CN110401229B - Power distribution network elastic lifting method considering supporting effect of micro energy network - Google Patents
Power distribution network elastic lifting method considering supporting effect of micro energy network Download PDFInfo
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- 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
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- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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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
- 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
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- 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
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- 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
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- 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
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- 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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- 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
- Y04S20/242—Home appliances
Abstract
A power distribution network elasticity improving method considering a micro energy network supporting effect comprises the following steps: giving an elasticity evaluation index of the power distribution network containing the micro energy network; providing a power failure management scheme which takes the micro energy network as a main body in the fault resisting and adapting stage of the power distribution network, namely establishing a micro energy network rolling scheduling model based on model prediction control; and establishing a fault recovery model taking the power distribution network as a main body in the fault recovery stage of the power distribution network. The invention can be suitable for the power distribution network containing multiple micro-energy networks under surface natural disasters such as typhoons, thunderstorms and the like, can be used for dealing with the fault characteristics of the power distribution network under extreme disasters such as large scale and long time, can improve the elasticity of the power distribution network in the whole process from disaster occurrence to disaster completion, fully utilizes two states of isolated island operation and grid-connected operation of the micro-energy networks, can play the complementary role of multiple energy sources in the micro-energy networks to the maximum extent, and provides a valuable technical basis for the elastic operation of the power distribution network under extreme disasters.
Description
Technical Field
The invention relates to an elastic lifting method for a power distribution network. In particular to an elasticity improving method of a power distribution network considering the supporting function of a micro-energy network, which is suitable for an urban power distribution network which is easy to suffer from extreme ground surface natural disasters such as typhoons, thunderstorms and the like.
Background
With global climate change, the occurrence of large-scale failure of power systems caused by extreme disasters is more frequent, and huge economic loss is caused. Traditionally, power distribution systems have been designed and operated based on reliability principles. However, high reliability power distribution systems may also experience large-scale failures in the face of extreme disasters resulting in long-term blackouts. Therefore, the concept of resiliency was introduced to evaluate the ability of the distribution network to reduce the losses caused by faults and to recover to a normal power supply state as soon as possible in extreme disasters.
In extreme disasters, due to faults of elements such as transmission lines, distribution lines or substations, the upper-level power grid cannot recover power supply to the interrupted load in time after the disaster is over. The distributed power supply and the micro-grid improve the redundancy of the system and improve the capacity of continuously supplying power to important loads.
At present, certain research has been developed at home and abroad on improving the elasticity of a power distribution network by using a distributed power supply and a micro-grid. A multi-agent system method considering the uncertainty of renewable energy has been proposed to solve the problem of power restoration and to deal with the uncertainty of the system by using an energy storage device as a flexible resource. A distributed multi-agent system method is provided, and key loads of a power distribution network are recovered by using a distributed power supply to operate in an island state. Some have proposed a two-stage method to formulate a fault recovery strategy for coordinating multiple distributed power supplies, in the first stage, an islanding is generated by using a heuristic algorithm, and in the second stage, a recovered load set is determined by using an iterative algorithm. A linear model has been proposed to form a microgrid with distributed power sources to maintain the power supply of critical loads. A layered power failure management scheme of multiple micro-grids is developed by people, and power transmission among the micro-grids under extreme disasters is coordinated. A method for recovering the power distribution network fault by using a grid-connected microgrid is provided, and the advantages and disadvantages of centralized control and distributed control of the microgrid are compared. The researches provide important theoretical bases for the elasticity improving method of the power distribution network considering the supporting effect of the micro energy network.
The power distribution network can sequentially go through three stages of resisting, adapting and fault recovery after an extreme disaster occurs, the existing research makes an operation strategy from a single stage, and the relevance among the stages is not considered. With the development of micro energy network technology, more and more micro energy networks are connected to a power distribution system, the coupling among multiple energy sources is deepened continuously, and the meaning of elasticity of the power distribution network is not limited to electric load but is expanded to the range of terminal energy requirements. Meanwhile, underground facilities usually have strong resistance to surface disasters such as typhoons, thunderstorms and the like, and natural gas pipeline systems are designed in a buried mode, so that a micro energy source network using a natural gas system as an energy source brings huge potential for improving the elasticity of a power distribution system, especially in areas not affected by frequent earthquake activities.
Disclosure of Invention
The invention aims to solve the technical problem of providing a power distribution network elasticity improving method considering the supporting effect of a micro energy network.
The technical scheme adopted by the invention is as follows: an elastic lifting method of a power distribution network considering the supporting effect of a micro energy network comprises the following steps:
1) Giving out the elasticity evaluation index of the power distribution network containing the micro energy network,
in a power distribution network containing a micro energy network, the system function at any moment is as follows:
in the formula, G represents a set of three types of loads of cold, heat and electricity; o represents a power distribution network and micro energy network user set; omega i The weight representing the user i is determined by the importance of the user; l is i,j (t) represents the j-class load size of the user i at time t, and L (t) represents the system function at time t.
Under any fault scene, the distribution network elasticity index AR is as follows:
in the formula, T represents the time from the occurrence of a disaster to the restoration of the power distribution network to a normal state; TL (t) represents the system function size at the time t when no fault exists; the formula represents the proportion of the system function maintaining normal state under extreme disasters.
2) The method comprises the steps of providing a power failure management scheme which takes a micro energy network as a main body at the stage of resisting and adapting to faults of a power distribution network, namely establishing a micro energy network rolling scheduling model based on model predictive control, wherein the rolling scheduling model comprises a target function which takes the minimum total cost in a scheduled scheduling period as a target function, a cold, heat and electricity power balance constraint, a micro source output constraint, an energy storage device capacity constraint, an energy storage device maximum charging constraint and a load reduction constraint;
3) The method comprises the steps of establishing a fault recovery model taking the power distribution network as a main body in the power distribution network fault recovery stage, and taking the minimum load reduction total value in the power distribution network fault recovery stage as an objective function, and performing resource recovery constraint, recovery time constraint, load node connection constraint, root node constraint, island connectivity constraint, line constraint, island radial constraint, island division constraint and power distribution network load control constraint.
The formula of the objective function with the minimum total cost in the planned scheduling period in the step 2) is as follows:
in the formula, C om Represents a maintenance cost; c fuel Represents a fuel cost; c env Represents an environmental cost; c LS Represents a load reduction cost; wherein the content of the first and second substances,
in the formula, c i Representing the unit load reduction cost of the user i; LS (least squares) i,j (t) represents the load reduction amount of class j for user i.
Step 2) the following steps:
(1) Cold, heat and electricity power balance constraint
In the formula, P PV (t)、P WT (t) and P GE (t) respectively representing the generated power of photovoltaic, wind power and a gas internal combustion engine; p EB (t) and P AC (t) respectively indicating the electric power consumption of the electric heating device and the electric refrigerating device; p ESS (t) represents the charge-discharge power of the energy storage device, wherein greater than 0 represents the charging state, and less than 0 represents the discharging state; q AC,c (t) and Q AR,c (t) the refrigeration capacities of the electric refrigeration device and the absorption refrigerator are respectively represented; q GE,h (t)、Q GB,h (t) and Q EB,h (t) heat generation amounts of the gas internal combustion engine, the gas boiler, and the electric heating apparatus are respectively represented; q AR,h (t) represents the heat consumption of the absorption refrigerator; l is e (t)、L c (t) and L h (t) respectively representing the total cooling, heating and power loads of the micro energy grid at the time t; LS (least squares) c (t)、LS h (t) and LS e (t) represents the total amount of reduction of the cooling, heating and power loads at time t, respectively; t is a unit of n Representing a scheduled scheduling period;
(2) Micro-source output constraints
In the formula, P k,min And P k,max Respectively the upper limit and the lower limit of the micro-source k output; r represents a micro-source set in the micro-energy source network; p k (t) represents the output of the micro source k at time t;
(3) Energy storage device capacity constraints
Wherein E (t) represents the capacity of the energy storage device at time t, E max And E min Respectively representing the upper and lower capacity limits of the energy storage device;
(4) Maximum charging constraint of energy storage device
In the formula, T ba Representing a micro energy network independent key load reduction time set; p ESS,max Represents a maximum charging power of the energy storage device; the constraint ensures that the micro energy network energy storage device is charged to the maximum extent at the moment of irrelevant key load reduction;
(5) Load shedding constraints
In the formula, epsilon i,e,c And ε i,e,h Respectively representing the user i electric cooling load correlation coefficient and the user i electric heating load correlation coefficient; LS (least squares) i,e (t)、LS i,c (t) and LS i,h (t) respectively representing the reduction amount of the cooling, heating and power loads of the user i at the time t; l is i,e (t)、L i,c (t) and L i,h (t) represents the magnitude of the cooling, heating, and power loads of the user i at time t.
The execution mode of the micro energy network rolling scheduling model based on the model predictive control in the step 2) is as follows:
(1) Predicting the future state of the system at the current time t and the state x (t), and making a scheduling plan at n future times by combining a micro energy network rolling scheduling model based on model prediction control;
(2) The dispatcher only executes the dispatching plan at the time t;
(3) And (4) at the time of t +1, updating the system state to be x (t + 1) according to the scheduling at the time of t, returning to the step (1) until the power distribution network enters a fault recovery stage, and ending the rolling scheduling.
Step 3) of taking the minimum load reduction total value at the fault recovery stage of the power distribution network as a target function
In the formula, ω h1 The weight of the power distribution network user h1 is determined by the importance degree of the user; PL z,t Representing the load capacity of a power distribution network node z at the moment t; Δ T represents the time set of the fault repair phase; d represents a power distribution network node set which does not recover power supply at the moment t; omega h2 The weight of the micro energy network user h2 is determined by the importance degree of the user; LS (least squares) n,h,t Representing the user h2 reduction amount in the nth micro energy network at the time t; IEM denotes a micro energy grid set; i represents each level load set.
Step 3) the
(1) Repairing resource constraints
In the formula, x e,f,cr Indicating whether the rush-repair team cr needs to rush-repair the fault element f from the fault element e; y is e,cr Indicating whether the fault element e is repaired by the rush repair team cr; BA represents a first-aid repair base set; DA represents a set of failed components; CR represents a first-aid repair team set; cr 0 The first-aid repair base where the first-aid repair team cr is located is shown; RESe represents the resources required to repair the failed element e; CAP (common Place Capacity) cr Representing the upper limit of resources which can be carried by the rush-repair team cr; constraint formulas (11) and (12) show that each emergency repair team only starts from the emergency repair base where the emergency repair team is located, and returns to the base after the task is finished; constraint equation (13) indicates that the rush repair team cannot stay where the failed component has been repaired; the constraint formula (14) shows that each fault can be repaired by only one rush-repair team; constraint formulas (15) (16) represent that the sum of the resources required by the fault elements repaired by any emergency repair team cannot exceed the upper limit of the resources that the team can carry;
(2) Repairing time constraints
In the formula, AT e,cr Indicating the time of arrival of the first-aid repair team cr at the faulty element e; f. of e,t Indicating whether the fault element e is repaired at the time t; HL (HL) e,t Indicating whether the faulty element e is in a faulty state at time t; m represents a large number; TRE e Indicating the time required to repair the failed element e; TTR e,f Representing the time required by the first-aid repair team from the failed component e to the failed component f; constraint formula (17) shows that the time of the first-aid repair team from the first-aid repair base is set to be 0; constraint equation (18) indicates that if the rush repair team cr does not repair the failed element e, AT e,cr Is 0; constraint equation (19) represents AT e,cr The time of the team cr reaching the fault e is equal to the sum of the time of the team reaching the last fault, the time of first-aid repair of the fault and the time of the distance between the two faults; constraint equation (20) represents f e,t If the repair time of the faulty element is not an integral multiple of the step length, f e,t Setting the time to be 1 at the nearest integral multiple step length of the time; constraint formula (21) represents that the failed component is updated to a non-failed state at the next time when the repair is completed;
(3) Load node connection constraints
In the formula, v z,m,t Whether the node z at the time t belongs to the island m or not is represented as 1, otherwise, the node z is represented as 0; n is a radical of IS Indicating the number of islands formed; b isRepresenting a node set in the power distribution network; the constraint means that the power distribution network is subdivided into a plurality of islands each time when an element is repaired, and each node can only belong to one island;
(4) Root node constraints
Wherein r represents a root node, N MDG Representing a node set where a micro energy source network serving as a main power source is located; IS represents a set of islands formed; the constraint indicates that a node where a micro energy source network serving as a main power source of an island is located must belong to the island;
(5) Island connectivity constraint
In the formula, theta z,m,t Representing a father node k set of a node z in the island m at the time t; the constraint means that if the node z belongs to the island m, at least one parent node of the node z belongs to the island m, and then a path from the node z to a main power supply exists;
(6) Line constraint
In the formula (I), the compound is shown in the specification,
representing time t by node z 1 And node z 2 The connection state of the lines of the head end node and the tail end node in the island m is 1, and the disconnection state is 0;
whether the line contains a tie switch or a section switch is represented, wherein the tie switch or the section switch contains 1 and does not contain 0; formula 1 in constraint equation (25) indicates that a line may be in island m only when the head and tail end nodes all belong to island mA connected state; equation 2 shows that when a line fails, the line must be in a disconnected state; formula 3 indicates that if the line does not contain a tie switch or a section switch and has no fault, the line is always in a connected state;
(7) Radial island restraint
The constraint represents that the interior of the island is communicated, and the difference between the number of nodes and the number of lines is 1 so as to ensure radial operation of the island;
(8) Islanding constraint
The constraint formula (27) shows that when the repair of each fault element is completed, the power distribution network can be subjected to island division again;
(9) Power distribution network load control constraints
In the formula, gamma z,m,t Whether the load of the node z at the time t recovers power supply in the island m or not is represented, the power supply is recovered to be 1, and the power supply is not recovered to be 0; chi-type food processing machine z,m,t And beta z,m,t Is a binary auxiliary variable; s z,m,t The state flag bit of the load switch is closed to be 1 and is disconnected to be 0; GE represents a node set directly connected with a superior power grid; PL z,t And QL z,t Respectively representing the active power and the reactive power of a node z; equation 1 and constraint equation (29) in constraint equation (28) indicate that the load at node z can be restored to power supply only in the following two cases: the first case is represented by equation 2 in the constraint equation (28), meaning the current nodeWhen the z belongs to an island and the load switch is closed, the z load of the node recovers power supply; the second condition is represented by formula 3 in the constraint formula (28), which means that when the node z is communicated with the superior power grid, the load of the node z is restored to supply power; equation 4 in the constraint equation (28) indicates that the load switch is not closed after being opened.
The elasticity improving method of the power distribution network considering the supporting effect of the micro energy network can be suitable for the power distribution network containing multiple micro energy networks under surface natural disasters such as typhoons, thunderstorms and the like, can be used for dealing with the fault characteristics of the power distribution network under extreme disasters such as large scale and long time, improves the elasticity of the power distribution network in the whole process from disaster occurrence to disaster completion, fully utilizes two states of island operation and grid-connected operation of the micro energy network, plays the complementary effect of multiple energy sources in the micro energy network to the maximum extent, and provides a valuable technical basis for the elastic operation of the power distribution network under extreme disasters.
Drawings
FIG. 1 is a functional curve of a power distribution network under an extreme disaster;
FIG. 2 is a flow chart of a method for improving the elasticity of a power distribution network by considering the supporting effect of a micro energy network according to the invention;
FIG. 3 is a power distribution network fault recovery model framework in accordance with the present invention;
FIG. 4 is a schematic diagram of a PG & E69 node topology;
FIG. 5 is a distribution network load factor;
fig. 6a is the output situation of the gas internal combustion engine, the gas boiler and the absorption chiller in the micro-energy network at the node 19 in fig. 4;
FIG. 6b is the output of the gas internal combustion engine, the gas boiler and the absorption chiller in the micro-energy grid at node 19 of FIG. 4;
FIG. 7a is a graph illustrating a reduction in the commercial load in the micro-energy network at node 19 of FIG. 4;
FIG. 7b is a graph illustrating load shedding by residents within the micro-energy network at node 19 of FIG. 4;
FIG. 8 is a comparison of the proposed power management scheme of the present invention and a fixed scheduling scheme during the distribution network defense and adaptation phase;
FIG. 9a shows the result of islanding of the power distribution network at 6-9 points in the power distribution network fault recovery phase;
FIG. 9b shows the result of the islanding of the power distribution network at 10-12 points in the fault recovery phase of the power distribution network;
FIG. 10 is a graph of lowest voltage at each node and highest load rate on each line during a fault recovery phase of the power distribution network;
fig. 11 is a comparison of the power distribution network system function curves in five scenarios.
Detailed Description
The following describes in detail an elasticity improvement method of a power distribution network considering a supporting function of a micro energy grid according to the present invention with reference to the following embodiments and accompanying drawings.
Firstly, combining a system function curve of the power distribution network under extreme disasters, constructing a power distribution network elasticity evaluation index from the perspective of terminal energy demand, and providing a research framework of a power distribution network overall process elasticity improvement strategy containing a micro energy network; secondly, in the stage of resisting and adapting to faults of the power distribution network, considering the correlation relation between the cold, heat and power loads of a user and the influence of the residual capacity of the energy storage device on the next stage, and providing a rolling power failure management scheme of the micro-energy network; thirdly, considering the supporting effect of the micro energy network on the power distribution network, establishing a power distribution network fault recovery model considering the emergency repair of fault elements and the load time sequence; finally, the effectiveness of the elasticity improvement strategy provided by the invention is verified through example analysis, and the improvement effect of different factors considered in the strategy on the elasticity of the power distribution network is compared.
As shown in fig. 2, the method for improving the elasticity of the power distribution network in consideration of the supporting effect of the micro energy network, provided by the invention, comprises the following steps:
1) Giving out the elasticity evaluation index of the power distribution network containing the micro energy network,
taking typhoon disasters as an example, after typhoon landing, a large number of power distribution network elements are gradually damaged under the action of wind power, and the fault scale is gradually enlarged to the maximum value; and after the typhoon passes through the boundary, the system starts fault recovery until the system is in a normal running state. A typical response curve of a power distribution network during a disaster is shown in figure 1.
In a power distribution network containing a micro-energy network, the system function should meet the requirements of three types of loads including cooling, heating and power, especially the requirements of key loads. The system function at any one time is as follows:
in the formula, G represents a set of three types of loads of cold, heat and electricity; o represents a power distribution network and micro energy network user set; omega i The weight representing the user i is determined by the importance of the user; l is i,j (t) represents the j-class load size of the user i at time t, and L (t) represents the system function at time t.
Under any fault scene, the distribution network elasticity index AR is as follows:
in the formula, T represents the time from the occurrence of a disaster to the restoration of the power distribution network to a normal state; TL (t) represents the system function size at the time t when no fault exists; the formula represents the proportion of the system function maintaining normal state under extreme disasters.
2) In the first stage, namely the power distribution network resisting and fault adapting stage, once a fault occurs to cause the micro energy network to be in power failure, the micro energy network immediately enters an island state, each micro source is dispatched to output power according to a preset power failure management scheme, and a multi-energy complementary means is utilized to supply power for a key load. At this time, the micro energy network is in a distributed control mode, and the main body is a micro energy network operator.
The invention provides a power failure management scheme taking a micro energy network as a main body in the stage of resisting and adapting to faults of a power distribution network, namely, a micro energy network rolling scheduling model based on model predictive control is established, and the scheme comprises the steps of taking the minimum total cost in a planned scheduling period as a target function, and performing cold, heat and power balance constraint, micro source output constraint, energy storage device capacity constraint, energy storage device maximum charging constraint and load reduction constraint; wherein the content of the first and second substances,
the formula taking the minimum total cost in the planning and scheduling period as an objective function is as follows:
in the formula, C om Represents a maintenance cost; c fuel Represents a fuel cost; c env Represents an environmental cost; c LS Represents a load reduction cost; wherein the content of the first and second substances,
in the formula, c i Representing the unit load reduction cost of the user i; LS (least squares) i,j (t) represents the class j load reduction amount of the user i.
The following steps:
(1) Thermal-electrical power balance constraint
In the formula, P PV (t)、P WT (t) and P GE (t) respectively representing the generated power of photovoltaic, wind power and a gas internal combustion engine; p is EB (t) and P AC (t) indicating the electric power of the electric heating device and the electric refrigerating device respectively; p ESS (t) represents the charge-discharge power of the energy storage device, wherein greater than 0 represents the charging state, and less than 0 represents the discharging state; q AC,c (t) and Q AR,c (t) the refrigeration capacities of the electric refrigeration device and the absorption refrigerator are respectively represented; q GE,h (t)、Q GB,h (t) and Q EB,h (t) heat generation amounts of the gas internal combustion engine, the gas boiler, and the electric heating apparatus, respectively; q AR,h (t) represents the heat consumption of the absorption refrigerator; l is e (t)、L c (t) and L h (t) respectively representing the total cooling, heating and power loads of the micro energy grid at the time t; LS (least squares) c (t)、LS h (t) and LS e (t) represents the total amount of reduction of cooling, heating and power loads at time t, respectively; t is a unit of n Representing a scheduled scheduling period;
(2) Micro source output constraint
In the formula, P k,min And P k,max Respectively the upper and lower limits of the micro-source k output; r represents a micro source set in the micro energy source network; p is k (t) represents the output of the micro-source k at time t;
(3) Energy storage device capacity constraints
Wherein E (t) represents the capacity of the energy storage device at time t, E max And E min Respectively representing the upper and lower capacity limits of the energy storage device;
(4) Maximum charging constraint for energy storage device
In the formula, T ba Representing a micro energy network independent key load reduction time set; p ESS,max Represents a maximum charging power of the energy storage device; the constraint ensures that the micro energy network energy storage device is charged to the maximum extent at the moment of irrelevant key load reduction;
(5) Load shedding constraints
In the formula, epsilon i,e,c And epsilon i,e,h Respectively representing the user i electric cooling load correlation coefficient and the user i electric heating load correlation coefficient; LS (least squares) i,e (t)、LS i,c (t) and LS i,h (t) respectively representing the reduction amount of the cooling, heating and power loads of the user i at the time t; l is i,e (t)、L i,c (t) and L i,h (t) represents the magnitude of the cooling, heating, and power loads of the user i at time t.
The execution mode of the micro energy network rolling scheduling model based on model predictive control is as follows:
(1) Predicting the future state of the system at the current moment t and the state x (t), and formulating a scheduling plan at n moments in the future by combining a micro energy grid rolling scheduling model based on model prediction control;
(2) The dispatcher only executes the dispatching plan at the time t;
(3) And (4) at the moment of t +1, updating the system state to be x (t + 1) according to the scheduling at the moment of t, returning to the step (1) until the power distribution network enters a fault recovery stage, and ending the rolling scheduling.
3) Establishing a fault recovery model taking the power distribution network as a main body in the fault recovery stage of the power distribution network
When the disaster is finished, the power distribution network operator finishes preparation work such as fault position and fault reason exploration, and then the power distribution network enters a second stage, namely a fault recovery stage. Due to the fact that the micro energy grid is provided with the controllable distributed power supply, the micro energy grid is considered to be used for supplying power to key loads of the power distribution network. By signing a protocol in advance, the micro energy network is in a centralized control mode at the stage, and the main body is a power distribution network. And the micro-energy network operator transmits the micro-source and load information to the power distribution network operator, and the power distribution network operator uniformly formulates a fault recovery scheme. The failure recovery scheme includes a main problem and a sub problem. The main problem is that the sum of the load recovery values in the stage is the maximum target, and an optimal fault first-aid repair scheme is formulated, namely, first-aid repair paths of each first-aid repair team in the power distribution network are arranged. After a main problem establishes a certain first-aid repair scheme, the fault state of each fault element at each moment is updated and transmitted to a subproblem, and the target function of the subproblem is calculated by the subproblem; in order to prevent frequent actions of the switches, the subproblems are divided into a plurality of time intervals according to the repair time of each fault, only at the initial moment of each time interval, namely the moment when a fault element is repaired, an island which takes the micro energy grid as a root node is formed by adjusting the state of the switches in the power distribution network, and the stable operation of the island is maintained by scheduling micro source output and a load control means in each time interval until all the fault elements are repaired, the system recovers to a normal state, and the objective function of the subproblems is the same as that of the main subproblems.
As shown in fig. 3, the fault recovery model based on the power distribution network is established in the power distribution network fault recovery stage, and includes a target function of minimizing load shedding total value in the power distribution network fault recovery stage, a resource repair constraint, a time repair constraint, a load node connection constraint, a root node constraint, an island connectivity constraint, a line constraint, an island radial constraint, an island division constraint and a power distribution network load control constraint. Wherein, the first and the second end of the pipe are connected with each other,
the minimum load reduction total value at the fault recovery stage of the power distribution network is taken as a target function
In the formula, omega h1 The weight of the power distribution network user h1 is represented and is determined by the importance degree of the user; PL z,t Representing the load capacity of a power distribution network node z at the moment t; Δ T represents the time set of the fault repair phase; d represents a power distribution network node set which does not recover power supply at the moment t; omega h2 The weight of the micro energy network user h2 is represented and is determined by the importance degree of the user; LS (least squares) n,h,t The user h2 reduction amount in the nth micro energy network at the moment t is represented; IEM denotes a micro energy grid set; i represents each level load set.
Said
(1) Repairing resource constraints
In the formula, x e,f,cr Indicating whether the rush-repair team cr needs to rush-repair the fault element f from the fault element e; y is e,cr Indicating whether the fault element e is repaired by the rush repair team cr; BA represents a first-aid repair base set; DA represents a set of failed components; CR represents a first-aid repair team set; cr is 0 The first-aid repair base where the first-aid repair team cr is located is shown; RESe represents the resources required to repair the failed element e; CAP (common Place Capacity) cr Representing the upper limit of resources which can be carried by the rush-repair team cr; constraint formulas (11) and (12) show that each emergency repair team only starts from the emergency repair base where the emergency repair team is located, and returns to the base after the task is finished; constraint equation (13) indicates that the rush repair team cannot stay where the failed component has been repaired; the constraint formula (14) shows that each fault can be repaired by only one rush-repair team; constraint formulas (15) (16) represent that the sum of the resources required by the fault elements repaired by any emergency repair team cannot exceed the upper limit of the resources that the team can carry;
(2) Repairing time constraints
In the formula, AT e,cr Indicating the time of arrival of the first-aid repair team cr at the faulty element e; f. of e,t Indicating whether the fault element e is repaired at the time t; HL (HL) e,t Indicating whether the failed element e is in a failed state at time t; m represents a large number; TRE e Indicating the time required to repair the failed element e; TTR e,f Representing the time required for rush-repairing the team from the failed element e to the failed element f; constraint formula (17) represents that the time of starting the emergency repair team from the emergency repair base is set to be 0; constraint equation (18) indicates that if the rush repair team cr does not repair the failed element e, AT e,cr Is 0; constraint equation (19) represents AT e,cr The time for the team cr to reach the fault e is equal to the sum of the time for the team to reach the last fault, the fault first-aid repair time and the distance time between two faults; constraint equation (20) represents f e,t If the repair time of the faulty element is not an integral multiple of the step length, f e,t Setting the time to be 1 at the nearest integral multiple step length of the time; constraint formula (21) represents that the failed component is updated to a non-failed state at the next time when the repair is completed;
(3) Load node connection constraints
In the formula, v z,m,t Indicating whether the node z at the time t belongs to the island m, wherein the node z belongs to 1, otherwise, the node z is 0; n is a radical of IS Indicating the number of islands formed; b represents a node set in the power distribution network; the constraint means that the power distribution network is divided into a plurality of isolated islands again every time when elements are repaired, and each node only belongs to one isolated island;
(4) Root node constraints
Wherein r represents a root node, N MDG Representing a node set where a micro energy network serving as a main power supply is located; IS represents the set of islands formed; the constraint indicates that a node where a micro energy source network serving as a main power source of an island is located must belong to the island;
(5) Island connectivity constraint
In the formula, theta z,m,t Representing a father node k set of a node z in an island m at the time t; the constraint means that if the node z belongs to the island m, at least one parent node of the node z belongs to the island m, and then a path from the node z to a main power supply exists;
(6) Line constraint
In the formula (I), the compound is shown in the specification,
representing time t by node z 1 And node z 2 The connection state of the line which is the head-end node in the island m is 1, and the disconnection state is 0;
whether the line contains a tie switch or a section switch is represented, wherein the tie switch or the section switch contains 1 and does not contain 0; formula 1 in constraint formula (25) indicates that the line can be in a connected state only when the head and tail end nodes all belong to the island m; equation 2 shows that when a line fails, the line must be in a disconnected state; formula 3 indicates that if the line does not contain a tie switch or a section switch and has no fault, the line must be in a connected state;
(7) Island radial constraint
The constraint represents that the interior of the island is communicated, and the difference between the number of nodes and the number of lines is 1 so as to ensure radial operation of the island;
(8) Islanding constraint
The constraint formula (27) shows that when the repair of each fault element is completed, the power distribution network can be subjected to island division again;
(9) Distribution network load control constraints
In the formula, gamma z,m,t Whether the load of the node z at the time t recovers power supply in the island m or not is represented, the power supply is recovered to be 1, and the power supply is not recovered to be 0; chi-type food processing machine z,m,t And beta z,m,t Is a binary auxiliary variable; s z,m,t The state flag bit of the load switch is closed to be 1 and is disconnected to be 0; GE represents a node set directly connected with a superior power grid; PL z,t And QL z,t Respectively representing the active power and the reactive power of a node z; equation 1 and constraint equation (29) in constraint equation (28) indicate that the load at node z can be restored to power supply only in the following two cases: the first case is represented by equation 2 in the constraint equation (28), meaning that when the node z belongs to the island and the load switch is closed, the node z load is restored to power supply; the second case is represented by formula 3 in the constraint formula (28), meaning that when the node z is connected with the upper-level power grid, the load of the node z is restored to power supply; equation 4 in constraint equation (28) indicates that the load switch is no longer closed after being opened;
the best examples are given below
A modified PG & E69 node power distribution system is used as shown in figure 4. Three micro energy networks are commonly connected in the power distribution network, three types of users including hospitals, businesses and residents are considered, wherein the hospitals are key loads, the businesses and the residents are non-key loads, the correlation coefficient of the cooling, heating and power loads of the business users is set to be 1, and the correlation coefficient of the cooling, heating and power loads of the hospitals and the residents is set to be 0. The distribution network has 7 key loads, the weight coefficients of the key loads and non-key loads are respectively 100 and 1, and the load coefficients are shown in fig. 5. The first-aid repair base 1 and the first-aid repair base 2 are respectively located at nodes 50 and 18 in the figure 4, wherein two first-aid repair teams are arranged on the first-aid repair base 1, one first-aid repair team is arranged on the first-aid repair base 2, and each first-aid repair team can carry resources for first-aid repair of 5 faults. Considering the scene of typhoon disasters in summer, the typhoon lasts from 18 points to 4 points on the next day, and 10 faults occur after 20 points. Assuming that the required repair time for each fault is 3 hours, the distance time between the faults is different from 10 minutes to 30 minutes, and 6 points enter the fault recovery phase.
1. And (4) a micro energy network power failure management scheme.
The micro energy grid power outage management scheme at the distribution grid node 19 shown in fig. 4 is taken as an example for explanation. The micro energy grid loses the power supply of the upper-level power grid due to the faults of the lines 14-15 at 20 points, the SOC of the energy storage device is 0.2 at the moment, and the planned time sequence is set to be 6 hours. The output and load shedding of each controllable micro-source during a power outage is shown in fig. 6a and 6 b. The power of the electric heating device and the reduction of the hospital load are both 0 during the power failure, and are not shown in the figure.
As can be seen from fig. 6a, 6b, 7a and 7b, by scheduling the output of each micro-source, the continuous energy supply of the critical load (hospital) is ensured by means of multi-energy complementation and non-critical load reduction. In the micro energy network, the electricity shortage condition is the most serious, so the output of the gas internal combustion engine is always in a high level, the output of an electric heating device and an electric refrigerating device is reduced, and a gas boiler and an absorption refrigerator are mainly used for supplying heat and cold. However, at 21-22, there is a small amount of power from the electric chiller, because of the large commercial cooling load demand during this period, and relying solely on the absorption chiller to provide cooling results in a reduction in cooling load, which in turn results in additional reductions in electrical and thermal loads. The energy storage device is charged at 20-23 points according to a constraint formula (8), the SOC reaches an upper limit of 0.9 at 24 points, and the redundancy of the power distribution network in the fault recovery stage can be enhanced. The micro energy grid preferentially reduces the residential electricity load due to the limitation of the constraint equation (9), and if the commercial electricity load is preferentially reduced, the commercial heat load and the commercial cold load are reduced in equal proportion, so that redundant load reduction is caused, and the power failure management cost of the micro energy grid is increased.
Fig. 8 is a functional comparison of a distribution network system of whether the micro-energy grid implements the power outage management scheme of the present invention during an outage. In the fixed scheduling strategy, the coupling between the energy sources is not considered, namely the output of the electric heating device and the electric refrigerating device is not considered, and simultaneously the load reduction is carried out according to the principle of preferentially supplying energy to the key load. In order to show more intuitiveness, the system function is normalized, and the original total load value is set as a unit 1. The comparison shows that the power failure management scheme provided by the invention can effectively improve the system function of the power distribution network.
2. Power distribution network fault recovery strategy
According to the power distribution network fault recovery strategy provided by the invention, a fault element rush-repair scheme of the power distribution network is shown in table 1, the general rule is that a path between a micro energy network and a key load is repaired firstly, then a path between the power distribution network and a superior power grid is repaired, and finally other fault elements are repaired, and the total time consumption is 13.21 hours. The islanding results of the distribution network 6-12 points are shown in fig. 9a and 9 b. During the island, all load switches of non-critical loads of the power distribution network are in a disconnected state, and the micro energy source network recovers the internal non-critical loads preferentially; and after 12 points, all loads recover power supply due to the fact that the path between the power distribution network and the superior power grid is repaired, and the power distribution network finishes the island state. The lowest voltage of each node and the highest load rate of each line of the power distribution network in the fault recovery stage are shown in fig. 10, the per-unit value of the lowest node voltage is 0.9384, the load rate of the highest line is 79.19%, and both the lowest voltage and the highest load rate meet the operation constraint of the power distribution network.
Table 1 breakdown element repair scheme
Five scenes are set to illustrate the rationality and effectiveness of the method, in the fault recovery stage, the supporting effect of the micro energy network on the power distribution network is not considered in the first scene, the method is adopted in the second scene, the traditional fault first-aid repair strategy is adopted in the third scene, namely, the shortest time is taken as the target for fault first-aid repair, the load time sequence is not considered in the fourth scene, and the constraint formula (8) is not considered in the first stage in the fifth scene. The normalized system function curve pairs of the power distribution network under each scene are shown in fig. 11, and the repair sequence of the fault elements and the system elasticity index are shown in table 2.
TABLE 2 repair sequence and elasticity index for each scene element
And comparing the first scene with the second scene. The support effect of the micro energy network on the power distribution network is not considered, so that the key load in the power distribution network is powered off for a long time, and the elasticity of the system is low.
And comparing the scene two with the scene three. Although all faults can be repaired and completed quickly according to the conventional repairing sequence, key loads, such as power failure of loads 9 and 32 in fig. 4 for a long time, can be caused due to failure in consideration of coordination with fault recovery, so that the system function curve at 10-12 points is much lower than that of the scenario two; meanwhile, in the third scene, the lines 2-3 are repaired at 15 points and 21 points, the system function can be recovered to be normal at 16 points, and the system is recovered for three hours two night later than the scene, so that the system elasticity is low.
And comparing the scene two with the scene four. And in the fourth scenario, a fault recovery strategy is made according to the maximum load without considering the load time sequence. At points 6-9, the micro energy grid at the distribution grid node 19 shown in fig. 4 cannot recover the power supply to the critical loads 13 and 22 at the same time, resulting in the power failure of the critical load 22, and therefore the system function curve is much lower than that of the scenario two. The repair sequence of the fault elements in the two scenarios is different, and in the fourth scenario, the path to the upper-level power grid is repaired preferentially compared with the repair lines 28-29, so that the critical load 32 is powered off for a longer time. The reason is that according to the maximum load optimization, the micro energy grid does not have enough electric quantity to maintain all the critical load power supplies in the period of large load at 10-12 points, so that the critical load is reduced, a path between the micro energy grid and an upper-level power grid is selected to be repaired firstly, and therefore the system function curve of scene four at 10-12 points is lower than that of scene two. At the same time, optimizing according to the maximum load also causes additional non-critical load reduction, further reducing the flexibility of the system.
And comparing the scene two with the scene five. In a fifth scenario, the initial SOC of the energy storage device in the second stage is 0.1, and at 6-9 points, since the key electrical load in the micro-energy network at the node 19 of the power distribution network shown in fig. 4 is large and the photovoltaic output is small, the key loads 13 and 22 of the power distribution network cannot be recovered simultaneously only by the gas internal combustion engine and the wind power, the small key load 22 cannot recover power supply at the time, so that the system function curve is lower than that in the second scenario, and the system elasticity is low.
Claims (6)
1. An elastic lifting method of a power distribution network considering the supporting effect of a micro energy network is characterized by comprising the following steps:
1) Giving out the elasticity evaluation index of the power distribution network containing the micro energy network,
in a power distribution network containing a micro energy network, the system function at any moment is as follows:
in the formula, G represents a set of three types of loads of cold, heat and electricity; o represents a power distribution network and micro energy network user set; omega i The weight of the user i is represented and is determined by the importance degree of the user; l is i,j (t) represents the j-class load size of the user i at the time t, and L (t) represents the system function at the time t;
under any fault scene, the distribution network elasticity index AR is as follows:
in the formula, T represents the time from the disaster to the restoration of the power distribution network to the normal state; TL (t) represents the system function size at the time t when no fault exists; the formula represents the proportion of the system function maintaining normal state under extreme disasters;
2) Providing a power failure management scheme taking a micro energy network as a main body at the stage of resisting and adapting to faults of the power distribution network, namely establishing a micro energy network rolling scheduling model based on model predictive control, wherein the power failure management scheme comprises a cold-hot-electricity power balance constraint, a micro-source output constraint, an energy storage device capacity constraint, an energy storage device maximum charging constraint and a load reduction constraint by taking the minimum total cost in a planned scheduling period as a target function;
3) The method comprises the steps of establishing a fault recovery model taking the power distribution network as a main body in the power distribution network fault recovery stage, and taking the minimum load reduction total value in the power distribution network fault recovery stage as an objective function, and performing resource recovery constraint, recovery time constraint, load node connection constraint, root node constraint, island connectivity constraint, line constraint, island radial constraint, island division constraint and power distribution network load control constraint.
2. The method for improving the elasticity of the power distribution network by considering the supporting effect of the micro energy network as claimed in claim 1, wherein the formula of the objective function of minimizing the total cost in the planned scheduling period in the step 2) is as follows:
in the formula, min f is an objective function; t is a unit of n Representing a planned scheduling period; c om Represents a maintenance cost; c fuel Represents a fuel cost; c env Represents an environmental cost; c LS Represents a load reduction cost; wherein, the first and the second end of the pipe are connected with each other,
in the formula, c i Representing the unit load reduction cost of the user i; LS (least squares) i,j (t) represents the load reduction amount of class j for user i.
3. The method for improving the elasticity of the power distribution network by considering the supporting effect of the micro energy network as claimed in claim 1, wherein the step 2) comprises:
(1) Cold, heat and electricity power balance constraint
In the formula, P PV (t)、P WT (t) and P GE (t) representing the generated power of photovoltaic, wind power and gas combustion engine respectively; p EB (t) and P AC (t) respectively indicating the electric power consumption of the electric heating device and the electric refrigerating device; p ESS (t) represents the charge-discharge power of the energy storage device, wherein greater than 0 represents the charging state, and less than 0 represents the discharging state; q AC,c (t) and Q AR,c (t) the refrigeration capacities of the electric refrigeration device and the absorption refrigerator are respectively represented; q GE,h (t)、Q GB,h (t) and Q EB,h (t) heat generation amounts of the gas internal combustion engine, the gas boiler, and the electric heating apparatus are respectively represented; q AR,h (t) represents the heat consumption of the absorption refrigerator; l is e (t)、L c (t) and L h (t) respectively representing the total amount of the cooling, heating and power loads of the micro energy network at the time t; LS (least squares) c (t)、LS h (t) and LS e (t) represents the total amount of reduction of cooling, heating and power loads at time t, respectively; t is n Representing a planned scheduling period;
(2) Micro source output constraint
In the formula, P k,min And P k,max Respectively the upper limit and the lower limit of the micro-source k output; r represents micro-energy netA set of micro-sources; p k (t) represents the output of the micro source k at time t;
(3) Energy storage device capacity constraints
Wherein E (t) represents the capacity of the energy storage device at time t, E max And E min Respectively representing the upper and lower capacity limits of the energy storage device;
(4) Maximum charging constraint of energy storage device
In the formula, T ba Representing a micro energy network independent key load reduction time set; p is ESS,max Represents a maximum charging power of the energy storage device; the constraint ensures that the micro energy network energy storage device is charged to the maximum extent at the moment of irrelevant key load reduction;
(5) Load shedding constraints
In the formula, epsilon i,e,c And epsilon i,e,h Respectively representing the user i electric cooling load correlation coefficient and the user i electric heating load correlation coefficient; LS (least squares) i,e (t)、LS i,c (t) and LS i,h (t) respectively representing the reduction amount of the cooling, heating and power loads of the user i at the time t; l is i,e (t)、L i,c (t) and L i,h (t) represents the magnitude of the cooling, heating, and power loads of the user i at time t.
4. The method for improving the elasticity of the power distribution network by considering the supporting effect of the micro energy network as claimed in claim 1, wherein the micro energy network rolling scheduling model based on the model predictive control in the step 2) is executed by:
(1) Predicting the future state of the system at the current time t and the state x (t), and making a scheduling plan at n future times by combining a micro energy network rolling scheduling model based on model prediction control;
(2) The dispatcher only executes the dispatching plan at the time t;
(3) And (4) at the time of t +1, updating the system state to be x (t + 1) according to the scheduling at the time of t, returning to the step (1) until the power distribution network enters a fault recovery stage, and ending the rolling scheduling.
5. The method for flexibly increasing the distribution network by considering the supporting effect of the micro energy network as claimed in claim 1, wherein the objective function of minimizing the total value of the load shedding in the fault recovery stage of the distribution network in the step 3) is
In the formula, ω h1 The weight of the power distribution network user h1 is determined by the importance degree of the user; PL z,t Representing the load of a power distribution network node z at the moment t; Δ T represents the time set of the fault repair phase; d represents a power distribution network node set which does not recover power supply at the moment t; omega h2 The weight of the micro energy network user h2 is determined by the importance degree of the user; LS (least squares) n,h,t The user h2 reduction amount in the nth micro energy network at the moment t is represented; IEM denotes a micro energy grid set; i represents each level load set.
6. The method for improving the elasticity of the power distribution network by considering the supporting effect of the micro energy grid as claimed in claim 1, wherein the step 3) (1) of repairing the resource constraint
In the formula, x e,f,cr Indicating whether the rush-repair team cr needs to rush-repair the fault element f from the fault element e; y is e,cr Indicating whether the fault element e is repaired by the rush repair team cr; BA represents a first-aid repair base set; DA represents a set of failed components; CR represents a first-aid repair team set; cr 0 The first-aid repair base where the first-aid repair team cr is located is shown; RESe represents the resources required to repair the failed element e; CAP (common Place Capacity) cr Representing the upper limit of resources which can be carried by the rush-repair team cr; constraint formulas (11) and (12) show that each emergency repair team only starts from the emergency repair base where the emergency repair team is located, and returns to the base after the task is finished; constraint equation (13) indicates that the rush repair team cannot stay where the failed component has been repaired; the constraint formula (14) shows that each fault can be repaired by only one rush-repair team; constraint formulas (15) and (16) show that the sum of the resources required by the fault elements repaired by any emergency repair team cannot exceed the upper limit of the resources which can be carried by the team;
(2) Repairing time constraints
In the formula, AT e,cr Representing the time for the first-aid repair team cr to arrive at the faulty element e; f. of e,t Indicating whether the fault element e is repaired at the time t; HL (HL) e,t Indicating whether the failed element e is in a failed state at time t; m represents a large number; TRE e Indicating the time required to repair the failed element e; TTR e,f Representing the time required for rush-repairing the team from the failed element e to the failed element f; constraint formula (17) represents that the time of starting the emergency repair team from the emergency repair base is set to be 0; constraint equation (18) indicates that if the rush repair team cr does not repair the failed element e, AT e,cr Is 0; constraint equation (19) represents AT e,cr The time of the team cr reaching the fault e is equal to the sum of the time of the team reaching the last fault, the time of first-aid repair of the fault and the time of the distance between the two faults; constraint equation (20) represents f e,t If the repair time of the faulty element is not an integral multiple of the step length, f e,t Setting the time to be 1 at the nearest integral multiple step length of the time; constraint formula (21) represents that the failed component is updated to a non-failed state at the next time when the repair is completed;
(3) Load node connection constraints
In the formula, v z,m,t Whether the node z at the time t belongs to the island m or not is represented as 1, otherwise, the node z is represented as 0; n is a radical of hydrogen IS Indicating the number of islands formed; b represents a node set in the power distribution network; the constraint means that the power distribution network is divided into a plurality of isolated islands again every time when elements are repaired, and each node only belongs to one isolated island;
(4) Root node constraints
Wherein r represents a root node, N MDG Representing a node set where a micro energy network serving as a main power supply is located; IS represents a set of islands formed; the constraint indicates that a node where a micro energy source network serving as a main power source of an island is located must belong to the island;
(5) Island connectivity constraint
In the formula, theta z,m,t Representing a father node k set of a node z in an island m at the time t; the constraint means that if the node z belongs to the island m, at least one parent node of the node z belongs to the island m, and then a path from the node z to a main power supply exists;
(6) Line constraint
In the formula (I), the compound is shown in the specification,
representing time t by node z 1 And node z 2 The connection state of the line which is the head-end node in the island m is 1, and the disconnection state is 0;
whether the line contains a tie switch or a section switch is represented, wherein the tie switch or the section switch contains 1 and does not contain 0; formula 1 in constraint formula (25) indicates that the line can be in a connected state only when the head and tail end nodes all belong to the island m; equation 2 shows that when a line fails, the line must be in a disconnected state; formula 3 indicates that if the line does not contain a tie switch or a section switch and has no fault, the line is always in a connected state;
(7) Radial island restraint
The constraint represents that the interior of the island is communicated, and the difference between the number of nodes and the number of lines is 1 so as to ensure radial operation of the island;
(8) Islanding constraint
The constraint formula (27) shows that when each fault element is repaired, the power distribution network is subjected to island division again;
(9) Power distribution network load control constraints
In the formula, gamma z,m,t Whether the load of the node z at the time t recovers power supply in the island m or not is represented, the power supply is recovered to be 1, and the power supply is not recovered to be 0; chi shape z,m,t And beta z,m,t Is a binary auxiliary variable; s z,m,t The status flag bit of the load switch is 1 when closed and 0 when open(ii) a GE represents a node set directly connected with a superior power grid; PL z,t And QL z,t Respectively representing the active power and the reactive power of a node z; equation 1 and equation 29 in constraint equation (28) indicate that the load at node z can be restored to power supply only in the following two cases: the first case is represented by equation 2 in the constraint equation (28), meaning that when node z belongs to the island and the load switch is closed, the node z load is restored to power supply; the second case is represented by formula 3 in the constraint formula (28), meaning that when the node z is connected with the upper-level power grid, the load of the node z is restored to power supply; equation 4 in constraint equation (28) indicates that the load switch is no longer closed after opening.
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