially energy storage equipment, compared with the scheme 1, the method has the characteristics of less single-node allocation stations and more dispersed allocation nodes, local balance can be better achieved during actual scheduling, the line load rate is more uniform, the whole line load rate is in a lower level, and the branch flexibility supporting capability is stronger. In summary, the method for allocating flexible resources of a power distribution network, which considers long-term and short-term uncertainties, greatly improves the level of sufficient flexible resources and the supporting capability of the power distribution network under the condition of sacrificing part of the economy.
Example 3
Corresponding to embodiment 1, this embodiment provides a system for evaluating flexibility and configuring resources of a power distribution network, including:
the first scene set construction module is used for clustering original scenes of the historical annual daily operating data by adopting a K-means clustering algorithm to obtain a typical combined scene set of a fan, a photovoltaic and a load curve;
the second scene set construction module is used for representing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set;
the first model building module is used for building a flexibility demand model;
the second model building module is used for building a flexibility adjusting capacity model according to the flexibility demand model and building a flexibility evaluation index;
the third model building module is used for building a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model;
and the resource allocation module is used for allocating the flexible resources of the power distribution network according to the flexible resource allocation model of the power distribution network.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for evaluating flexibility and allocating resources of a power distribution network is characterized by comprising the following steps:
acquiring historical annual day operation data of a power distribution network, and clustering original scenes of the historical annual day operation data by adopting a K-means clustering algorithm to obtain a typical operation scene set; establishing a typical combined scene set of a fan, a photovoltaic and a load curve according to the typical operation scene set; representing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set;
establishing a flexibility demand model, and establishing a flexibility regulation capacity model according to the flexibility demand model;
establishing a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model;
and carrying out distribution network flexible resource allocation according to the distribution network flexible resource allocation model.
2. The method as claimed in claim 1, wherein the power distribution network flexibility evaluation and resource allocation method,
the model expression of the typical operation scene set is as follows:
Figure FDA0003878274950000011
in the formula (1), S 1 Representing the number of typical operation scenes, wherein n is the clustering number in the iteration process; l n Loss indexes caused by clustering; m is a clustering number ending value; s is the number of the original scene; p s Is the power vector of the original scene s;
Figure FDA0003878274950000012
is P s The cluster center of the cluster;
the model expression of the typical combined scene set is as follows:
Figure FDA0003878274950000013
in the formula (2), the reaction mixture is,
Figure FDA0003878274950000014
and the power vectors of the fan, the photovoltaic and the load curve in the original scene s at all times are obtained.
3. The method for flexibility assessment and resource allocation of the power distribution network according to claim 2, wherein the model expression of the power distribution network operation scene set is as follows:
Figure FDA0003878274950000015
in the formula (3), Σ | a | represents information of each period in one scheduling cycle,
Figure FDA0003878274950000016
respectively representing the actual power of a fan, a photovoltaic and a load under the s-th typical scene,
Figure FDA0003878274950000017
respectively representing the predicted output of the fan, the photovoltaic and the load under the s-th typical scene,
Figure FDA0003878274950000018
respectively representing the worst uncertain ranges allowed by the wind light and load prediction errors in the s-th typical scene; gamma-shaped ζ The index of conservation is shown.
4. The method according to claim 3, wherein the expression of the flexibility requirement model is as follows:
Figure FDA0003878274950000021
Figure FDA0003878274950000022
Figure FDA0003878274950000023
in the formula (4), F D (t, τ) is the flexibility requirement of the distribution network, P NL (t + T) and P NL (t) actual net load power, P, of the distribution network at times t and t + T, respectively NL (t+τ)-P NL (t) is the net load fluctuation amount of the power distribution network from t to t + tau; equation (5) represents the upward flexibility requirement created by the distribution network; equation (6) represents the downward flexibility requirement created by the distribution network.
5. The method according to claim 4, wherein the flexibility adjustment capability model comprises: a flexibility adjustment capability model of the micro gas turbine, a flexibility adjustment capability model of the interruptible load and a flexibility adjustment capability model of the energy storage device;
the expression of the flexibility regulation capability model of the micro gas turbine is as follows:
Figure FDA0003878274950000024
in the formula (7), the reaction mixture is,
Figure FDA0003878274950000025
and
Figure FDA0003878274950000026
respectively representing the flexibility up-and down-regulation capabilities provided by the gas turbine.
Figure FDA0003878274950000027
And
Figure FDA0003878274950000028
respectively representing the maximum and minimum output, P, of the gas turbine gt,t Represents the real-time output power of the gas turbine,
Figure FDA0003878274950000029
and
Figure FDA00038782749500000210
representing the gas turbine ramp up and ramp down rates, respectively;
the expression of the flexibility regulation capability model of the micro gas turbine is as follows:
Figure FDA00038782749500000211
(8) In the formula (8), P il,t And
Figure FDA00038782749500000212
respectively representing the output force and the maximum output force of the controllable load at the current moment,
Figure FDA00038782749500000213
and
Figure FDA00038782749500000214
respectively representing the maximum limits of the system for cutting off the controllable load and recovering the load;
the expression of the flexibility adjusting capacity model of the energy storage device is as follows:
Figure FDA00038782749500000215
Figure FDA00038782749500000216
in the formula (9), the reaction mixture is,
Figure FDA00038782749500000217
respectively representing the flexibility up-and down-regulation capabilities of the energy storage device.
Figure FDA00038782749500000218
Respectively, representing the real-time charging/discharging power of the energy storage device.
Figure FDA00038782749500000219
Respectively, the maximum charge/discharge power of the energy storage device. E ess,t Representing a real-time electrical quantity of the energy storage device;
Figure FDA00038782749500000220
it indicates the minimum and maximum stored charge of the energy storage device, respectively.
6. The method for flexibility assessment and resource allocation of a power distribution network according to claim 5, wherein the method for flexibility assessment and resource allocation comprises the following steps after the flexibility adjustment capability model is established: establishing a flexible resource adequacy model and a branch flexible response capability model according to the flexible adjustment capability model;
the expression of the flexible resource adequacy model is as follows:
Figure FDA0003878274950000031
Figure FDA0003878274950000032
in the formula (10), I fl Representing an abundance level of distribution network flexibility resources during an operating cycle;
the expression of the branch flexibility response capability model is as follows:
Figure FDA0003878274950000033
in the formula (11), L ij,t Real-time load margins for branch transmission channels, I br And the power flow balance degree of the power distribution network line in the whole operation period is represented.
7. The method for flexibility assessment and resource allocation of a power distribution network according to claim 6, wherein the objective function of the flexibility resource allocation model of the power distribution network is as follows: minF = C inv +C ope (12) In formula (12), C inv Planning investment of equal-year valued output, C ope Representing equal-year valued output operation;
Figure FDA0003878274950000034
Figure FDA0003878274950000035
Figure FDA0003878274950000036
C p,t ＝P p,t π e,t (16)，
C wt,t ＝P we,t π wt (17)，
C pv,t ＝P pv,t π pv (18)，
C il,t ＝P il,t π il (19)，
Figure FDA0003878274950000037
Figure FDA0003878274950000038
C loss,t ＝P loss,t π e,t (22)，
in formulae (13) to (22), τ a Representing the equivalent annual coefficient of the device a, r being the discount rate, y a Is the economic life of the device a. c. C gt Investment cost for micro gas turbine；c il 、c ess The unit investment costs of interruptible load contract electric quantity and energy storage configuration capacity are respectively. x is the number of gt,i 、x il,i 、x ess,i Respectively, the investment decision variables of the gas turbine, the interruptible load and the energy storage equipment. Ψ gt 、Ψ il 、Ψ ess And respectively configuring candidate nodes for the gas turbine, the interruptible load and the energy storage equipment. P is p,t Purchasing electric quantity from a superior power grid for the power distribution network; p wt,t 、P pv,t Actual output of wind and solar power generation; p loss,t Is the network tidal current transmission loss. Pi e,t 、π g,t Real-time electricity prices and gas prices, respectively; pi wt 、π pv The unit cost of wind and light power generation is saved; pi il Invoking a cost for the interruptible load;
Figure FDA0003878274950000041
the unit cost of charging/discharging the energy storage device is respectively. Eta gt Generating power efficiency for the gas turbine; q lhv Is the heating value of natural gas.
8. The method according to claim 7, wherein the constraints of the distribution network flexibility resource allocation model include: the method comprises the following steps of power distribution network safe operation constraint, wind-solar output constraint, gas turbine constraint, energy storage device constraint, interruptible load constraint, reactive power compensation device constraint and flexibility constraint;
the expression of the power distribution network safe operation constraint is as follows:
Figure FDA0003878274950000042
Figure FDA0003878274950000043
V j,t 2 ＝V i,t 2 -2(P ij,t r ij +Q ij,t x ij )+I ij,t 2 (r ij 2+x ij 2 ) (25)，
Figure FDA0003878274950000044
Figure FDA0003878274950000045
Figure FDA0003878274950000046
in the formulas (23) to (28), pi (j) and delta (j) respectively represent the first node and the last node set of the branch connected with the node j; b represents a node set; r is a radical of hydrogen ij And x ij Respectively representing the resistance and reactance of branch ij; p is ij,t And Q ij,t A branch power flow representing a t period; v j,t Is the node voltage; q cb,j,t Representing the reactive power provided by the switchable capacitors. tan theta ij A power factor that is an interruptible load;
the expression of the wind-solar output constraint is as follows:
Figure FDA0003878274950000047
Figure FDA0003878274950000048
in the formulae (29) and (30),
Figure FDA0003878274950000051
and
Figure FDA0003878274950000052
respectively the upper limit of the output of the fan and the photovoltaic machineAn upper limit of the group;
the expression for the gas turbine constraint is:
Figure FDA0003878274950000053
Figure FDA0003878274950000054
Figure FDA0003878274950000055
in the formulae (31) to (33),
Figure FDA0003878274950000056
and
Figure FDA0003878274950000057
respectively representing the climbing rate and the descending rate of the gas turbine;
the energy storage device constraint expression is:
Figure FDA0003878274950000058
E ess,T ＝E ess,0 (35)，
Figure FDA0003878274950000059
Figure FDA00038782749500000510
Figure FDA00038782749500000511
Figure FDA00038782749500000512
equation (34) is a charge-discharge model of the energy storage device; equation (35) indicates that the state of charge of the stored energy remains unchanged before and after a scheduling period; equations (36), (37), and (38) represent a charge power constraint, a discharge power constraint, and a charge quantity constraint, respectively; in formulae (34) to (39), n loss The self-loss rate of the energy storage device is represented, and the inherent electric quantity loss of the stored energy is described;
Figure FDA00038782749500000513
and
Figure FDA00038782749500000514
respectively representing the charging efficiency and the discharging efficiency of the energy storage device. E ess,0 Indicating the initial state of charge of the energy storage device, E ess,T Indicating the state of charge of the energy storage device after the end of the scheduling period. c. C ess,t The charging and discharging identification bit of the energy storage device is represented as a 0-1 type variable; x is the number of ess A decision variable for representing whether the energy storage device is configured is an integer variable;
the interruptible load constraint is expressed as:
Figure FDA00038782749500000515
the expression for the reactive compensation device constraint is:
Figure FDA00038782749500000516
n cb,j,t ≤N cb,j (42)，
Figure FDA0003878274950000061
in formulae (41) to (43), n cb,j,t The number of groups of capacitors are switched on for each group,
Figure FDA0003878274950000062
reactive compensation power provided for each group of capacitors; n is a radical of cb,j The upper limit of the number of groups of capacitors which can be put into operation;
Figure FDA0003878274950000063
the upper limit of the switchable operation times of the capacitor in one operation period is set;
the expression of the flexibility constraint is:
Figure FDA0003878274950000064
Figure FDA0003878274950000065
9. the method according to claim 8, wherein the power distribution network safety operation constraints comprise: node voltage constraints and line flow constraints.
10. A power distribution network flexibility assessment and resource allocation system is characterized by comprising:
the first scene set building module is used for clustering original scenes of the historical annual operating data by adopting a K-means clustering algorithm to obtain a typical combined scene set of a fan, a photovoltaic and a load curve;
the second scene set construction module is used for representing the prediction error of the typical combined scene set by adopting a polyhedron set to obtain a power distribution network operation scene set;
the first model building module is used for building a flexibility demand model;
the second model building module is used for building a flexibility adjusting capacity model according to the flexibility demand model and building a flexibility evaluation index;
the third model building module is used for building a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility adjusting capability model;
and the resource allocation module is used for allocating the flexible resources of the power distribution network according to the flexible resource allocation model of the power distribution network.
CN202211221233.5A 2022-10-08 2022-10-08 Power distribution network flexibility evaluation and resource allocation method and system Pending CN115411777A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
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CN116882718A (en) * 2023-09-08 2023-10-13 湖南大学 Power distribution network and drainage basin network flexible resource aggregation regulation and control method in high-temperature arid weather
CN117272850A (en) * 2023-11-23 2023-12-22 国网天津市电力公司宁河供电分公司 Elastic space analysis method for safe operation scheduling of power distribution network
CN118014164A (en) * 2024-04-08 2024-05-10 国网江西省电力有限公司经济技术研究院 Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116882718A (en) * 2023-09-08 2023-10-13 湖南大学 Power distribution network and drainage basin network flexible resource aggregation regulation and control method in high-temperature arid weather
CN116882718B (en) * 2023-09-08 2023-12-01 湖南大学 Power distribution network and drainage basin network flexible resource aggregation regulation and control method in high-temperature arid weather
CN117272850A (en) * 2023-11-23 2023-12-22 国网天津市电力公司宁河供电分公司 Elastic space analysis method for safe operation scheduling of power distribution network
CN117272850B (en) * 2023-11-23 2024-01-30 国网天津市电力公司宁河供电分公司 Elastic space analysis method for safe operation scheduling of power distribution network
CN118014164A (en) * 2024-04-08 2024-05-10 国网江西省电力有限公司经济技术研究院 Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements

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