CN110571799A - distributed power supply key node optimal configuration method for improving elasticity of power distribution network - Google Patents

distributed power supply key node optimal configuration method for improving elasticity of power distribution network Download PDF

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CN110571799A
CN110571799A CN201910843608.3A CN201910843608A CN110571799A CN 110571799 A CN110571799 A CN 110571799A CN 201910843608 A CN201910843608 A CN 201910843608A CN 110571799 A CN110571799 A CN 110571799A
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power supply
distributed power
distribution network
node
attack
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CN110571799B (en
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别朝红
卞艺衡
黄格超
李更丰
林雁翎
马慧远
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Xian Jiaotong University
State Grid Beijing Electric Power Co Ltd
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Xian Jiaotong University
State Grid Beijing Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a distributed power supply key node optimal configuration method for improving the elasticity of a power distribution network, which comprises the following steps: 1) acquiring transformer data connected with a distribution network system, distribution network load data and system topological structure information; 2) under given attack, establishing constraints such as distribution network power flow and distributed power supply configuration, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target; 3) and (3) iterating and continuously searching the most serious attack scene in the step 2) by applying a robust optimized CCG algorithm, thereby optimizing a distributed power supply configuration scheme, maintaining the function of the power grid to the maximum extent under the most serious attack, and reducing the load shedding loss. According to the method, the optimal scheme of configuring the distributed power supply at the key node of the power distribution network is obtained in the planning stage, when the power distribution network is subjected to natural disasters or artificial attacks, the distributed power supply can be used for providing emergency power for the local load, the elasticity of the power distribution network is improved, and the scheme can reduce the disaster damage of the power distribution network in the most serious attack scene.

Description

Distributed power supply key node optimal configuration method for improving elasticity of power distribution network
Technical Field
The invention belongs to the field of safety planning operation of power systems, and particularly relates to a distributed power supply key node optimal configuration method for improving the elasticity of a power distribution network.
Background
As an important infrastructure related to national security and national economic life lines, an electric power system is required to perform stable and reliable operation in a normal environment and maintain necessary functions in the event of an extreme disaster. A large-scale power failure accident is caused by a frequently-occurring extreme disaster, and research on the resilience of a power system is an important issue to be focused. On the distribution network level, the application of new technologies of the power system such as distributed power supply, network reconstruction and the like provides a feasible idea for improving the elasticity of the power grid. The distributed power supply can drive important loads in an area after a distribution network fault, emergency power supply is carried out on the loads, and how to configure the distributed power supply in a planning stage is achieved, so that the problem that power failure loss of the loads is reduced to the maximum extent after an extreme disaster event occurs becomes a key problem.
disclosure of Invention
The invention aims to provide a distributed power supply key node optimal configuration method for improving the elasticity of a power distribution network. The invention improves the elasticity of the power distribution network, and the scheme can reduce the disaster damage of the power distribution network in the most serious attack scene.
In order to achieve the purpose, the invention adopts the following technical scheme:
A distributed power supply key node optimal configuration method for improving elasticity of a power distribution network comprises the following steps:
1) Acquiring transformer data connected with a distribution network system, distribution network load data and system topological structure information;
2) under given attack, establishing distribution network power flow, distributed power supply configuration and line attack constraint, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target;
3) and (3) applying a robust optimized CCG algorithm to iteratively search the most serious attack scene in the step 2), thereby optimizing a distributed power supply configuration scheme, and maintaining the function of the power grid to the maximum extent when the power grid is subjected to the most serious attack.
as a further improvement of the invention, the distributed power supplies are all controllable generator sets, when the distribution network fails after a disaster occurs, the failure part is isolated, and the distributed power supplies supply power for important loads in a non-failure area.
as a further improvement of the present invention, the establishment of the distribution network power flow, the distributed power supply configuration and the line attack constraint optimizes the location of the distributed power supply with the minimum load shedding amount as a target, and the optimization objective function is as follows:
wherein h represents a decision variable vector configured by the distributed power supply, u represents a decision variable vector of an attack line, z represents a power flow variable vector such as power voltage, and the specific content and meaning of the variable vectors are explained in detail below; pshed,jrepresenting the load shed power for node j and V representing the set of nodes.
As a further improvement of the invention, the power flow constraint of the distribution network is as follows:
Equation (2) is a power balance constraint, where δ (j) and π (j) represent the set of child and parent nodes of node j, respectively; pij、QijIs the active and reactive power, P, flowing through the line (i, j)DG,j、QDG,jFor generator output, PL,j、Pshed,jload demand and load shedding active power, Q, for node j, respectivelyL,j、Qshed,jRespectively cutting off reactive power for the load demand and the load of the node j; equation (3) is a node voltage relationship constraint of the linearized distribution network power flow, where L is a set of lines and U is a set of linesiRepresents the node voltage, rij、xijIs line resistance, reactance, U0At a rated voltage, M is a large number, cijThe variable is 0/1, 0 is taken to represent that the line (i, j) is in an open state, and otherwise, the line (i, j) is in a closed (normal operation) state; equation (4) is a line capacity constraint, wherein,Represents the maximum allowable power of the line; formula (5) bit load shedding constraint; the formula (6) is the restriction of the upper limit and the lower limit of the node voltage;
Distributed power configuration constraints:
Equation (7) indicates that if node j is configured with a distributed power supplyOr the node j is connected with the transformer substation, the power generation power of the node j should not exceed the power generation powercapacity of an electric machine or substation, wherein sDGIs the minimum unit capacity, kj,nIs a variable of 0/1, and the content of the active carbon,Representing a multiple of the minimum unit capacity allocation at node j, parameter Nsthe maximum allowable configuration capacity of each node is NssDGThe distributed power supply of (a) is,Namely the capacity of the distributed power supply on the node j; equation (8) is the planning budget for configuring the distributed power supply, i.e. the maximum number of configurations should not exceed CNMaximum total configuration capacity should not exceed CS；
And (3) line attack constraint:
Wherein u isijThe variable is 0/1 and represents whether the line is attacked or not, B represents the attack budget of the maximum number of attack lines, and U represents an uncertain set of attacks; equation (9) shows that if the line (i, j) is attacked (u)ij1) it will be in the off state (c)ij＝0)。
As a further improvement of the invention, the CCG algorithm is applied to continuously search the most serious attack scene through iteration, and the method specifically comprises the following steps:
4.1) decomposing the robust optimization problem into two sub-problems, wherein the upper layer problem is to optimize the configuration position of the distributed power supply under given attack;
4.2) the lower layer problem is to optimize the most serious attack strategy for the system under the given distributed power supply configuration scheme;
and 4.3) continuously iterating the upper-layer problem and the lower-layer problem, continuously adding constraint to the upper-layer problem by the CCG algorithm, and after the solution of the upper-layer problem and the lower-layer problem converges to a given interval, obtaining the distributed power supply configuration scheme which is the optimal scheme considering the most serious scene by the upper-layer problem.
The invention has the beneficial effects that:
According to the method, the position and the capacity of the distributed power supply are optimized in a distribution network planning order, so that the load guarantee and the recovery capability of the distribution network in a post-disaster fault state are improved, a double-layer robust optimization model is established, the distributed power supply is configured under a given planning budget, the loss of the distribution network under the most serious disaster attack is minimized, the power failure economic loss of the power grid can be effectively reduced, the economy and the practicability are both considered, and a reasonable suggestion can be provided for the planning of the elastic distribution network. By utilizing the method, the optimal scheme of configuring the distributed power supply at the key nodes of the power distribution network can be obtained in the planning stage, and when the power distribution network is attacked by natural disasters (typhoons, floods and the like) or people, the distributed power supply can be used for providing emergency power for the local load, so that the elasticity of the power distribution network is improved, and the scheme can reduce the disaster damage of the power distribution network in the most serious attack scene.
Drawings
FIG. 1 is a diagram of a typical radial distribution network;
FIG. 2 is a diagram of an IEEE 37 node distribution network system;
FIG. 3 is specific load data;
FIG. 4 is a relationship between distribution network post-disaster load shedding and the number of configurable distributed power sources and attack budgets; (a) the load shedding value when the total capacity budget of the distributed power supply is 500 kVA; (b) the load shedding value when the total capacity budget of the distributed power supply is 1000 kVA;
Fig. 5 shows a configuration scheme and a worst attack scenario in which the number of configurable distributed power sources is 3 and the attack budget is 7.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
the invention provides a distributed power supply key node optimal configuration method for improving elasticity of a power distribution network, which is characterized in that distributed power supplies are configured under given planning budgets (configurable distributed power supply number and capacity) by identifying key nodes of the power distribution network, so that the power distribution network can guarantee important loads to the maximum extent by virtue of the distributed power supplies after bearing the most serious disaster attack under the given attack budget (attack line number), and a distributed power supply configuration scheme capable of guaranteeing electric energy supply to the maximum extent under the most serious attack scene is obtained by applying a double-layer robust optimization (CCG) algorithm (Column and constraint generation algorithm). The scheme comprises the following steps:
1) acquiring transformer data connected with a distribution network system, distribution network load data and system topological structure information;
2) Under given attack, establishing constraints such as distribution network tide and distributed power supply configuration, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target;
3) And (3) iterating and continuously searching the step 2) by applying a robust optimized CCG algorithm (Column and Constraint Generation algorithm) to find the most serious attack scene, so that a distributed power supply configuration scheme is optimized, the power grid can maintain the function to the maximum extent under the most serious attack, and the load shedding loss is reduced.
Preferably, the distributed power supply optimization configuration problem is solved through a mathematical optimization method, and optimization constraints comprise power flow constraints such as distribution network power and voltage and distributed power supply configuration constraints.
the method comprises the following steps of applying a CCG algorithm (Column and Constraint Generation algorithm) to search the most serious attack scene through iteration, optimizing a distributed power supply configuration scheme, enabling a power grid to maintain functions to the maximum extent under the most serious attack, and reducing load shedding loss, wherein the method specifically comprises the following steps:
4.1) decomposing the robust optimization problem into two sub-problems, wherein the upper layer problem is to optimize the configuration position of the distributed power supply under given attack (obtained by the lower layer problem of the previous iteration) because the attack is given and not optimal, and the upper layer problem is the relaxation problem of the original robust optimization problem and provides the lower bound of the solution;
4.2) the lower layer problem is to optimize the most severe attack strategy for the system given the distributed power configuration scheme (resulting from the upper layer problem of this iteration) since the distributed power configuration scheme is given rather than optimal, the lower layer problem providing an upper bound for the solution.
And 4.3) continuously iterating the upper layer problem and the lower layer problem, continuously adding constraints (attack scenes) to the upper layer problem by the CCG algorithm, and after the solutions of the upper layer problem and the lower layer problem converge to a given interval, obtaining the distributed power supply configuration scheme which is the optimal scheme considering the most serious scene by the upper layer problem.
The distributed power supplies are controllable generator sets such as micro gas turbines, when a distribution network fails after a disaster occurs, the failure part is isolated, and the distributed power supplies supply power for important loads in a non-failure area.
establishing constraints such as distribution network tide and distributed power supply configuration, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target, wherein an optimization objective function is as follows:
wherein h represents a decision variable vector of the distributed power supply configuration, u represents a decision variable vector of an attack line, and z represents a power flow variable vector such as power voltage, and specific contents and meanings of the variable vectors will be described in detail below. Pshed,jRepresenting the load shed power for node j and V representing the set of nodes.
the power flow constraint of the distribution network is as follows:
Equation (2) is a power balance constraint, where δ (j), and π (j) represent the set of child and parent nodes of node j, respectively, as shown in FIG. 1. Pij、Qijis the active and reactive power, P, flowing through the line (i, j)DG,j、QDG,jFor generator output, PL,j、Pshed,jload demand and load shedding active power, Q, for node j, respectivelyL,j、Qshed,jReactive power is cut for the load demand and load of node j, respectively. Equation (3) is a node voltage relationship constraint of the linearized distribution network power flow, where L is a set of lines and U is a set of linesirepresents the node voltage, rij、xijis line resistance, reactance, U0At a rated voltage, M is a large number, cijAnd the variable is 0/1, 0 is taken to represent that the line (i, j) is in an open state, and otherwise, the line (i, j) is in a closed (normal operation) state. Equation (4) is a line capacity constraint, wherein,Indicating the maximum allowed power of the line. Equation (5) is the load shedding constraint. And the formula (6) is the restriction of the upper limit and the lower limit of the node voltage.
distributed power configuration constraints:
equation (7) indicates that if node j is configured with a distributed power supplyor the node j is connected with the transformer substation, the generated power of the node j should not exceed the capacity of the generator or the transformer substation, wherein sDGIs the minimum unit capacity, kj,nIs a variable of 0/1, and the content of the active carbon,Representing a multiple of the minimum unit capacity allocation at node j, parameter Nsthe maximum allowable configuration capacity of each node is NssDGThe distributed power supply of (a) is,i.e. the capacity of the distributed power supply at node j. Equation (8) is the planning budget for configuring the distributed power supply, i.e. the maximum number of configurations should not exceed CNMaximum total configuration capacity should not exceed CS。
And (3) line attack constraint:
Wherein u isijthe variable is 0/1, which indicates whether the line is attacked or not, B is the attack budget of the maximum number of attack lines, and U indicates the uncertain set of the attack. Equation (9) shows that if the line (i, j) is attacked (u)ij1) it will be in the off state (c)ij＝0)。
the robust optimized CCG algorithm comprises the following steps:
To simplify the variable representation, all power flow variables (P) are definedij，Qij，Pshed,j，Qshed,j，PDG,j，QDG,j，Uj) Component variable vector z, distributed power configuration decision variable: (kj,n) A component variable vector h, an attack decision variable (u)ij) The component variable vector u, the vector relation A for the constraints (2) - (7)1h+B1z≥F1And (4) showing.
the upper layer problem:
the upper layer problem is that at a given attack strategySlightly optimizing the optimal configuration of the distributed power supply, wherein the mathematical expression is as follows:Defining an auxiliary variable alpha to represent the minimum optimized load shedding value under the most serious attack, and converting the original problem into the form which can be iterated as follows:
An objective function: min alpha
Constraint conditions are as follows:
A1h+B1u*≥F1 k＝1,2,
The symbol indicates that the corresponding variable is a given value (resulting from the previous iteration) and k is the current iteration number.
The lower layer problems:
The lower layer problem is configured in a given distributed power supply scheme (i.e. h obtained by the upper layer problem in the previous iteration)*) The most serious attack scenario is found as follows, and the mathematical expression is as follows:Let η denote the dual variable vector of z with respect to constraints (2) - (7) and use Φ (h)*) The feasible domain of η, according to the dual principle (dual principle), the minimization problem inside the linear programming can be translated into its dual maximization problem, as follows:
An objective function:
constraint conditions are as follows:
u∈U
η∈Φ(h*)
The iteration flow of the CCG algorithm is shown in Table 1.
TABLE 1 CCG Algorithm flow
The process of the present invention is further illustrated below with reference to specific examples.
An IEEE 37-node distribution network is adopted as an example, as shown in FIG. 2, the capacity of a transformer substation is 5MVA, and the minimum unit capacity s of a distributed power supply is configuredDGThe voltage is 50kVA, the feasible voltage range is 0.9 to 1.1p.u, the line capacity is 0.5MVA, and the total system load is 981.58kW +544.02 kvar. The solution of the optimization problem adopts a Cplex 12.8.0 solver in an MATLAB environment.
tables 1 and 2 show the optimization results of the positions and capacities of the distributed power supplies under different conditions that the attack budget ranges from 3 to 6 and the distributed power supply quantity budget ranges from 1 to 3 under the total capacity budget of the distributed power supplies of 500 and 1000kVA respectively.
TABLE 2 Total Capacity budget C for distributed Power supplySoptimal configuration results at 500kVA
TABLE 3 Total Capacity budget C for distributed Power supplySoptimal configuration results at 1000kVA
As can be seen from the above table, when the number of configured distributed power sources is small and the attack budget is large, the node 732 is a key node because the node 732 has the highest active load demand, and as the budget for the number of distributed power sources increases, the key nodes become 709, 702, 744, because these nodes have more connection lines and more probability of reserving a power flow path after the attack.
Fig. 4 shows a relationship between the trimming load after the distribution network disaster, the number of the configurable distributed power supplies, and the attack budget, and it can be seen that the trimming load is approximately in a decreasing trend with the increase of the number and capacity of the configurable distributed power supplies, and the trimming load is in an increasing trend with the increase of the number of attack lines.
fig. 5 is a diagram of an optimal configuration scheme and a corresponding most severe attack when the number of configurable distributed power sources is 3, the total capacity is 600kVA, and the attack budget is 7. Distributed power supplies are configured on nodes 702, 709 and 734 with more lines, the corresponding most serious attacks are attacking substation power supply lines 701-799 and distributed power supply lines 709-730, 709-708, 709-775, 734-733, 710-734 and 737-734, and after the configuration scheme is adopted, even if the most serious attack with a fault line of 7 is suffered, the distributed power supply of the node 702 supplies power for surrounding loads, and the distributed power supply of the node 709 supplies power for loads of two nodes 709 and 731.
The scope of the present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included in the scope of the claims and their equivalents, which are described in the specification, for those skilled in the art.

Claims (5)

1. a distributed power supply key node optimal configuration method for improving elasticity of a power distribution network is characterized by comprising the following steps:
1) acquiring transformer data connected with a distribution network system, distribution network load data and system topological structure information;
2) Under given attack, establishing distribution network power flow, distributed power supply configuration and line attack constraint, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target;
3) And (3) applying a robust optimized CCG algorithm to iteratively search the most serious attack scene in the step 2), thereby optimizing a distributed power supply configuration scheme, and maintaining the function of the power grid to the maximum extent when the power grid is subjected to the most serious attack.
2. the distributed power supply key node optimal configuration method for improving the elasticity of the power distribution network according to claim 1, characterized in that: the distributed power supplies are controllable generator sets, when a distribution network fails after a disaster occurs, the failed part is isolated, and the distributed power supplies supply power for important loads in a non-failure area.
3. The distributed power supply key node optimal configuration method for improving the elasticity of the power distribution network according to claim 1, characterized in that: establishing distribution network tide, distributed power supply configuration and line attack constraint, and optimizing the position of the distributed power supply by taking the minimum load shedding amount as a target, wherein an optimization objective function is as follows:
Wherein h represents a decision variable vector configured by the distributed power supply, u represents a decision variable vector of an attack line, z represents a power flow variable vector such as power voltage, and the specific content and meaning of the variable vectors are explained in detail below; pshed,jrepresenting the load shed power for node j and V representing the set of nodes.
4. The distributed power supply key node optimal configuration method for improving the elasticity of the power distribution network according to claim 3, wherein:
The power flow constraint of the distribution network is as follows:
Equation (2) is a power balance constraint, where δ (j) and π (j) represent the set of child and parent nodes of node j, respectively; pij、QijIs the active and reactive power, P, flowing through the line (i, j)DG,j、QDG,jfor generator output, PL,j、Pshed,jload demand and load shedding active power, Q, for node j, respectivelyL,j、Qshed,jRespectively cutting off reactive power for the load demand and the load of the node j; equation (3) is a node voltage relationship constraint of the linearized distribution network power flow, where L is a set of lines and U is a set of linesirepresents the node voltage, rij、xijIs line resistance, reactance, U0At a rated voltage, M is a large number, cijThe variable is 0/1, 0 is taken to represent that the line (i, j) is in an open state, and otherwise, the line (i, j) is in a closed (normal operation) state; equation (4) is a line capacity constraint, wherein,Represents the maximum allowable power of the line; formula (5) bit load shedding constraint; the formula (6) is the restriction of the upper limit and the lower limit of the node voltage;
distributed power configuration constraints:
Equation (7) indicates that if node j is configured with a distributed power supplyOr the node j is connected with the transformer substation, the generated power of the node j should not exceed the capacity of the generator or the transformer substation, wherein sDGis the minimum unit capacity, kj,nIs a variable of 0/1, and the content of the active carbon,Multiple, parameter representing minimum unit capacity configuration at node jN srepresents the maximum allowable configuration capacity of each node asN ssDGthe distributed power supply of (a) is,Namely the capacity of the distributed power supply on the node j; equation (8) is the planning budget for configuring the distributed power supply, i.e. the maximum number of configurations should not exceed CNMaximum total configuration capacity should not exceed CS；
And (3) line attack constraint:
Wherein u isijThe variable is 0/1 and represents whether the line is attacked or not, B represents the attack budget of the maximum number of attack lines, and U represents an uncertain set of attacks; equation (9) shows that if the line (i, j) is attacked (u)ij1) it will be in the off state (c)ij＝0)。
5. the distributed power supply key node optimal configuration method for improving the elasticity of the power distribution network according to claim 1, characterized in that: the method for searching the most serious attack scene through iteration by applying the CCG algorithm specifically comprises the following steps:
4.1) decomposing the robust optimization problem into two sub-problems, wherein the upper layer problem is to optimize the configuration position of the distributed power supply under given attack;
4.2) the lower layer problem is to optimize the most serious attack strategy for the system under the given distributed power supply configuration scheme;
And 4.3) continuously iterating the upper-layer problem and the lower-layer problem, continuously adding constraint to the upper-layer problem by the CCG algorithm, and after the solution of the upper-layer problem and the lower-layer problem converges to a given interval, obtaining the distributed power supply configuration scheme which is the optimal scheme considering the most serious scene by the upper-layer problem.
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