CN114723258A - Two-stage planning method and system for power emergency resources - Google Patents

Two-stage planning method and system for power emergency resources Download PDF

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CN114723258A
CN114723258A CN202210318051.3A CN202210318051A CN114723258A CN 114723258 A CN114723258 A CN 114723258A CN 202210318051 A CN202210318051 A CN 202210318051A CN 114723258 A CN114723258 A CN 114723258A
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张思璐
刘庆时
刘念
韩建沛
张宏宇
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North China Electric Power University
State Grid Beijing Electric Power Co Ltd
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Abstract

The invention relates to a two-stage planning method and a two-stage planning system for electric power emergency resources, which belong to the field of energy sources, and comprise the following steps: constructing a load point power failure risk model considering load importance, an electric power emergency network model based on graph theory and a multi-point multi-target site selection planning model aiming at minimizing the load point power failure risk and the investment cost of emergency service points, solving by adopting an epsilon-constraint method to obtain an emergency service point site selection set of the power distribution network to be planned, and realizing site selection planning of the electric power emergency service points; and a second stage: based on the emergency service point site selection planning result in the first stage, considering the uncertainty characteristics of the emergency resource demand of each load point, providing an optimal configuration model of emergency resources at each service point based on interval optimization, introducing an optimistic optimization problem and a pessimistic optimization problem, realizing the optimal configuration of the emergency resources, providing decision support for emergency allocation of the emergency resources in the event, ensuring emergency response of the emergency resources in the event and quickly recovering power supply afterwards, and improving the toughness of the power system.

Description

Two-stage planning method and system for power emergency resources
Technical Field
The invention relates to the field of energy, in particular to a two-stage planning method and a two-stage planning system for electric power emergency resources.
Background
Under the constraints of carbon peak and carbon neutralization targets, the construction of a novel power system mainly based on new energy sources has become a necessary trend for the development of power systems in China. Since the 21 st century, the development of the power grid has been a leap forward, presenting a new feature of access to a high proportion of renewable energy sources and a high proportion of power electronic equipment. Meanwhile, with the construction and development of smart power grids and energy internet, the deep coupling of power grid information-physical systems and the increasingly complex morphological structure and operation mode of power grids make the safe operation of the power grids more vulnerable to the factors such as extreme weather, natural disasters and malicious attacks than before. The toughness of the power grid is the capability of reducing the loss caused by faults and recovering to a normal power supply state as soon as possible under various threats and disturbances inside and outside the power grid, and the optimization of the power grid considering the improvement of the toughness becomes a hot problem of the current domestic and foreign research.
Measures for improving the toughness of the power distribution network can be generally divided into the following three dimensions: the emergency resource planning method comprises the steps of planning and arranging emergency resources before an extreme event occurs in advance, emergently allocating the emergency resources in the event process and quickly responding the emergency resources after the event occurs. The existing research aiming at the toughness of the power distribution network mostly focuses on the aspects of emergency allocation and rapid power restoration of emergency resources, the planning of power emergency resources is the premise of ensuring emergency response in emergency resources and rapid power restoration afterwards, and has a vital role in improving the toughness of the power distribution network, the planning not only relates to the optimized site selection of emergency service points, but also comprises the capacity allocation of the emergency resources at each emergency service point, and the existing research cannot realize the organic combination of the emergency allocation and the rapid power restoration. Therefore, research related to a two-stage planning method for power emergency resources considering uncertainty and load importance needs to be developed, and theoretical support is provided for improving toughness of the power distribution network.
Disclosure of Invention
The invention aims to provide a two-stage planning method and a two-stage planning system for power emergency resources, so as to realize site selection planning of power emergency service points and optimal configuration of the emergency resources and improve the toughness of a power system.
In order to achieve the purpose, the invention provides the following scheme:
a method of two-phase planning of power emergency resources, the method comprising:
constructing a load point power failure risk model considering load importance;
establishing an electric power emergency network model based on graph theory by taking the power failure risk of the load point as the weight of the load point;
establishing a multi-point and multi-target site selection planning model aiming at minimizing the power failure risk of a load point and the investment cost of an emergency service point;
solving a multi-point multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method to obtain an emergency service point site selection set of the power distribution network to be planned;
considering the uncertainty of the emergency resource demand of each load point, establishing a power emergency resource configuration model representing the emergency resource demand of each load point in an interval form;
converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model;
based on the address selection set of the emergency service points of the power distribution network to be planned, an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned are solved by using a linear programming algorithm, and the quantity of emergency resources transported from the emergency service points of the power distribution network to be planned to the load points is obtained.
Optionally, the load point outage risk model considering the load importance degree is
Figure BDA0003569504540000021
In the formula, RiThe risk of power failure per unit time for load point i, piProbability of power failure at load point i in a given period, ρiThe importance of the load point i, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit blackout cost for the user.
Optionally, the method for establishing the power emergency network model based on the graph theory by using the load point power outage risk as the weight of the load point specifically includes:
the power failure risk of a load point is used as the weight of the load point, and an electric power emergency network empowerment undirected graph model is constructed as
Figure BDA0003569504540000022
Wherein,
Figure BDA0003569504540000023
which represents an undirected graph of the graph,
Figure BDA0003569504540000024
set of load points, v, representing an electric power emergency network1、v2And vVRespectively represent the 1 st, 2 nd and V th load points in the power emergency network, and epsilon ═ e1，e2，…，eEDenotes the set of paths of the power emergency network, e1、e2And eERespectively representing the 1 st, 2 nd and E th paths in the power emergency network,
Figure BDA0003569504540000025
representing a set of weights for load points of an electrical emergency network, where the load point outage risk is used to characterize the weight of the load point, R1、R2And RVWeights representing 1 st, 2 nd and V th load points in the power emergency network, respectively;
according to the power emergency network empowerment undirected graph model, establishing a power emergency network model based on graph theory as
Figure BDA0003569504540000031
Wherein l (v)i，vj) Representing load points v in an undirected graphiAnd load point vjThe shortest distance between, l (v)i，vp) Representing load points v in an undirected graphiAnd load point vpThe shortest distance between, l (v)p，vj) Representing the load point v in an undirected graphpAnd load point vjThe shortest distance between, l (v)i，vq) Representing load points v in an undirected graphiAnd load point vqThe shortest distance between, l (v)p，vq) Representing the load point v in an undirected graphpAnd load point vqThe shortest distance between the two elements of the first and second,
Figure BDA0003569504540000032
and a load point vjIn undirected graph
Figure BDA0003569504540000033
Medium load point vpAnd load point vqIn the meantime.
Optionally, the multi-point multi-target site selection planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point is
Figure BDA0003569504540000034
In the formula, F1Indicating the risk of blackout at the load point, F2Represents the investment cost of the emergency service point,
Figure BDA0003569504540000035
set of addresses representing load points i to emergency service points
Figure BDA0003569504540000036
The shortest distance of (a) satisfies
Figure BDA0003569504540000037
davgRepresenting an average travel speed of the emergency resource over the traffic network; siIs 01 variable, when siWhen 1, it indicates the load point
Figure BDA0003569504540000038
Addressing emergency service points, otherwise si＝0；CiIndicating the point of load
Figure BDA0003569504540000039
Equivalent investment cost for emergency service points in case of emergency service points, C0Represents the operation and maintenance cost of the emergency service platform, ti，maxMaximum allowable power off time, C, representing load point imaxAn upper cost limit for the investment is planned for the emergency service points.
Optionally, the solving, by using an epsilon-constraint method, a multi-point multi-target site selection planning model of the power distribution network to be planned to obtain an emergency service point site selection set of the power distribution network to be planned specifically includes:
converting a multi-point multi-target site selection planning model into a single-target optimization model of
Figure BDA0003569504540000041
In the formula,
Figure BDA0003569504540000042
representing the investment cost F of an emergency service Point in the kth sub-optimization problem2The upper limit of (a) is,
Figure BDA0003569504540000043
and
Figure BDA0003569504540000044
respectively representing the investment costs F of the emergency service points2The upper and lower limits of (d); k represents F2The value range of (A) is averagely divided into K sections;
solving the single-target optimization model according to basic data of the power distribution network to be planned to obtain an emergency service point address set of the power distribution network to be planned
Figure BDA0003569504540000045
Optionally, the power emergency resource configuration model for representing the emergency resource demand of each load point in an interval form is
Figure BDA0003569504540000046
The constraint conditions are as follows:
Figure BDA0003569504540000047
Figure BDA0003569504540000048
Figure BDA0003569504540000049
in the formula, FalloRepresents the total cost of the emergency resource,
Figure BDA00035695045400000410
represents the total transportation cost of the power emergency resource,
Figure BDA00035695045400000411
represents the investment cost of the emergency resources,
Figure BDA00035695045400000412
representing a set of power emergency resources, H representing the number of emergency resource types, pirjhRepresents the number of emergency resources h, l (v) transported by the emergency service point r to the load point jr,vj) Represents the shortest distance, ρ, between the emergency service point r and the load point jjRepresenting the probability of a power outage failure at load point j within a given time period,
Figure BDA00035695045400000413
represents the transportation cost per unit distance, M, of the emergency resource hrhRepresenting emergency resources in an emergency service Point rThe number of the (h) s is,
Figure BDA00035695045400000414
represents the unit investment cost of the emergency resource h, DjhRepresenting the demand of the load point j power emergency resource h,
Figure BDA0003569504540000051
Figure BDA0003569504540000052
and jhDrespectively representing the upper and lower boundaries of the uncertainty interval,
Figure BDA0003569504540000053
representing the upper limit of the number of emergency resources h for the emergency service point r.
Optionally, the optimistic optimization model is
Figure BDA0003569504540000054
Figure BDA0003569504540000055
Figure BDA0003569504540000056
Figure BDA0003569504540000057
The pessimistic optimization model is
Figure BDA0003569504540000058
Figure BDA0003569504540000059
Figure BDA00035695045400000510
Figure BDA00035695045400000511
Optionally, the linear programming algorithm is a simplex method or a branch-and-bound method.
A power emergency resource two-phase planning system, the system comprising:
the load point power failure risk model building module is used for building a load point power failure risk model considering the load importance degree;
the power emergency network model building module is used for building a power emergency network model based on graph theory by taking the power failure risk of the load point as the weight of the load point;
the multipoint and multi-target addressing planning model establishing module is used for establishing a multipoint and multi-target addressing planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point;
the emergency service point address set obtaining module is used for solving a multi-point multi-target address planning model of the power distribution network to be planned by adopting an epsilon-constraint method and obtaining an emergency service point address set of the power distribution network to be planned;
the power emergency resource allocation model establishing module is used for considering the uncertainty of the emergency resource requirements of each load point and establishing a power emergency resource allocation model representing the emergency resource requirements of each load point in an interval form;
the model conversion module is used for converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model;
and the emergency resource allocation planning module is used for solving an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned by utilizing a linear programming algorithm based on the emergency service point address set of the power distribution network to be planned, and obtaining the quantity of emergency resources transported to the load point by the emergency service point of the power distribution network to be planned.
Optionally, the load point outage risk model considering the load importance degree is
Figure BDA0003569504540000061
In the formula, RiThe risk of power failure per unit time for load point i, piProbability of power failure at load point i in a given period, ρiThe importance of the load point i, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit outage cost for the customer.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a two-stage planning method and a two-stage planning system for electric power emergency resources.A load point power failure risk model considering load importance is constructed in a first stage by considering the differentiated characteristics of power failure losses of different types of loads; establishing an electric power emergency network model based on graph theory by taking the power failure risk as the weight of a load point; establishing a multi-point multi-target site selection planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point, solving the multi-point multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method, obtaining an emergency service point site selection set of the power distribution network to be planned, and realizing site selection planning of the power emergency service point; in the second stage, based on the emergency service point addressing planning result in the first stage, the uncertainty characteristics of the emergency resource requirements of each load point are considered, the number of intervals is introduced to represent the emergency resource requirements of each load point, an optimal configuration model of the emergency resources at each service point based on interval optimization is provided, an optimistic optimization problem and a pessimistic optimization problem are introduced aiming at the constructed interval planning model, a deterministic conversion and solving method of the interval planning problem is provided, the optimal configuration of the emergency resources is realized, decision support is provided for emergency allocation of the emergency resources in the accident, emergency response in the accident of the emergency resources is guaranteed, power supply is rapidly recovered afterwards, and the toughness of the power system is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a two-stage planning method for power emergency resources provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a two-stage planning method and a two-stage planning system for power emergency resources, so as to realize site selection planning of power emergency service points and optimal configuration of the emergency resources and improve the toughness of a power system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a two-stage planning method for power emergency resources, which comprises the following steps of:
and 101, constructing a load point power failure risk model considering load importance.
Power systems generally quantify risk by multiplying the probability of an event or fault occurring by its resulting loss. For the same destructive event, the loss of different types of users is different, the differentiated characteristic of the power failure loss of different types of loads is considered, and a power failure risk quantification model considering the importance of the loads is provided.
For a load point i, the probability of power failure in a given period is assumed to be pi(this value can be obtained by a reliability assessment, and is not analyzed in detail here), the load point outage risk model considering the load importance is
Figure BDA0003569504540000071
In the formula, RiThe risk of power failure per unit time for load point i, piTo determine the probability of a power failure at load point i within a given time period, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit blackout cost for the user. Where ρ isiFor the importance of the load point i, when the value of ρ i is closer to 0, the importance representing the load point i is lower, and vice versa.
And step 102, establishing a graph theory-based power emergency network model by taking the power failure risk of the load point as the weight of the load point.
The method specifically comprises the following steps:
assuming that a given power emergency network comprises V load points and E traffic paths, taking the power failure risk of the load points as the weight of the load points, and constructing a weighted undirected graph model of the power emergency network as
Figure BDA0003569504540000081
Wherein,
Figure BDA0003569504540000082
which represents an undirected graph of the graph,
Figure BDA0003569504540000083
set of load points, v, representing an electric power emergency network1、v2And vVRespectively represent the 1 st, 2 nd and V th load points in the power emergency network, and epsilon is { e }1，e2，…，eEMeans for indicating the way of the electric power emergency networkSet of paths, e1、e2And eERespectively representing the 1 st, 2 nd and E th paths in the power emergency network,
Figure BDA0003569504540000084
representing a set of weights for load points of an electrical emergency network, where the load point outage risk is used to characterize the weight of the load point, R1、R2And RVWeights representing 1 st, 2 nd and V th load points in the power emergency network, respectively;
according to the power emergency network empowerment undirected graph model, the power emergency network model based on the graph theory is established as
Figure BDA0003569504540000085
Wherein l (v)i，vj) Representing load points v in an undirected graphiAnd load point vjThe shortest distance between, l (v)i，vp) Representing load points v in an undirected graphiAnd load point vpThe shortest distance between, l (v)p，vj) Representing load points v in an undirected graphpAnd load point vjThe shortest distance between, l (v)i，vq) Representing load points v in an undirected graphiAnd load point vqThe shortest distance between, l (v)p，vq) Representing load points v in an undirected graphpAnd load point vqThe shortest distance between, vi，
Figure BDA0003569504540000086
And a load point vjIn undirected graph
Figure BDA0003569504540000087
Medium load point vpAnd load point vqIn the meantime.
And 103, establishing a multipoint and multi-target addressing planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point.
An emergency service point multi-target planning model is established, the power failure risk of a load point and the investment cost of the emergency service point are comprehensively considered, and the optimization targets are as follows:
Figure BDA0003569504540000088
Figure BDA0003569504540000089
constructing an emergency service point multi-target planning model under the following constraint conditions:
emergency service time limit constraints:
Figure BDA0003569504540000091
and (3) investment cost constraint:
Figure BDA0003569504540000092
therefore, the multipoint and multi-target site selection planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point is
Figure BDA0003569504540000093
In the formula, F1Indicating the risk of blackout at the load point, F2Represents the investment cost of the emergency service point,
Figure BDA0003569504540000094
set of addresses from load point i to emergency service point
Figure BDA0003569504540000095
The shortest distance of (a) satisfies
Figure BDA0003569504540000096
davgRepresenting an average travel speed of the emergency resource on the traffic network; siIs a variable from 0 to 1, when siWhen 1, it indicates the load point
Figure BDA0003569504540000097
Addressing emergency service points, otherwise si＝0；CiIndicating the point of load
Figure BDA0003569504540000098
Equivalent investment cost for emergency service points in case of emergency service points, C0Represents the operation and maintenance cost of the emergency service platform, ti，maxMaximum allowable power off time, C, representing load point imaxAn upper cost limit for the investment is planned for the emergency service points.
And 104, solving a multi-point and multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method, and obtaining an emergency service point site selection set of the power distribution network to be planned.
The general idea of the epsilon-constraint method is to select an important target from a multi-target optimization model as an optimization object and the rest targets as additional constraint conditions, so that the original multi-target optimization problem can be converted into a series of single-target optimization problems. The generalized form of a given multi-objective optimization problem is as follows:
min{f1(x)，f2(x)，…，fL(x)}
s.t.x∈Ω
wherein x is a decision variable, f1(x) …, fL (x) is the feasible field for L objective functions, and Ω is x. Selection of f1(x) For the main purpose, the epsilon-constraint method can be considered and converted into:
min{f1(x)，f2(x)，…，fL(x)}
s.t.f2(x)≤ε2
…
fL(x)≤εL
x∈Ω
wherein epsilon2，…，εLAre respectively an objective function f2(x)，…，fL(x) The upper limit of (3).
Based on this, for the purposes of this documentPlanning model of emergency service point by using objective function F1As a primary objective, apply an objective function F2The value range is averagely divided into K sections, then the multi-point multi-target addressing planning model is converted into a single-target optimization model of
Figure BDA0003569504540000101
In the formula,
Figure BDA0003569504540000102
representing the investment cost F of an emergency service Point in the kth sub-optimization problem2The upper limit of (a) is,
Figure BDA0003569504540000103
to know
Figure BDA0003569504540000104
Investment costs F for emergency service points respectively2The upper and lower limits of (d);
traffic and power network topology, load point power requirements, backup power capacity, and the like are collected.
Solving the single-target optimization model to obtain an emergency service point site set of the power distribution network to be planned
Figure BDA0003569504540000105
And 105, considering the uncertainty of the emergency resource demand of each load point, and establishing an electric power emergency resource configuration model representing the emergency resource demand of each load point in an interval form.
The optimal configuration of the power emergency resources needs to realize the minimum transportation cost of the emergency resources by optimally distributing various emergency resources at each emergency service point on the basis of deeply analyzing the emergency resource requirements of each load point. The method is characterized in that strong uncertainty exists in outage of power element equipment due to factors such as weather and human factors, the emergency resource requirements of each load point are represented in an interval form, and a power emergency resource configuration model for representing the emergency resource requirements of each load point in the interval form is
Figure BDA0003569504540000106
The constraint conditions are as follows:
Figure BDA0003569504540000111
Figure BDA0003569504540000112
Figure BDA0003569504540000113
in the formula, FalloRepresents the total cost of the emergency resource,
Figure BDA0003569504540000114
represents the total transportation cost of the power emergency resource,
Figure BDA0003569504540000115
represents the investment cost of the emergency resources and,
Figure BDA0003569504540000116
representing a set of power emergency resources, H representing the number of emergency resource types, pirjhRepresents the number of emergency resources h, l (v) transported by the emergency service point r to the load point jr,vj) Represents the shortest distance, ρ, between the emergency service point r and the load point jjRepresenting the probability of a power outage failure at load point j within a given time period,
Figure BDA0003569504540000117
represents the transportation cost per unit distance, M, of the emergency resource hrhRepresenting the number of emergency resources h in the emergency service point r,
Figure BDA0003569504540000118
represents the unit investment cost of the emergency resource h, DjhRepresenting the demand of the load point j power emergency resource h,
Figure BDA0003569504540000119
Figure BDA00035695045400001110
and jhDrespectively representing the upper and lower boundaries of the uncertainty interval,
Figure BDA00035695045400001111
representing the upper limit of the number of emergency resources h for the emergency service point r.
And 106, converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model.
For the interval optimization model, the optimization aim is to obtain an interval solution of the emergency resource configuration scheme. Therefore, in order to find the upper and lower boundaries of the interval solution, the generalized idea is to convert the interval optimization model into two deterministic models, namely an optimistic optimization problem and a pessimistic optimization problem. By solving two deterministic optimization problems, optimistic and pessimistic solutions for emergency resource configuration schemes may be obtained.
The optimistic optimization problem is as follows:
Figure BDA00035695045400001112
Figure BDA00035695045400001113
Figure BDA00035695045400001114
Figure BDA00035695045400001115
Figure BDA00035695045400001116
wherein wjhRepresenting the number of emergency resources h required for load point j.
Because the optimistic optimization problem is minimized in both the inner layer and the outer layer, the optimistic optimization problem can be directly combined into a single-layer optimization problem to be solved. In order to ensure that the solution is the lower bound of the interval optimization problem, the uncertain interval is relaxed to the lower bound to ensure that the feasible domain is maximum, so the optimistic optimization model after conversion is
Figure BDA0003569504540000121
Figure BDA0003569504540000122
Figure BDA0003569504540000123
Figure BDA0003569504540000124
The pessimistic optimization problem is as follows:
Figure BDA0003569504540000125
Figure BDA0003569504540000126
Figure BDA0003569504540000127
Figure BDA0003569504540000128
Figure BDA0003569504540000129
for pessimistic optimization problems, if the feasible domain of optimization is expected to be as small as possible, the uncertain interval is relaxed to be an upper bound, so that the converted pessimistic optimization model is
Figure BDA00035695045400001210
Figure BDA00035695045400001211
Figure BDA00035695045400001212
Figure BDA00035695045400001213
And 107, solving an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned by utilizing a linear programming algorithm based on the address selection set of the emergency service points of the power distribution network to be planned, and obtaining the quantity of emergency resources transported to a load point by the emergency service points of the power distribution network to be planned.
The power emergency resource configuration model based on interval optimization is converted into two deterministic linear programming problems, and a simple method, a branch-and-bound method and the like can be adopted for solving.
In the provided two-stage planning framework, in the stage 1, considering the differentiated characteristics of different types of loads in power failure loss, and providing a load importance and power failure risk quantitative model; establishing an empowerment undirected graph model of the power emergency network by taking the power failure risk as the weight of the load point; and (3) taking the factors such as the power failure risk of the load point, the emergency service time limit, the service point investment cost and the like into consideration, providing an emergency service point multi-target location planning model, and providing an epsilon-constraint-based multi-target planning model solving method, so that the actual location requirement of the emergency service point is met. In the stage 2, based on the emergency service point addressing planning result in the stage 1, considering the uncertainty characteristics of the emergency resource requirements of each load point, introducing interval numbers to represent the emergency resource requirements of each load point, and providing an optimal configuration model of the emergency resources at each service point based on interval optimization; aiming at the constructed interval planning model, an optimistic optimization problem and a pessimistic optimization problem are introduced, and a deterministic transformation and solving method of the interval planning problem is provided.
The two-stage planning method provided by the invention can realize site selection planning of the power emergency service point and optimal configuration of emergency resources, improves the toughness of the power system and has certain practical application value.
By adopting the two-stage planning method for the power emergency resources, the optimized result can be applied to the emergency service point optimized location of the actual power system and the capacity allocation of the emergency resources at each emergency service point. The basic data according to the two-stage planning method provided by the invention comprise traffic and power network topology, load point power requirements, standby power supply capacity and the like, and the development practical situation of a power system is met; considering the differentiated characteristics of different types of load power failure losses, providing a load importance and power failure risk quantitative model; establishing an empowerment undirected graph model of the power emergency network by taking the power failure risk as the weight of the load point; and (3) taking the factors such as the power failure risk of the load point, the emergency service time limit, the service point investment cost and the like into consideration, providing an emergency service point multi-target location planning model, and providing an epsilon-constraint-based multi-target planning model solving method, so that the actual location requirement of the emergency service point is met. Introducing interval numbers to represent the emergency resource requirements of each load point, and reflecting the uncertainty characteristics of the emergency resource requirements of each load point; aiming at the constructed interval planning model, an optimistic optimization problem and a pessimistic optimization problem are introduced, a deterministic transformation and solving method of the interval planning problem is provided, and the solving process is simplified. By using the two-stage planning method, the optimization result is applied to the integrated planning of the site selection of the emergency service point and the capacity allocation of the emergency resources, the site selection planning of the power emergency service point and the optimal allocation of the emergency resources can be realized, the decision support is provided for the emergency allocation of the emergency resources in the accident, the emergency response of the emergency resources in the accident is ensured, the power supply is quickly recovered afterwards, and the toughness of the power system is improved.
The invention also provides a two-stage planning system for the power emergency resources, which comprises:
the load point power failure risk model building module is used for building a load point power failure risk model considering the load importance degree;
the power emergency network model building module is used for building a power emergency network model based on graph theory by taking the power failure risk of the load point as the weight of the load point;
the multipoint and multi-target addressing planning model establishing module is used for establishing a multipoint and multi-target addressing planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point;
the emergency service point site selection set obtaining module is used for solving a multi-point multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method and obtaining an emergency service point site selection set of the power distribution network to be planned;
the power emergency resource allocation model establishing module is used for considering the uncertainty of the emergency resource requirements of each load point and establishing a power emergency resource allocation model representing the emergency resource requirements of each load point in an interval form;
the model conversion module is used for converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model;
and the emergency resource allocation planning module is used for solving an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned by utilizing a linear programming algorithm based on the emergency service point address set of the power distribution network to be planned, and obtaining the quantity of emergency resources transported to the load point by the emergency service point of the power distribution network to be planned.
The load point power failure risk model considering the load importance degree is
Figure BDA0003569504540000141
In the formula, RiThe risk of power failure per unit time for load point i, piTo determine the probability of a power failure at load point i within a given time period, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit blackout cost for the user.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A two-stage planning method for power emergency resources is characterized by comprising the following steps:
constructing a load point power failure risk model considering load importance;
establishing an electric power emergency network model based on graph theory by taking the power failure risk of the load point as the weight of the load point;
establishing a multi-point and multi-target site selection planning model aiming at minimizing the power failure risk of a load point and the investment cost of an emergency service point;
solving a multi-point multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method to obtain an emergency service point site selection set of the power distribution network to be planned;
considering the uncertainty of the emergency resource demand of each load point, establishing a power emergency resource allocation model representing the emergency resource demand of each load point in an interval form;
converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model;
based on the address selection set of the emergency service points of the power distribution network to be planned, an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned are solved by using a linear programming algorithm, and the quantity of emergency resources transported from the emergency service points of the power distribution network to be planned to the load points is obtained.
2. The two-stage power emergency resource planning method according to claim 1, wherein the load point outage risk model considering the load importance degree is
Figure FDA0003569504530000011
In the formula, RiThe risk of power failure per unit time for load point i, piProbability of power failure at load point i in a given period, ρiThe importance of the load point i, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit blackout cost for the user.
3. The two-stage planning method for power emergency resources according to claim 2, wherein the power emergency network model based on the graph theory is established by taking the power outage risk of the load point as the weight of the load point, and specifically comprises:
the power failure risk of a load point is used as the weight of the load point, and an electric power emergency network empowerment undirected graph model is constructed as
Figure FDA0003569504530000021
Wherein,
Figure FDA0003569504530000022
which represents an undirected graph of the graph,
Figure FDA00035695045300000213
represents a set of load points of the power emergency network,
Figure FDA00035695045300000214
and
Figure FDA00035695045300000215
respectively represent the 1 st, 2 nd and V th load points in the power emergency network, and epsilon ═ e1，e2，…，eEDenotes the set of paths of the power emergency network, e1、e2And eERespectively representing the 1 st, 2 nd and E th paths in the power emergency network,
Figure FDA0003569504530000023
representing a set of weights for load points of an electrical emergency network, where the load point outage risk is used to characterize the weight of the load point, R1、R2And RVWeights representing 1 st, 2 nd and V th load points in the power emergency network, respectively;
according to the power emergency network empowerment undirected graph model, establishing a power emergency network model based on graph theory as l (v)i，vj)＝min{l(vi，vp)+l(vp，vj)，l(vi，vq)+l(vp，vq)}
Figure FDA0003569504530000024
Wherein,
Figure FDA00035695045300000218
representing load points in an undirected graph
Figure FDA00035695045300000216
And load point
Figure FDA00035695045300000217
The shortest distance between, l (v)i，vp) Representing load points in an undirected graph
Figure FDA00035695045300000219
And load point vpThe shortest distance between, l (v)p，vj) Representing load points v in an undirected graphpAnd load point
Figure FDA00035695045300000220
The shortest distance between, l (v)i，vq) Representing load points in an undirected graph
Figure FDA00035695045300000221
And load point vqThe shortest distance between, l (v)p，vq) Representing load points v in an undirected graphpAnd load point vqThe shortest distance between the two elements of the first and second,
Figure FDA0003569504530000025
and load point
Figure FDA00035695045300000222
In undirected graph
Figure FDA0003569504530000026
Medium load point vpAnd load point vqIn the meantime.
4. The two-stage power emergency resource planning method according to claim 3, wherein the multipoint and multi-target site selection planning model aiming at minimizing the load point outage risk and the investment cost of the emergency service point is
Figure FDA0003569504530000027
In the formula, F1Represents negativeRisk of power failure at load point, F2Represents the investment cost of the emergency service point,
Figure FDA0003569504530000028
set of addresses representing load points i to emergency service points
Figure FDA0003569504530000029
The shortest distance of (a) satisfies
Figure FDA00035695045300000210
davgRepresenting an average travel speed of the emergency resource over the traffic network; s isiIs a variable from 0 to 1, when siWhen 1, it indicates the load point
Figure FDA00035695045300000211
Addressing emergency service points, otherwise si＝0；CiIndicating the point of load
Figure FDA00035695045300000212
Equivalent investment cost for emergency service points in case of emergency service points, C0Represents the operation and maintenance cost of the emergency service platform, ti，maxRepresents the maximum allowable power-off time, C, of the load point imaxAn upper cost limit for the investment is planned for the emergency service points.
5. The two-stage power emergency resource planning method according to claim 4, wherein the step of solving the multi-point multi-target site selection planning model of the power distribution network to be planned by using an epsilon-constraint method to obtain the emergency service site selection set of the power distribution network to be planned specifically comprises the steps of:
converting a multi-point multi-target site selection planning model into a single-target optimization model of
Figure FDA0003569504530000031
In the formula,
Figure FDA0003569504530000032
investment cost F of emergency service points in atmosphere of kth sub-optimization problem2The upper limit of (a) is,
Figure FDA0003569504530000033
and
Figure FDA0003569504530000034
respectively representing the investment costs F of the emergency service points2The upper and lower limits of (d); k represents F2The value range of (A) is averagely divided into K sections;
solving the single-target optimization model according to basic data of the power distribution network to be planned to obtain an emergency service point address set of the power distribution network to be planned
Figure FDA0003569504530000035
6. The two-stage power emergency resource planning method according to claim 5, wherein the power emergency resource configuration model for representing the emergency resource demand of each load point in an interval form is
Figure FDA0003569504530000036
The constraint conditions are as follows:
Figure FDA0003569504530000037
Figure FDA0003569504530000038
Figure FDA0003569504530000039
in the formula, FalloRepresents the total cost of the emergency resource,
Figure FDA00035695045300000310
represents the total transportation cost of the power emergency resource,
Figure FDA00035695045300000311
represents the investment cost of the emergency resources,
Figure FDA00035695045300000312
representing a set of power emergency resources, H representing the number of emergency resource types, pirjhRepresents the number of emergency resources h, l (v) transported by the emergency service point r to the load point jr，vj) Represents the shortest distance, ρ, between the emergency service point r and the load point jjRepresenting the probability of a power outage failure at load point j within a given time period,
Figure FDA0003569504530000041
represents the transportation cost per unit distance, M, of the emergency resource hrhRepresenting the number of emergency resources h in the emergency service point r,
Figure FDA0003569504530000042
represents the unit investment cost of the emergency resource h, DjhRepresenting the demand of the load point j power emergency resource h,
Figure FDA0003569504530000043
Figure FDA0003569504530000044
and
Figure FDA0003569504530000045
respectively representing the upper and lower boundaries of the uncertainty interval,
Figure FDA0003569504530000046
indicating emergency service pointsr upper limit of the number of emergency resources h.
7. The power emergency resource two-phase planning method according to claim 6,
the optimistic optimization model is
Figure FDA0003569504530000047
s.t.
Figure FDA0003569504530000048
Figure FDA0003569504530000049
Figure FDA00035695045300000410
The pessimistic optimization model is
Figure FDA00035695045300000411
s.t.
Figure FDA00035695045300000412
Figure FDA00035695045300000413
Figure FDA00035695045300000414
8. The two-stage power emergency resource planning method according to claim 1, wherein the linear planning algorithm is a simplex method or a branch-and-bound method.
9. A power emergency resource two-phase planning system, the system comprising:
the load point power failure risk model building module is used for building a load point power failure risk model considering the load importance degree;
the power emergency network model building module is used for building a power emergency network model based on graph theory by taking the power failure risk of the load point as the weight of the load point;
the multipoint and multi-target addressing planning model establishing module is used for establishing a multipoint and multi-target addressing planning model aiming at minimizing the power failure risk of the load point and the investment cost of the emergency service point;
the emergency service point site selection set obtaining module is used for solving a multi-point multi-target site selection planning model of the power distribution network to be planned by adopting an epsilon-constraint method and obtaining an emergency service point site selection set of the power distribution network to be planned;
the power emergency resource allocation model establishing module is used for considering the uncertainty of the emergency resource requirements of each load point and establishing a power emergency resource allocation model representing the emergency resource requirements of each load point in an interval form;
the model conversion module is used for converting the power emergency resource configuration model into an optimistic optimization model and a pessimistic optimization model;
and the emergency resource allocation planning module is used for solving an optimistic optimization model and a pessimistic optimization model of the power distribution network to be planned by utilizing a linear programming algorithm based on the emergency service point address set of the power distribution network to be planned, and obtaining the quantity of emergency resources transported to the load point by the emergency service point of the power distribution network to be planned.
10. The two-stage power emergency resource planning system according to claim 9, wherein the load point outage risk model considering load importance is
Figure FDA0003569504530000051
In the formula, RiRisk of blackout per unit time for load point i, piProbability of power failure at load point i in a given period, ρiThe importance of the load point i, h (ρ)i) Unit power loss, L, for load point iiIs the power demand of load point i, PiReserve generating capacity, Q, for load point i0The unit blackout cost for the user.
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