CN114696321B - Multi-type distributed power supply two-stage planning method for power distribution network restoring force improvement - Google Patents
Multi-type distributed power supply two-stage planning method for power distribution network restoring force improvement Download PDFInfo
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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
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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- H02J2300/28—The renewable source being wind energy
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Abstract
The invention relates to a multi-type distributed power supply two-stage planning method for improving the restoring force of a power distribution network, which comprises the following steps: 1, counting fault rates of distribution network lines under different disaster conditions and availability rates of different types of distributed power Supplies (DGs) according to regional historical disaster data; 2, simulating and generating a damage scene of the circuit after the power distribution network is subjected to extreme disasters of various types and levels by using a Monte Carlo method; 3, respectively optimizing planning positions of the distributed photovoltaic, wind power and a conventional distributed unit aiming at each scene; and 4, respectively counting the optimal planning positions of the distributed power supplies in all scenes contained under different disasters, sorting according to the descending order of the occurrence times of the positions, and taking the first several times of positions with the same number as the planning number of the power supplies as the optimal planning positions of the power supplies under the corresponding disasters. The invention can improve the capacity of supplying power to the load and the speed of recovering the load after the power distribution network encounters an extreme disaster, thereby improving the recovery power of the power distribution network.
Description
Technical Field
The invention relates to the field of power supply planning of restoring force lifting measures, in particular to a multi-type distributed power supply two-stage planning method for restoring force lifting of a power distribution network
Background
The electric power system is used as one of key infrastructures of the energy system and is a basis for normal operation of other infrastructures, so that the electric power system not only can reliably operate in normal conditions, but also needs to maintain necessary functions and quickly recover power supply capacity in extreme disasters. In order to improve the restoration force of the power system, the power supply capacity is often selected and addressed in a power supply planning mode. The power supply planning in the past often considers planning in aspects of power supply type, capacity, investment scheme, environmental protection and the like in a power grid under general conditions. Under the extreme disaster condition, how to start more quickly and recover more load after the power grid is disconnected is not considered, so that the power system cannot start quickly and efficiently after the extreme disaster, and the power supply of the important load is recovered. The equipment is damaged and the human body is injured and killed, and huge losses are brought to national economy.
Disclosure of Invention
The invention provides a multi-type distributed power supply two-stage planning method for improving the restoring force of a power distribution network, which aims to overcome the defects of the prior art, so that the power distribution network can be restored more quickly and better after being damaged by extreme disasters, the load power supply capacity and the load restoring speed of the power distribution network after the power distribution network encounters the extreme disasters are improved, and the overall restoring force level of the power distribution network is improved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The invention discloses a multi-type distributed power supply two-stage planning method for improving the restoring force of a power distribution network, which is characterized by comprising the following steps of:
Step one, counting fault rates of distribution network lines under different disasters according to regional historical disaster data, and availability rates of distributed power sources DG of different types:
Step 1.1, counting the failure rate of the power distribution network lines under different disasters, wherein the failure rate Q= { Q 1,Q2…Qi…QN } of the power distribution network lines corresponding to different levels of typhoons and stormy weather is included, and Q i represents the failure rate of the ith line corresponding to different levels of typhoons and stormy weather; i=1, 2, …, N is the line number; n represents the number of lines;
Step 1.2, counting working states of distributed power sources DG of a power distribution network under different disasters, wherein the working states comprise working states of distributed photovoltaic power generation, a distributed fan and a conventional distributed power source under typhoons and stormy weather at different levels respectively; wherein, let the working state of the p-th distributed photovoltaic power generation be PV p, then the working state vector of N 1 distributed photovoltaic power generation be The working state of the w distributed fan is recorded as WT w; the operating state vector of the N 2 distributed fans is recorded as/>When wind comes, let PV p＝0,WTw =1; when a storm comes, let PV p＝1,WTw =0;
simulating and generating a damage scene of the circuit of the power distribution network after each type and each level of extreme disasters by using a Monte Carlo method;
step 2.1, defining scene simulation times under various disasters as NUM according to typhoons and stormy weather at different levels;
Step 2.2, randomly generating a random number R i which is uniformly distributed in the interval of [0,1] for the ith line;
Step2.3, determining the state S i of the ith line by using the formula (1), thereby obtaining a state vector s= (S 1,…Si…,SN) of the N lines:
In the formula (1), S i =1 indicates that the i-th line is in an operating state, and S i =0 indicates that the i-th line is in a failure state;
step 2.4, randomly sampling according to the output statistical characteristic of the distributed power supply to obtain a column vector of the distributed photovoltaic power generation Column vector of distributed fan outputColumn vector of conventional distributed power supply outputWherein/>Output representing p-th distributed photovoltaic power generation,/>Represents the output force of the w-th distributed fan,/>Representing the output of the v-th conventional distributed power supply, T representing the number of conventional distributed power supplies;
Step 2.5, obtaining a power output value PVG=PV×PV ABS of distributed photovoltaic power generation under extreme weather conditions by using the power state and the power output product, wherein the power output value WTG=WT×WT ABS of a distributed fan, and the power output value PDG=PDG ABS of a conventional distributed power supply;
Step 2.6, repeating NUM for 2.2-step 2.5, and respectively storing data of each simulation generation scene;
step three, respectively optimizing planning positions of distributed power generation, fans and conventional distributed power supplies according to each scene:
step 3.1, determining a subnet where the distributed power supply DG is located by taking the maximum scene total load recovery value as a target:
Step 3.1.1, acquiring load PL s connected with any s-th node, distributed photovoltaic output PVG s connected with the s-th node, distributed fan output WTG s connected with the s-th node and conventional distributed power output PDG s connected with the s-th node in the power distribution network;
Defining a distributed power value to be planned, which is connected with an s-th node, as RPDG s; wherein the load PL s to which the s-th node is connected is negative; the rest are positive;
Adding the load PL s connected with the s node, the distributed photovoltaic output PVG s connected with the s node, the distributed fan output WTG s connected with the s node, the conventional distributed power output PDG s connected with the s node and the distributed power value RPDG s to be planned connected with the s node to obtain a load recovery value PT s of the s node;
Step 3.1.2, determining a subnet where the distributed power supply DG is located by using the formula (2) with the maximum scene total load recovery value as a target:
In formula (2), N 3,num represents the total number of subnets of num in the current scene; PM j,num represents the total load recovery value of the jth subnet under the current scene num, and has:
In the formula (3), PL j,num is the absolute value of the sum of the loads of all the nodes in the j-th subnet under the current scene num; PG j,num is the absolute value of the sum of the power generation values of all nodes in the jth subnet under the current scene num and the power generation value of the power supply to be planned; PT s,num is the load recovery value of the s-th node under the current scene num; PT j,num is the sum of load recovery values of all nodes in the jth subnet under the current scene num;
Step 3.2, on the basis of determining the subnet where the distributed power supply is located, determining the optimal node where the distributed power supply is located by taking the shortest power transmission time from the black start unit to the unit to be recovered as a target:
Step 3.2.1, assuming that r units to be recovered are provided, b black start units, distributing all the units to be recovered to the black start units according to the aim that the sum of recovery time from all the units to be recovered to the black start units is shortest, wherein the grouping number is equal to the black start power supply number, and establishing a grouping model of the units to be recovered by using the formula (4) -formula (6):
s.t.v 1g,…,vcg,…,vbg =1 and v 1g、vcg、…、vbg are all non-negative integers (5)
In the formulas (4) - (6), [ omega 1g,…,ωtg,…,ωbg]T ] is a recovery time vector of the unit g to be recovered, and omega tg represents the time required for starting power to be sent from the black start unit t to the unit g to be recovered; [ v 1g,…,vcg,…,vbg ] represents the distribution relation between the unit g to be recovered and all the black start units, v cg represents the distribution relation between the unit c to be recovered and the unit g to be recovered, v eg represents the distribution relation between the unit e to be recovered and the unit g to be recovered, and when v cg =1 and v eg =0, the unit g to be recovered is distributed to the unit c to be recovered; c is not equal to e, e is not less than 1 and not more than b, c is not less than 1 and not more than b, and alpha is a proportionality coefficient;
step 3.2.2, enumerating node positions of the distributed power supply DGs in the sub-network, and obtaining optimal nodes of the distributed power supply DGs in the sub-network through a to-be-recovered unit model;
Step four, respectively counting the optimal nodes of each distributed power supply DG in all scenes contained by different disasters, sorting the nodes in descending order according to the occurrence times of each position, and taking the first few times of positions with the same number as the power supply planning number as the optimal planning positions of the power supplies under the corresponding disasters:
step 4.1, counting the optimal nodes of each distributed power supply DG in all scenes under any disaster, wherein the optimal planning node vector of the distributed photovoltaic power generation in the scene num is made to be The optimal planning node vector of the distributed fan is as followsOptimal planning node vector of conventional distributed power supply is/>Thus, an optimal planning node vector L PV of distributed photovoltaic power generation of all scenes under the current disaster, an optimal planning node vector L WT of a distributed fan and an optimal planning node vector L PDG of a conventional distributed power supply are obtained;
Step 4.2, respectively ordering the times of the elements in the L PV,LWT,LPDG in a descending order, and selecting the elements with the corresponding quantity, which are ordered in front, as the optimal planning positions of the distributed power supply DG under the current disaster according to the planning quantity of the distributed power supply DG;
and 4.3, repeating the steps 4.1-4.2 to obtain the optimal planning positions of the distributed power supply DG under different disasters.
Compared with the prior art, the invention has the beneficial effects that:
1. The method comprises the steps of firstly, optimizing the target of the highest total load recovery level of each sub-network after the power system is subjected to extreme disaster cracking, determining the sub-network where the distributed power supply (DG) is located, and improving the coordination of each region during the load recovery of the power system;
2. the method and the device have the advantages that after the sub-network where the distributed power supply is located is determined by taking the maximum scene total load recovery value as a target, the shortest power transmission time from the black start unit to the unit to be recovered is taken as a target, the position of a specific node where the distributed power supply (DG) is located in the sub-network is determined, and the recovery speed after the system accident is improved;
Drawings
Fig. 1 is a schematic flow chart of a two-stage planning method of a multi-type distributed power supply for improving the restoring force of a power distribution network.
Detailed Description
In this embodiment, as shown in fig. 1, a multi-type distributed power supply two-stage planning for improving the restoring force of a power distribution network is performed according to the following steps: step one, counting fault rates of distribution network lines under different disasters according to regional historical disaster data, and availability rates of distributed power sources DG of different types:
Step 1.1, counting the failure rate of the power distribution network lines under different disasters, wherein the failure rate Q= { Q 1,Q2…Qi…QN } of the power distribution network lines corresponding to different levels of typhoons and stormy weather is included, and Q i represents the failure rate of the ith line corresponding to different levels of typhoons and stormy weather; i=1, 2, …, N is the line number; n represents the number of lines;
Step 1.2, counting working states of distributed power sources DG of a power distribution network under different disasters, wherein the working states comprise working states of distributed photovoltaic power generation, a distributed fan and a conventional distributed power source under typhoons and stormy weather at different levels respectively; wherein, let the working state of the p-th distributed photovoltaic power generation be PV p, then the working state vector of N 1 distributed photovoltaic power generation be The working state of the w distributed fan is recorded as WT w; the operating state vector of the N 2 distributed fans is recorded as/>When wind comes, let PV p＝0,WTw =1; when a storm comes, let PV p＝1,WTw =0;
simulating and generating a damage scene of the circuit of the power distribution network after each type and each level of extreme disasters by using a Monte Carlo method;
step 2.1, defining scene simulation times under various disasters as NUM according to typhoons and stormy weather at different levels;
Step 2.2, randomly generating a random number R i which is uniformly distributed in the interval of [0,1] for the ith line;
Step2.3, determining the state S i of the ith line by using the formula (1), thereby obtaining a state vector s= (S 1,…Si…,SN) of the N lines:
In the formula (1), S i =1 indicates that the i-th line is in an operating state, and S i =0 indicates that the i-th line is in a failure state;
step 2.4, randomly sampling according to the output statistical characteristic of the distributed power supply to obtain a column vector of the distributed photovoltaic power generation Column vector of distributed fan outputColumn vector of conventional distributed power supply outputWherein/>Output representing p-th distributed photovoltaic power generation,/>Represents the output force of the w-th distributed fan,/>Representing the output of the v-th conventional distributed power supply, T representing the number of conventional distributed power supplies;
Step 2.5, obtaining a power output value PVG=PV×PV ABS of distributed photovoltaic power generation under extreme weather conditions by using the power state and the power output product, wherein the power output value WTG=WT×WT ABS of a distributed fan, and the power output value PDG=PDG ABS of a conventional distributed power supply;
Step 2.6, repeating NUM for 2.2-step 2.5, and respectively storing data of each simulation generation scene;
step three, respectively optimizing planning positions of distributed power generation, fans and conventional distributed power supplies according to each scene:
step 3.1, determining a subnet where the distributed power supply DG is located by taking the maximum scene total load recovery value as a target:
Step 3.1.1, acquiring load PL s connected with any s-th node, distributed photovoltaic output PVG s connected with the s-th node, distributed fan output WTG s connected with the s-th node and conventional distributed power output PDG s connected with the s-th node in the power distribution network;
Defining a distributed power value to be planned, which is connected with an s-th node, as RPDG s; wherein the load PL s to which the s-th node is connected is negative; the rest are positive;
Adding the load PL s connected with the s node, the distributed photovoltaic output PVG s connected with the s node, the distributed fan output WTG s connected with the s node, the conventional distributed power output PDG s connected with the s node and the distributed power value RPDG s to be planned connected with the s node to obtain a load recovery value PT s of the s node;
Step 3.1.2, determining a subnet where the distributed power supply DG is located by using the formula (2) with the maximum scene total load recovery value as a target:
In formula (2), N 3,num represents the total number of subnets of num in the current scene; PM j,num represents the total load recovery value of the jth subnet under the current scene num, and has:
In the formula (3), PL j,num is the absolute value of the sum of the loads of all the nodes in the j-th subnet under the current scene num; PG j,num is the absolute value of the sum of the power generation values of all nodes in the jth subnet under the current scene num and the power generation value of the power supply to be planned; PT s,num is the load recovery value of the s-th node under the current scene num; PT j,num is the sum of load recovery values of all nodes in the jth subnet under the current scene num;
Step 3.2, on the basis of determining the subnet where the distributed power supply is located, determining the optimal node where the distributed power supply is located by taking the shortest power transmission time from the black start unit to the unit to be recovered as a target:
Step 3.2.1, assuming that r units to be recovered are provided, b black start units, distributing all the units to be recovered to the black start units according to the aim that the sum of recovery time from all the units to be recovered to the black start units is shortest, wherein the grouping number is equal to the black start power supply number, and establishing a grouping model of the units to be recovered by using the formula (4) -formula (6):
s.t.v 1g,…,vcg,…,vbg =1 and v 1g、vcg、…、vbg are all non-negative integers (5)
In the formulas (4) - (6), [ omega 1g,…,ωtg,…,ωbg]T ] is a recovery time vector of the unit g to be recovered, and omega tg represents the time required for starting power to be sent from the black start unit t to the unit g to be recovered; [ v 1g,…,vcg,…,vbg ] represents the distribution relation between the unit g to be recovered and all the black start units, v cg represents the distribution relation between the unit c to be recovered and the unit g to be recovered, v eg represents the distribution relation between the unit e to be recovered and the unit g to be recovered, and when v cg =1 and v eg =0, the unit g to be recovered is distributed to the unit c to be recovered; c is not equal to e, e is not less than 1 and not more than b, c is not less than 1 and not more than b, and alpha is a proportionality coefficient;
step 3.2.2, enumerating node positions of the distributed power supply DGs in the sub-network, and obtaining optimal nodes of the distributed power supply DGs in the sub-network through a to-be-recovered unit model;
Step four, respectively counting the optimal nodes of each distributed power supply DG in all scenes contained by different disasters, sorting the nodes in descending order according to the occurrence times of each position, and taking the first few times of positions with the same number as the power supply planning number as the optimal planning positions of the power supplies under the corresponding disasters:
step 4.1, counting the optimal nodes of each distributed power supply DG in all scenes under any disaster, wherein the optimal planning node vector of the distributed photovoltaic power generation in the scene num is made to be The optimal planning node vector of the distributed fan is as followsOptimal planning node vector of conventional distributed power supply is/>Thus, an optimal planning node vector L PV of distributed photovoltaic power generation of all scenes under the current disaster, an optimal planning node vector L WT of a distributed fan and an optimal planning node vector L PDG of a conventional distributed power supply are obtained;
And 4.2, respectively ordering the occurrence times of the elements in the L PV,LWT,LPDG in a descending order, and selecting the elements with the corresponding quantity in the front ordering order as the optimal planning positions of the distributed power supply DG under the current disaster according to the planning quantity of the distributed power supply DG. Assuming that the planning number of the distributed fans is n1, taking the element of n1 before sequencing in L PV as the optimal planning position of the distributed fans under the disaster, wherein the steps of selecting the distributed photovoltaic and the conventional distributed power supply are similar;
and 4.3, repeating the steps 4.1-4.2 to obtain the optimal planning positions of the distributed power supply DG under different disasters.
Claims (1)
1. A multi-type distributed power supply two-stage planning method for improving restoring force of a power distribution network is characterized by comprising the following steps:
Step one, counting fault rates of distribution network lines under different disasters according to regional historical disaster data, and availability rates of distributed power sources DG of different types:
Step 1.1, counting the failure rate of the power distribution network lines under different disasters, wherein the failure rate Q= { Q 1,Q2…Qi…QN } of the power distribution network lines corresponding to different levels of typhoons and stormy weather is included, and Q i represents the failure rate of the ith line corresponding to different levels of typhoons and stormy weather; i=1, 2, …, N is the line number; n represents the number of lines;
Step 1.2, counting working states of distributed power sources DG of a power distribution network under different disasters, wherein the working states comprise working states of distributed photovoltaic power generation, a distributed fan and a conventional distributed power source under typhoons and stormy weather at different levels respectively; wherein, let the working state of the p-th distributed photovoltaic power generation be PV p, then the working state vector of N 1 distributed photovoltaic power generation be The working state of the w distributed fan is recorded as WT w; the operating state vector of the N 2 distributed fans is recorded as/>When wind comes, let PV p＝0,WTw =1; when a storm comes, let PV p＝1,WTw =0;
simulating and generating a damage scene of the circuit of the power distribution network after each type and each level of extreme disasters by using a Monte Carlo method;
step 2.1, defining scene simulation times under various disasters as NUM according to typhoons and stormy weather at different levels;
Step 2.2, randomly generating a random number R i which is uniformly distributed in the interval of [0,1] for the ith line;
Step2.3, determining the state S i of the ith line by using the formula (1), thereby obtaining a state vector s= (S 1,…Si…,SN) of the N lines:
In the formula (1), S i =1 indicates that the i-th line is in an operating state, and S i =0 indicates that the i-th line is in a failure state;
step 2.4, randomly sampling according to the output statistical characteristic of the distributed power supply to obtain a column vector of the distributed photovoltaic power generation Column vector of distributed fan outputColumn vector of conventional distributed power supply outputWherein/>Output representing p-th distributed photovoltaic power generation,/>Represents the output force of the w-th distributed fan,/>Representing the output of the v-th conventional distributed power supply, T representing the number of conventional distributed power supplies;
Step 2.5, obtaining a power output value PVG=PV×PV ABS of distributed photovoltaic power generation under extreme weather conditions by using the power state and the power output product, wherein the power output value WTG=WT×WT ABS of a distributed fan, and the power output value PDG=PDG ABS of a conventional distributed power supply;
Step 2.6, repeating NUM for 2.2-step 2.5, and respectively storing data of each simulation generation scene;
step three, respectively optimizing planning positions of distributed power generation, fans and conventional distributed power supplies according to each scene:
step 3.1, determining a subnet where the distributed power supply DG is located by taking the maximum scene total load recovery value as a target:
Step 3.1.1, acquiring load PL s connected with any s-th node, distributed photovoltaic output PVG s connected with the s-th node, distributed fan output WTG s connected with the s-th node and conventional distributed power output PDG s connected with the s-th node in the power distribution network;
Defining a distributed power value to be planned, which is connected with an s-th node, as RPDG s; wherein the load PL s to which the s-th node is connected is negative; the rest are positive;
Adding the load PL s connected with the s node, the distributed photovoltaic output PVG s connected with the s node, the distributed fan output WTG s connected with the s node, the conventional distributed power output PDG s connected with the s node and the distributed power value RPDG s to be planned connected with the s node to obtain a load recovery value PT s of the s node;
Step 3.1.2, determining a subnet where the distributed power supply DG is located by using the formula (2) with the maximum scene total load recovery value as a target:
In formula (2), N 3,num represents the total number of subnets of num in the current scene; PM j,num represents the total load recovery value of the jth subnet under the current scene num, and has:
In the formula (3), PL j,num is the absolute value of the sum of the loads of all the nodes in the j-th subnet under the current scene num; PG j,num is the absolute value of the sum of the power generation values of all nodes in the jth subnet under the current scene num and the power generation value of the power supply to be planned; PT s,num is the load recovery value of the s-th node under the current scene num; PT j,num is the sum of load recovery values of all nodes in the jth subnet under the current scene num;
Step 3.2, on the basis of determining the subnet where the distributed power supply is located, determining the optimal node where the distributed power supply is located by taking the shortest power transmission time from the black start unit to the unit to be recovered as a target:
Step 3.2.1, assuming that r units to be recovered are provided, b black start units, distributing all the units to be recovered to the black start units according to the aim that the sum of recovery time from all the units to be recovered to the black start units is shortest, wherein the grouping number is equal to the black start power supply number, and establishing a grouping model of the units to be recovered by using the formula (4) -formula (6):
s.t.v 1g,…,vcg,…,vbg =1 and v 1g、vcg、…、vbg are all non-negative integers (5)
In the formulas (4) - (6), [ omega 1g,…,ωtg,…,ωbg]T ] is a recovery time vector of the unit g to be recovered, and omega tg represents the time required for starting power to be sent from the black start unit t to the unit g to be recovered; [ v 1g,…,vcg,…,vbg ] represents the distribution relation between the unit g to be recovered and all the black start units, v cg represents the distribution relation between the unit c to be recovered and the unit g to be recovered, v eg represents the distribution relation between the unit e to be recovered and the unit g to be recovered, and when v cg =1 and v eg =0, the unit g to be recovered is distributed to the unit c to be recovered; c is not equal to e, e is not less than 1 and not more than b, c is not less than 1 and not more than b, and alpha is a proportionality coefficient;
step 3.2.2, enumerating node positions of the distributed power supply DGs in the sub-network, and obtaining optimal nodes of the distributed power supply DGs in the sub-network through a to-be-recovered unit model;
Step four, respectively counting the optimal nodes of each distributed power supply DG in all scenes contained by different disasters, sorting the nodes in descending order according to the occurrence times of each position, and taking the first few times of positions with the same number as the power supply planning number as the optimal planning positions of the power supplies under the corresponding disasters:
step 4.1, counting the optimal nodes of each distributed power supply DG in all scenes under any disaster, wherein the optimal planning node vector of the distributed photovoltaic power generation in the scene num is made to be Optimal planning node vector of distributed fan is/>Optimal planning node vector of conventional distributed power supply is/>Thus, an optimal planning node vector L PV of distributed photovoltaic power generation of all scenes under the current disaster, an optimal planning node vector L WT of a distributed fan and an optimal planning node vector L PDG of a conventional distributed power supply are obtained;
Step 4.2, respectively ordering the times of the elements in the L PV,LWT,LPDG in a descending order, and selecting the elements with the corresponding quantity, which are ordered in front, as the optimal planning positions of the distributed power supply DG under the current disaster according to the planning quantity of the distributed power supply DG;
and 4.3, repeating the steps 4.1-4.2 to obtain the optimal planning positions of the distributed power supply DG under different disasters.
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