CN114819480A - Power distribution network distributed emergency dispatching method considering cooperation of electric public transport company - Google Patents

Power distribution network distributed emergency dispatching method considering cooperation of electric public transport company Download PDF

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CN114819480A
CN114819480A CN202210238932.4A CN202210238932A CN114819480A CN 114819480 A CN114819480 A CN 114819480A CN 202210238932 A CN202210238932 A CN 202210238932A CN 114819480 A CN114819480 A CN 114819480A
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distribution network
electric
power distribution
power
public transport
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武传涛
马云聪
陈岑
李飞宇
林湘宁
李正天
魏繁荣
柯彬
樊昌
李可竞
柯宏宇
熊玮
汪旸
王雄伟
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Huazhong University of Science and Technology
Central China Grid Co Ltd
Huangshi Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Huazhong University of Science and Technology
Central China Grid Co Ltd
Huangshi Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention relates to a power distribution network distributed emergency dispatching method considering cooperation of an electric bus company, which comprises the following steps of constructing a power distribution network self-dispatching submodel; constructing a self-scheduling sub-model of the electric public transport company for supporting the operation of the power distribution network by the electric public transport company; based on the power distribution network self-scheduling submodel and the electric public transport company self-scheduling submodel, constructing a power distribution network-electric public transport company-electric public transport vehicle cooperative emergency scheduling double-layer distributed model by adopting an augmented Lagrange method; and performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model by adopting a target cascade method, and solving by using a solver to obtain an optimal emergency dispatching strategy of the power distribution network and the electric public transport company. The emergency dispatching method is used for carrying out emergency dispatching from a distributed angle, can effectively protect data privacy of a power distribution network and an electric public transport company, and improves the optimized solving speed of dispatching problems through a double-layer distributed architecture.

Description

Power distribution network distributed emergency dispatching method considering cooperation of electric public transport company
Technical Field
The invention relates to the field of distribution network elastic recovery, in particular to a distribution network distributed emergency dispatching method considering cooperation of electric buses.
Background
In recent years, extreme weather such as earthquake, tsunami, hurricane and the like frequently occurs, physical damage is easily caused to power lines and key equipment, and further a blackout accident in a local area is induced. A large number of new energy source units such as wind power and photovoltaic and electrochemical energy storage equipment exist in a novel power system in the future. Meanwhile, a plurality of diesel generating sets are arranged in the power distribution network for the demand from time to time. These resources all provide possible conditions for the restoration of the operation of important loads in the distribution network after a blackout accident. However, extreme events can destroy several power lines at the same time, forcing the power distribution network to have to operate independently in the form of a plurality of isolated networks, resulting in that a large amount of new energy power generation cannot form an effective supply chain for important loads, and the system faces the dilemma that a part of isolated networks abandon wind and light and another part of isolated networks do not have available power.
In fact, a high-quality mobile energy storage resource, namely an electric bus, exists in cities. After a blackout accident occurs, the traffic demand of urban residents can be reduced to some extent, and under the condition, the electric public transport company can properly prolong the time interval of bus departure so as to draw out partial idle electric buses and support the elastic recovery of a power distribution network. The electric buses can be charged in the isolated network with abundant new energy and then driven to the isolated network with insufficient energy to discharge, so that the cross-space-time transfer of isolated network energy is realized, and a discrete energy link is formed.
At present, a large amount of research on the problem that electric vehicles participate in emergency dispatching of a power distribution network by scholars at home and abroad is carried out. The research now considers that the power distribution network can directly dispatch the electric bus, and the problem of emergency recovery dispatching of the power distribution network is optimized from the perspective of centralized integration. However, in fact, the electric buses are generally affiliated to the electric public transport company, the power distribution network does not directly control the electric buses, and the existing centralized integrated emergency dispatching mode is not suitable for real scenes. Generally, the distribution network and the electric public transport company belong to different benefit subjects, and have different benefit appeal and privacy protection requirements.
Disclosure of Invention
The invention aims to solve the technical problem of providing a power distribution network distributed emergency dispatching method considering the cooperation of electric buses, which can overcome the defects of the traditional centralized integrated emergency dispatching model, protect the privacy of the power distribution network and the electric buses, provide an optimal scheme for the cooperative emergency dispatching of the power distribution network and the electric buses, improve the elastic recovery capability of the power distribution network and ensure more load operation.
The technical scheme for solving the technical problems is as follows: a distributed emergency dispatching method for a power distribution network considering cooperation of electric buses comprises the following steps,
s1, calculating constraint conditions of the power distribution network based on a diesel generator, an energy storage system, a load shedding device and renewable energy in the power distribution network, and constructing a power distribution network self-scheduling submodel;
s2, according to the parameters of the electric buses and the schedulable quantity of the electric buses, considering the constraint conditions of the electric buses, constructing a self-scheduling submodel of the electric buses, which supports the operation of the power distribution network, of the electric buses;
s3, constructing a power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
s4, performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascading method, and solving by using a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
Based on the power distribution network distributed emergency dispatching method considering the cooperation of the electric public transport company, the invention also provides a power distribution network distributed emergency dispatching system considering the cooperation of the electric public transport company.
A distribution network distributed emergency dispatching system considering cooperation of electric buses comprises the following modules,
the power distribution network self-scheduling submodel building module is used for building a power distribution network self-scheduling submodel by considering power distribution network constraint conditions based on a diesel generator, an energy storage system, a load shedding and renewable energy sources in the power distribution network;
the electric public transport company self-scheduling submodel building module is used for calculating constraint conditions of electric public transport vehicles according to electric public transport vehicle parameters and the number of the electric public transport vehicles which can be scheduled of the electric public transport company and building an electric public transport company self-scheduling submodel for supporting the power distribution network to run by the electric public transport company;
the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model building module is used for building a power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
the distributed iterative optimization module is used for performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascade method, and solving by utilizing a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
Based on the distribution network distributed emergency dispatching method considering the cooperation of the electric buses, the invention also provides a computer storage medium.
A computer storage medium comprising a memory and a computer program stored in the memory, the computer program when executed by a processor implementing the method for distributed emergency dispatch of a power distribution network taking into account coordination of electric buses as described above.
The invention has the beneficial effects that: the invention provides a power distribution network distributed emergency dispatching method and system considering cooperation of electric public transport companies and a computer storage medium, aiming at a blackout accident scene caused by an extreme event. Specifically, in the invention, the power distribution network and the electric public transport company can obtain an optimal emergency dispatching strategy in a short time after a major power failure occurs so as to guide the operation of each electrical device and the electric public transport vehicle in the power distribution network, and more important load recovery can be ensured, thereby recovering huge economic loss and maintaining the stability of the society. And secondly, emergency scheduling is performed from a distributed angle, so that the data privacy of the power distribution network and the electric public transport company can be effectively protected, the method is more suitable for the practical situation, and the method has good engineering popularization value. Finally, the invention provides a double-layer distributed dispatching framework of the distribution network-electric buses and the electric buses, which takes into consideration that the solution process is prolonged due to the fact that the space-time constraints of the electric buses contain a large number of discrete variables, the optimization solution speed of the dispatching problem is improved through the double-layer distributed framework, the optimization efficiency is far higher than that of the single-layer distributed dispatching framework of the distribution network-electric buses (containing electric bus models), and the optimal emergency dispatching strategy can be obtained in a shorter time.
Drawings
Fig. 1 is a flow chart of a distributed emergency scheduling method for a power distribution network considering coordination of an electric public transport company according to the present invention;
FIG. 2 is a diagram of a power distribution network-electric bus company-electric bus double-layer distributed optimization framework;
FIG. 3 is a flow chart of a double-layer distributed optimization solution based on a target cascading method;
FIG. 4 is a schematic diagram of an improved IEEE 33 node power distribution network;
FIG. 5 is a time-varying coefficient graph of distribution network load power, wind power, and photovoltaic power;
fig. 6 is a block diagram of a distributed emergency dispatching system for a power distribution network in consideration of cooperation of an electric bus company.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a distributed emergency dispatching method for a power distribution network considering coordination of electric buses comprises the following steps,
s1, calculating constraint conditions of the power distribution network based on a diesel generator, an energy storage system, a load shedding device and renewable energy in the power distribution network, and constructing a power distribution network self-scheduling submodel;
s2, according to the parameters of the electric buses and the schedulable quantity of the electric buses, considering the constraint conditions of the electric buses, constructing a self-scheduling submodel of the electric buses, which supports the operation of the power distribution network, of the electric buses;
s3, constructing a power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
s4, performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascading method, and solving by using a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
S1-S4 are specifically described below:
S1：
before the self-scheduling submodel of the power distribution network is constructed, the parameters of the equipment in the power distribution network, including the node where the diesel generator is located and the maximum/minimum power, need to be collected
Figure BDA0003543486940000051
Node and rated capacity of energy storage system
Figure BDA0003543486940000052
Maximum charge and discharge power
Figure BDA0003543486940000053
Power node set
Figure BDA0003543486940000054
Rated voltage, bus node set
Figure BDA0003543486940000055
Power line assembly
Figure BDA0003543486940000056
And an impedance parameter x of the power line (i, j) ij 、r ij And the like. In addition, the power node load data within T hours of expected recovery time after power failure accident of the power distribution network is predicted based on a seasonal difference autoregressive moving average model according to load, wind power historical data and photovoltaic historical data
Figure BDA0003543486940000057
Wind power output data
Figure BDA0003543486940000058
Data relating to photovoltaic output
Figure BDA0003543486940000059
And after the relevant parameters and data are collected and predicted, a power distribution network self-scheduling sub-model can be constructed. The power distribution network self-scheduling submodel is specifically a first objective function which takes the constraint condition of the power distribution network into account and takes the minimum operation cost of the power distribution network as a target;
the first objective function comprises the fuel cost of the diesel generator, the charge and discharge breaking cost of the energy storage system and the load shedding loss cost;
the renewable energy comprises wind power and photovoltaic energy; the power distribution network constraint conditions comprise diesel generator operation constraint conditions, energy storage system operation constraint conditions, wind power and photovoltaic output constraint conditions, constraint conditions of active power and reactive power injected into each bus node of the power distribution network and power flow constraint conditions of the power distribution network.
Specifically, the expression of the first objective function is,
Figure BDA0003543486940000061
wherein the content of the first and second substances,
Figure BDA0003543486940000062
in order to account for the fuel costs of the diesel generator,
Figure BDA0003543486940000063
the cost is reduced for the charging and discharging of the energy storage system,
Figure BDA0003543486940000064
cost for said load shedding loss;
specifically, C ds For the total cost of operation of the power distribution network,
Figure BDA0003543486940000065
for the pre-recovery time after the power failure accident of the power distribution network,
Figure BDA0003543486940000066
is a collection of diesel generators in a power distribution network,
Figure BDA0003543486940000067
is a collection of energy storage systems in an electric distribution network,
Figure BDA0003543486940000068
for a collection of power nodes in a distribution network, p g,t And u g,t Respectively the active power and the start-stop state of the diesel generator g in the period t,
Figure BDA0003543486940000069
is the maximum active power of the diesel generator g,
Figure BDA00035434869400000610
and
Figure BDA00035434869400000611
the charging power and the discharging power of the energy storage system s during the period t respectively,
Figure BDA00035434869400000612
load shedding power at the power node i in the period t is taken as a decision variable; c. C g,1 And c g,2 All are the fuel cost coefficient, epsilon, of the diesel generator g s Is the unit of charge and discharge of the energy storage system s breaking cost coefficient,
Figure BDA00035434869400000613
the cost coefficients of the unit load shedding loss at the power node i are known quantities;
the expression of the diesel generator operation constraint is,
Figure BDA00035434869400000614
Figure BDA00035434869400000615
wherein the content of the first and second substances,
Figure BDA00035434869400000616
is the minimum active power of the diesel generator g, q g,t The reactive power of the diesel generator g in the t period is obtained; phi is a max Is the maximum power factor angle;
the expression of the energy storage system operation constraint condition is,
Figure BDA00035434869400000617
Figure BDA00035434869400000618
Figure BDA00035434869400000619
Figure BDA00035434869400000620
wherein the content of the first and second substances,
Figure BDA0003543486940000071
is the maximum energy storage power of the energy storage system s,
Figure BDA0003543486940000072
for the reactive power of the energy storage system s during the period t,
Figure BDA0003543486940000073
for rated apparent power, SOC, of the energy storage system s s,t Is the state of charge, η, of the energy storage system s during a period t s In order to achieve the charging and discharging efficiency of the energy storage system s,
Figure BDA0003543486940000074
is the rated capacity, SOC, of the energy storage system s min And SOC max Respectively a minimum charge state and a maximum charge state of the energy storage system;
the expression of the constraint conditions of the wind power output and the photovoltaic output is as follows,
Figure BDA0003543486940000075
Figure BDA0003543486940000076
wherein the content of the first and second substances,
Figure BDA0003543486940000077
and
Figure BDA0003543486940000078
the actual dissipated power for the fan w and the photovoltaic v respectively during the time period t,
Figure BDA0003543486940000079
and
Figure BDA00035434869400000710
respectively the maximum absorption power of the fan w and the photovoltaic v in the time period t; wind power and photovoltaic sets in a power distribution network are respectively
Figure BDA00035434869400000711
The expression of the constraint conditions of active power and reactive power injected into each bus node of the power distribution network is as follows,
Figure BDA00035434869400000712
Figure BDA00035434869400000713
wherein p is j,t And q is j,t Respectively injecting active power and reactive power at a bus node j in the period t;
Figure BDA00035434869400000714
and
Figure BDA00035434869400000715
respectively an active load and a reactive load at a bus node j in the period t;
Figure BDA00035434869400000716
load shedding power at a bus node j in a period t; phi is a j Is a load power factor angle at a bus node j;
Figure BDA00035434869400000717
and
Figure BDA00035434869400000718
the active power and the reactive power of the electric public transport company at the power distribution network access point m in the time period t are respectively, when the access of the electric automobile is not considered, the values are all 0, and when the access of the electric public transport company is considered, both variables are used as decision variables;
in addition to the above equipment operation constraints, power distribution network flow constraints also need to be considered. Because the power distribution network is usually a radial network, a Distflow power flow equation model is adopted, the expression of the power distribution network power flow constraint condition is,
Figure BDA00035434869400000719
Figure BDA00035434869400000720
Figure BDA00035434869400000721
Figure BDA0003543486940000081
Figure BDA0003543486940000082
Figure BDA0003543486940000083
wherein, P ij,t And Q ij,t Active and reactive power, l, respectively, transmitted by the line (i, j) during a period t ij,t For the square of the amplitude of the current transmitted by the line (i, j) during the period t, r ij Is the resistance of the line (i, j), x ij Is the reactance of the line (i, j), P jx,t And Q jx,t The active power and the reactive power transmitted by the line (x, j) in the period t are respectively, and the power node x is different from the power node i; v. of j,t Is the square of the voltage amplitude at the bus node j in the period t, v i,t Is the square of the voltage amplitude at power node i for a period t;
Figure BDA0003543486940000084
is the square of the minimum and maximum voltage amplitudes of power node i,
Figure BDA0003543486940000085
is the square of the maximum transmission current amplitude of the line (i, j); the line (i, j) is a transmission line between the power node i and the bus node j;
Figure BDA0003543486940000086
is a set of lines (i, j);
combining the first objective function and the constraint condition of the power distribution network, the compact form expression of the constructed power distribution network self-scheduling submodel is as follows:
Figure BDA0003543486940000087
S2：
before constructing the self-scheduling submodel of the electric public transport company, the model and the battery parameters of the electric public transport vehicles and the access point set of the power distribution network which belong to the electric public transport company need to be obtained
Figure BDA0003543486940000088
Adjusting the frequency of bus shifts to obtain an electric bus set capable of participating in emergency dispatching of a power distribution network
Figure BDA0003543486940000089
And after the related parameters are obtained, the self-scheduling submodel of the electric public transport company can be constructed. The self-scheduling submodel of the electric public transport company is specifically a second objective function which takes the constraint conditions of the electric public transport company into account and aims at minimizing the operation cost of the electric public transport company, wherein the operation cost mainly comes from the charge and discharge loss cost of a battery of the electric public transport company, namely: the second objective function comprises the charge and discharge loss cost of the battery of the electric bus;
the constraint conditions of the electric buses comprise space-time operation constraint conditions of the electric buses and energy constraint conditions of vehicle-mounted batteries of the electric buses.
Specifically, the expression of the second objective function is,
Figure BDA0003543486940000091
Figure BDA0003543486940000092
wherein, C ebc For the total operating cost of the electric public transport company,
Figure BDA0003543486940000093
for the operating cost of the electric bus n,
Figure BDA0003543486940000094
and
Figure BDA0003543486940000095
the charging power and the discharging power of the electric bus n at the access point m of the power distribution network in the period of t are respectively,
Figure BDA0003543486940000096
for a period of t, the driving power of the electric bus n, epsilon n The cost is reduced for charging and discharging the battery unit of the electric bus n;
Figure BDA0003543486940000097
is a collection of electric buses in an electric bus company,
Figure BDA0003543486940000098
the method comprises the steps of (1) collecting access points of the power distribution network;
the expression of the space-time operation constraint condition of the electric bus is as follows,
Figure BDA0003543486940000099
Figure BDA00035434869400000910
wherein, mu n,m,t The position state of the electric bus n is t time period, and when mu n,m,t When the time is 1, the time is t, the electric bus n is at the access point m of the power distribution network, otherwise, the time is tThe mobile bus n is not at the power distribution network access point m; alpha is alpha kr The number of time periods required for the electric bus to travel from the power distribution network access point k to the power distribution network access point r;
the electrical characteristics of the battery on the electric bus are basically the same as those of the common battery, the expression of the energy constraint condition of the battery on the electric bus is as follows,
Figure BDA00035434869400000911
Figure BDA00035434869400000912
Figure BDA00035434869400000913
Figure BDA00035434869400000914
Figure BDA00035434869400000915
Figure BDA00035434869400000916
Figure BDA0003543486940000101
wherein the content of the first and second substances,
Figure BDA0003543486940000102
is the maximum charge-discharge power, p, of the electric bus n drive The power consumed by the electric bus in a unit time period,
Figure BDA0003543486940000103
respectively the active power and the reactive power of the electric bus n at the access point m of the power distribution network in the time period t,
Figure BDA0003543486940000104
for the nominal apparent power of the battery of the electric bus n,
Figure BDA0003543486940000105
is the battery state of charge, η, of the electric bus n in the period of t n In order to increase the charge/discharge efficiency of the battery of the electric bus n,
Figure BDA0003543486940000106
the rated capacity of the battery of the electric bus n;
combining the second objective function and the constraint conditions of the electric buses, the compact form expression of the self-scheduling submodel of the electric buses is as follows:
Figure BDA0003543486940000107
S3：
as shown in fig. 2, the problem of cooperative scheduling between the power distribution network and the electric buses is divided into two layers of interaction problem of the power distribution network-the electric buses, and the upper layer of power distribution network and the electric buses are in interaction with each other through an interaction power matrix
Figure BDA0003543486940000108
Through interactive power matrix between lower electric public transport company and electric bus
Figure BDA0003543486940000109
And respectively scheduling sub-problems are optimized in a distributed cooperation mode, so that optimal emergency scheduling is realized. The power matrixes transferred to the power distribution network by the power distribution network and the power matrixes transferred to the power distribution network by the electric public transport company are defined as
Figure BDA00035434869400001010
Electric public transport company for transmitting electricityThe power matrixes transmitted to the electric public transport company by the electric public transport bus and the electric public transport bus are respectively
Figure BDA00035434869400001011
Based on the power distribution network self-scheduling submodel and the electric public transport company self-scheduling submodel, an augmented Lagrange method is adopted to construct a power distribution network-electric public transport company-electric public transport vehicle cooperative emergency scheduling double-layer distributed model, an expression of the power distribution network-electric public transport company-electric public transport vehicle cooperative emergency scheduling double-layer distributed model is as follows,
Figure BDA00035434869400001012
wherein the content of the first and second substances,
Figure BDA0003543486940000111
an interactive power matrix is respectively transmitted to the electric public transport company and the electric public transport company for the power distribution network, and
Figure BDA0003543486940000112
Figure BDA0003543486940000113
respectively transmitting the electric buses to the electric buses and transmitting the electric buses to the power matrixes of the electric buses,
Figure BDA0003543486940000114
Figure BDA0003543486940000115
lagrange multipliers, gamma, of the upper and lower layer interaction problem, respectively up 、γ lw Punishment coefficients of the upper layer and the lower layer of the interaction problem are respectively; specifically, the upper layer interaction problem is the interaction problem between the power distribution network and the electric public transport company, and the lower layer interaction problem is the interaction problem between the electric public transport company and the electric public transport vehicle.
S4:
Specifically, the step S4 is,
s41, performing distributed iterative optimization modeling on the power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model P3 by adopting a target cascade method, wherein the distributed iterative optimization modeling comprises a power distribution network subproblem P13, an electric public transport company subproblem P23, an electric public transport vehicle subproblem P33 and a Lagrange multiplier and penalty coefficient iteration mechanism;
s42, performing distributed iterative optimization on the distribution network subproblem P13, the electric bus subproblem P23 and the electric bus subproblem P33 based on the iteration times of the upper layer and the lower layer interaction problems and the Lagrange multiplier and penalty coefficient iteration mechanism to obtain a distribution network subproblem optimization model, an electric bus subproblem optimization model and an electric bus subproblem optimization model;
and S43, solving the power distribution network sub-problem optimization model, the electric bus company sub-problem optimization model and the electric bus sub-problem optimization model by using a solver to obtain the power distribution network and the optimal emergency dispatching strategy of the electric bus company.
Let the iteration number of the upper and lower layer interaction problem be k respectively 1 、k 2 And will k 1 、k 2 And the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model P3 is integrated.
As shown in fig. 3, in S42, specifically,
s421, initializing upper layer interaction problem parameters:
Figure BDA0003543486940000116
k 1 ＝1、
Figure BDA0003543486940000117
wherein the content of the first and second substances,
Figure BDA0003543486940000118
is an interactive power matrix transmitted to the power distribution network by the electric public transport company at the 0 th iteration,
Figure BDA0003543486940000121
is the lagrange multiplier of the upper layer interaction problem at iteration 0,
Figure BDA0003543486940000122
the penalty coefficient is the penalty coefficient of the upper layer interaction problem in the 0 th iteration;
s422, solving the following distribution network subproblem P13, and optimizing to obtain
Figure BDA0003543486940000123
Figure BDA0003543486940000124
Wherein the content of the first and second substances,
Figure BDA0003543486940000125
is the k-th 1 Lagrange multiplier of the upper layer interaction problem at 1 iteration,
Figure BDA0003543486940000126
Figure BDA0003543486940000127
are respectively k 1 The power distribution network transmits to the electric public transport company and the interactive power matrix transmitted to the power distribution network by the electric public transport company during the second iteration,
Figure BDA0003543486940000128
is the k-th 1 -a penalty factor for upper layer interaction problems at 1 iteration;
s423, initializing the parameters of the lower layer interaction problem:
Figure BDA0003543486940000129
k 2 ＝1、
Figure BDA00035434869400001210
wherein the content of the first and second substances,
Figure BDA00035434869400001211
is a power matrix transmitted to the electric bus by the electric bus at the 0 th iteration,
Figure BDA00035434869400001212
is the lagrange multiplier of the underlying interaction problem at iteration 0,
Figure BDA00035434869400001213
the penalty coefficient of the lower layer interaction problem in the 0 th iteration is obtained;
s424, solving the following subproblem P23 of the electric bus company, and optimizing to obtain
Figure BDA00035434869400001214
Figure BDA00035434869400001215
Wherein the content of the first and second substances,
Figure BDA00035434869400001216
is the k-th 2 Lagrange multipliers of the underlying interaction problem at 1 iteration,
Figure BDA00035434869400001217
is the k-th 2 -a power matrix delivered by the electric bus to the electric bus at 1 iteration,
Figure BDA00035434869400001218
is the k-th 2 The power matrix delivered by the electric bus company to the electric bus at the time of the second iteration,
Figure BDA00035434869400001219
is the k-th 2 -a penalty factor for the underlying interaction problem at 1 iteration;
s425, solving the following electric bus subproblem P33 and optimizing to obtain
Figure BDA00035434869400001220
Figure BDA00035434869400001221
S426, updating the lower Lagrange multiplier and the penalty coefficient, wherein the formula is as follows:
Figure BDA0003543486940000131
Figure BDA0003543486940000132
wherein, beta is a penalty factor iteration coefficient;
s427, judging whether the lower layer error meets the preset solving precision requirement xi lw I.e. by
Figure BDA0003543486940000133
Whether the result is true or not; if yes, go to S428, otherwise let k 2 ＝k 2 +1, jumping to execute the S424;
s428, updating the upper layer Lagrange multiplier and the penalty coefficient, wherein the formula is as follows:
Figure BDA0003543486940000134
Figure BDA0003543486940000135
s429, judging whether the upper layer error meets preset solving precision requirement xi up I.e. by
Figure BDA0003543486940000136
Whether the result is true; if yes, ending the optimization process, otherwise, making k 1 ＝k 1 +1, jumping to execute theS422。
Aiming at each sub-problem optimization model, the existing solver (such as a cardinal number solver, COPT, developed by China fir technology) can be used for solving to obtain the respective optimal scheduling strategy. After distributed optimization, an optimal scheduling strategy of the electric bus and the power distribution network can be obtained and used for guiding the recovery process of the power distribution network.
The present invention will be described below by taking the schematic diagram of the modified IEEE 33 node distribution network shown in fig. 4 as an example.
In the embodiment, an improved IEEE 33 node power distribution network is taken as a simulation object, and as shown in FIG. 4, three island networks are formed on the assumption that lines 0-1, 3-4 and 8-9 are damaged. The diesel generators are positioned at nodes 6 and 22, the installed capacity is 500kW, and the fuel cost coefficients are 1.875 yuan/kWh and 0.6 yuan/kWh respectively; the energy storage systems are positioned at nodes 2 and 15, the installation specifications are 500kW/1000kWh, the unit loss cost is 0.2 yuan/kWh, the charge-discharge efficiency is 95%, and the maximum and minimum charge states are 0.1 and 0.9; the installed positions and capacities of the wind power generation are nodes 11(200kW), 12(300kW), 13(450kW), 14(500kW), 18(350kW) and 19(450 kW); the photovoltaic installed positions and capacity are nodes 20(400kW), 21(400 kW), 23(500kW), 24(450kW), 26(300kW) and 27(350 kW). The load types of each node are commercial load (1, 2, 3, 7, 8, 24, 25, 30, 31, 32, 33), industrial load (4, 5, 6, 14, 15, 16, 17, 28, 29) and residential load (all other nodes), and the corresponding load shedding unit loss costs are 104.36 yuan/kWh, 247.53 yuan/kWh and 5.29 yuan/kWh, respectively. The time-varying coefficients of various types of loads and wind-solar power are shown in fig. 5.
Electric buses have sufficient electric buses. Suppose that after a major power failure accident occurs, an electric public transport company can draw and call 5 electric buses for emergency recovery after a disaster. The battery capacity of the electric bus is 300kWh, the rated power is 360kW, the unit driving power is 30kW, the charging and discharging loss is 95%, the unit charging and discharging cost is 0.4 yuan/kWh, and the minimum and maximum charge states are 0.1 and 0.9. There are three electric bus access points in the distribution network, nodes 10, 25 and 33, and an electric bus can reach from any access point to another access point within half an hour. When an accident occurs, the 10 electric vehicles are all nodes 10, the charge state is 0.8, and the charge state is not less than 0.3 when scheduling is finished. Assume an expected recovery time of 6 hours with 30 minutes as the minimum scheduled time. And carrying out modeling simulation analysis on the scene on a personal computer configured as i7-8700 CPU and 16GB memory based on a MATLAB platform and YALMIP modeling software.
Without considering the participation of electric buses, the power distribution network needs to cut off 4421.06kWh load, which causes 631714.28 yuan of economic loss. Load shedding mainly comes from island 2, and islands 1 and 3 do not have load shedding phenomenon. The island 3 has a large amount of wind power and photovoltaic resources, but is limited by no power transmission channel, and 6290.92kWh energy has to be abandoned. In this case, the operating cost of the distribution network is as high as 642314.18 yuan. When the electric public transport company is considered to participate in emergency recovery cooperatively, the power distribution network only needs to cut off 2828.05kWh load, the wind and light abandoning amount is also reduced to 3994.36kWh, part of new energy is consumed for power generation, and more load operation is guaranteed. Under the coordination condition of the electric buses, the operation cost of the power distribution network is reduced to 242770.95 yuan, and 399543.23 yuan of economic loss is saved in total. The electric bus can release the redundant electric energy to supply to the load, can also get the electricity from the island 3, and then drive to the island 2 to discharge, thereby forming a temporary power transmission channel between the islands 2 and 3 and ensuring more loads to operate. Therefore, the participation of the electric public transport company can effectively improve the elastic recovery capability of the power distribution network and ensure more loads to safely operate. The method can provide a reasonable and effective scheduling strategy for power distribution network emergency recovery in cooperation with the electric public transport company.
In addition, the two-layer distributed optimization has good convergence. In order to highlight the advantage of the calculation efficiency, a single-layer distributed optimization (between a power distribution network and an electric bus company, and an electric bus model is incorporated into the electric bus company model) is introduced for comparison and analysis. In the single-layer distributed optimization, only upper-layer interactive optimization and scheduling problems of electric buses, namely a mixed integer quadratic programming problem, are considered, and a large amount of time is needed for each solving, so that the solving time of the single-layer distributed optimization is greatly increased. In the example simulation, the time required for solving the single-layer distributed optimization exceeds 20000 seconds. However, the solution process can be significantly accelerated by the two-layer distributed optimization, and a reasonable scheduling strategy can be obtained only by 1530 seconds. If parallel computation is adopted, the solving time can be further shortened. Therefore, the method has obvious advantages in solving speed, and the optimal emergency dispatching strategy of the power distribution network can be obtained in a short time.
Based on the power distribution network distributed emergency dispatching method considering the cooperation of the electric public transport company, the invention also provides a power distribution network distributed emergency dispatching system considering the cooperation of the electric public transport company.
As shown in fig. 6, a distributed emergency dispatching system for a power distribution network considering coordination of electric buses comprises the following modules,
the power distribution network self-scheduling submodel building module is used for building a power distribution network self-scheduling submodel by considering power distribution network constraint conditions based on a diesel generator, an energy storage system, a load shedding and renewable energy sources in the power distribution network;
the electric public transport company self-scheduling submodel building module is used for calculating constraint conditions of electric public transport vehicles according to electric public transport vehicle parameters and the number of the electric public transport vehicles which can be scheduled of the electric public transport company and building an electric public transport company self-scheduling submodel for supporting the power distribution network to run by the electric public transport company;
the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model building module is used for building a power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
the distributed iterative optimization module is used for performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascade method, and solving by utilizing a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
Based on the power distribution network distributed emergency dispatching method considering the cooperation of the electric buses, the invention also provides a computer storage medium.
A computer storage medium comprising a memory and a computer program stored in the memory, the computer program when executed by a processor implementing the method for distributed emergency dispatch of a power distribution network taking into account coordination of electric buses as described above.
The invention provides a power distribution network distributed emergency dispatching method and system considering cooperation of electric public transport companies and a computer storage medium, aiming at a blackout accident scene caused by an extreme event. Specifically, in the invention, the power distribution network and the electric public transport company can obtain an optimal emergency dispatching strategy in a short time after a major power failure occurs so as to guide the operation of each electrical device and the electric public transport vehicle in the power distribution network, and more important load recovery can be ensured, thereby recovering huge economic loss and maintaining the stability of the society. And secondly, emergency scheduling is performed from a distributed angle, so that the data privacy of the power distribution network and the electric public transport company can be effectively protected, the method is more suitable for the practical situation, and the method has good engineering popularization value. Finally, the invention provides a double-layer distributed dispatching framework of the distribution network-electric buses and the electric buses, which takes into consideration that the solution process is prolonged due to the fact that the space-time constraints of the electric buses contain a large number of discrete variables, the optimization solution speed of the dispatching problem is improved through the double-layer distributed framework, the optimization efficiency is far higher than that of the single-layer distributed dispatching framework of the distribution network-electric buses (containing electric bus models), and the optimal emergency dispatching strategy can be obtained in a shorter time.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A power distribution network distributed emergency dispatching method considering cooperation of electric buses is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, calculating constraint conditions of the power distribution network based on a diesel generator, an energy storage system, a load shedding device and renewable energy in the power distribution network, and constructing a power distribution network self-scheduling submodel;
s2, according to the parameters of the electric buses and the schedulable quantity of the electric buses, considering the constraint conditions of the electric buses, constructing a self-scheduling submodel of the electric buses, which supports the operation of the power distribution network, of the electric buses;
s3, constructing a power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
s4, performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascading method, and solving by using a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
2. The distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 1, wherein: in S1, the power distribution network self-scheduling submodel is specifically a first objective function that takes into account the power distribution network constraint condition and aims at minimizing the power distribution network operation cost;
the first objective function comprises the fuel cost of the diesel generator, the charge and discharge breaking cost of the energy storage system and the load shedding loss cost;
the renewable energy comprises wind power and photovoltaic energy; the power distribution network constraint conditions comprise diesel generator operation constraint conditions, energy storage system operation constraint conditions, wind power and photovoltaic output constraint conditions, constraint conditions of active power and reactive power injected into each bus node of the power distribution network and power flow constraint conditions of the power distribution network.
3. The distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 2, wherein: the expression of the first objective function is,
Figure FDA0003543486930000021
wherein, C ds The total running cost of the power distribution network is obtained;
Figure FDA0003543486930000022
for the pre-recovery time after the power failure accident of the power distribution network,
Figure FDA0003543486930000023
is a collection of diesel generators in a power distribution network,
Figure FDA0003543486930000024
is a collection of energy storage systems in an electric distribution network,
Figure FDA0003543486930000025
the method comprises the steps of (1) collecting power nodes in a power distribution network; p is a radical of g,t And u g,t Respectively the active power and the start-stop state of the diesel generator g in the period t,
Figure FDA0003543486930000026
is the maximum active power of the diesel generator g,
Figure FDA0003543486930000027
and
Figure FDA0003543486930000028
the charging power and the discharging power of the energy storage system s during the period t respectively,
Figure FDA0003543486930000029
load shedding power at a power node i in a period t; c. C g,1 And c g,2 All are the fuel cost coefficient, epsilon, of the diesel generator g s Is the unit of charge and discharge of the energy storage system s breaking cost coefficient,
Figure FDA00035434869300000210
a unit load shedding loss cost coefficient at a power node i;
the expression of the diesel generator operation constraint condition is,
Figure FDA00035434869300000211
Figure FDA00035434869300000212
wherein the content of the first and second substances,
Figure FDA00035434869300000213
is the minimum active power of the diesel generator g, q g,t The reactive power of the diesel generator g in the t period is obtained; phi is a max Is the maximum power factor angle;
the expression of the energy storage system operation constraint condition is,
Figure FDA00035434869300000214
Figure FDA00035434869300000215
Figure FDA00035434869300000216
Figure FDA00035434869300000217
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00035434869300000218
is the maximum energy storage power of the energy storage system s,
Figure FDA00035434869300000219
for the reactive power of the energy storage system s during the period t,
Figure FDA00035434869300000220
for rated apparent power, SOC, of the energy storage system s s,t Is the state of charge, η, of the energy storage system s during a period t s In order to achieve the charging and discharging efficiency of the energy storage system s,
Figure FDA00035434869300000221
is the rated capacity, SOC, of the energy storage system s min And SOC max Respectively a minimum charge state and a maximum charge state of the energy storage system;
the expression of the constraint conditions of the wind power output and the photovoltaic output is as follows,
Figure FDA00035434869300000222
Figure FDA00035434869300000223
wherein the content of the first and second substances,
Figure FDA0003543486930000031
and
Figure FDA0003543486930000032
the actual dissipated power for the fan w and the photovoltaic v respectively during the time period t,
Figure FDA0003543486930000033
and
Figure FDA0003543486930000034
maximum of fan w and photovoltaic v respectively in time period tThe power is absorbed;
the expression of the constraint conditions of active power and reactive power injected into each bus node of the power distribution network is as follows,
Figure FDA0003543486930000035
Figure FDA0003543486930000036
wherein p is j,t And q is j,t Respectively injecting active power and reactive power at a bus node j in the period t;
Figure FDA0003543486930000037
and
Figure FDA0003543486930000038
respectively an active load and a reactive load at a bus node j in the period t;
Figure FDA0003543486930000039
load shedding power at a bus node j in a period t; phi is a j Is a load power factor angle at a bus node j;
Figure FDA00035434869300000310
and
Figure FDA00035434869300000311
respectively the active power and the reactive power of the electric public transport company at the power distribution network access point m in the time period t;
the expression of the power flow constraint condition of the power distribution network is as follows,
Figure FDA00035434869300000312
Figure FDA00035434869300000313
Figure FDA00035434869300000314
Figure FDA00035434869300000315
Figure FDA00035434869300000316
Figure FDA00035434869300000317
wherein, P ij,t And Q ij,t Active and reactive power, l, respectively, transmitted by the line (i, j) during a period t ij,t For the square of the amplitude of the current transmitted by the line (i, j) during the period t, r ij Is the resistance of the line (i, j), x ij Is the reactance of line (i, j); v. of j,t Is the square of the voltage amplitude at the bus node j in the period t, v i,t Is the square of the voltage amplitude at power node i for a period t;
Figure FDA00035434869300000318
is the square of the minimum and maximum voltage amplitudes of power node i,
Figure FDA00035434869300000319
is the square of the maximum transmission current amplitude of the line (i, j); the line (i, j) is a transmission line between the power node i and the bus node j;
Figure FDA00035434869300000320
is a set of lines (i, j);
the compact form of the power distribution network self-scheduling submodel is expressed as follows:
Figure FDA00035434869300000321
4. the distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 3, wherein: in S2, the self-scheduling submodel of the electric public transportation company is specifically a second objective function that takes into account the constraint conditions of the electric public transportation company and aims at minimizing the operation cost of the electric public transportation company;
the second objective function comprises the charge and discharge loss cost of the battery of the electric bus;
the constraint conditions of the electric buses comprise space-time operation constraint conditions of the electric buses and energy constraint conditions of vehicle-mounted batteries of the electric buses.
5. The distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 4, wherein the method comprises the following steps: the expression of the second objective function is,
Figure FDA0003543486930000041
Figure FDA0003543486930000042
wherein, C ebc For the total operating cost of the electric public transport company,
Figure FDA0003543486930000043
for the operating cost of the electric bus n,
Figure FDA0003543486930000044
and
Figure FDA0003543486930000045
the charging power and the discharging power of the electric bus n at the access point m of the power distribution network in the period of t are respectively,
Figure FDA0003543486930000046
for a period of t, the driving power of the electric bus n, epsilon n The cost is reduced for charging and discharging the battery unit of the electric bus n;
Figure FDA0003543486930000047
is a set of electric buses which can participate in emergency dispatching of a power distribution network in an electric bus company,
Figure FDA0003543486930000048
the method comprises the steps of (1) collecting access points of the power distribution network;
the expression of the space-time operation constraint condition of the electric bus is as follows,
Figure FDA0003543486930000049
Figure FDA00035434869300000410
wherein, mu n,m,t The position state of the electric bus n is t time period, and when mu n,m,t When the time is 1, the electric bus n is at the power distribution network access point m in the time period t, otherwise, the electric bus n is not at the power distribution network access point m in the time period t; alpha is alpha kr The time period required for the electric bus to travel from the power distribution network access point k to the power distribution network access point r is counted;
the expression of the constraint condition of the battery energy on the electric bus is as follows,
Figure FDA0003543486930000051
Figure FDA0003543486930000052
Figure FDA0003543486930000053
Figure FDA0003543486930000054
Figure FDA0003543486930000055
Figure FDA0003543486930000056
Figure FDA0003543486930000057
wherein the content of the first and second substances,
Figure FDA0003543486930000058
is the maximum charge-discharge power, p, of the electric bus n drive The power consumed by the electric bus in a unit time period,
Figure FDA0003543486930000059
respectively the active power and the reactive power of the electric bus n at the access point m of the power distribution network in the time period t,
Figure FDA00035434869300000510
for the nominal apparent power of the battery of the electric bus n,
Figure FDA00035434869300000511
is the battery state of charge, η, of the electric bus n in the period of t n In order to increase the charge/discharge efficiency of the battery of the electric bus n,
Figure FDA00035434869300000512
the rated capacity of the battery of the electric bus n;
the compact form expression of the self-scheduling submodel of the electric public transport company is as follows:
Figure FDA00035434869300000513
6. the distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 5, wherein: in the step S3, the expression of the distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatch double-layer distributed model is,
Figure FDA00035434869300000514
wherein the content of the first and second substances,
Figure FDA00035434869300000515
an interactive power matrix is respectively transmitted to the electric public transport company and the electric public transport company for the power distribution network, and
Figure FDA00035434869300000516
respectively transmitting the electric buses to the electric buses and transmitting the electric buses to the power matrixes of the electric buses,
Figure FDA00035434869300000517
lagrange multiplier, gamma, of the upper and lower layer interaction problem, respectively up 、γ lw Are respectively asPunishment coefficients of the upper layer and the lower layer of the interaction problem; specifically, the upper layer interaction problem is the interaction problem between the power distribution network and the electric public transport company, and the lower layer interaction problem is the interaction problem between the electric public transport company and the electric public transport vehicle.
7. The distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 6, wherein: specifically, the step S4 is,
s41, performing distributed iterative optimization modeling on the power distribution network-electric public transport company-electric public transport vehicle cooperative emergency dispatching double-layer distributed model P3 by adopting a target cascade method, wherein the distributed iterative optimization modeling comprises a power distribution network subproblem P13, an electric public transport company subproblem P23, an electric public transport vehicle subproblem P33 and a Lagrange multiplier and penalty coefficient iteration mechanism;
s42, performing distributed iterative optimization on the distribution network subproblem P13, the electric bus subproblem P23 and the electric bus subproblem P33 based on the iteration times of the upper layer and the lower layer interaction problems and the Lagrange multiplier and penalty coefficient iteration mechanism to obtain a distribution network subproblem optimization model, an electric bus subproblem optimization model and an electric bus subproblem optimization model;
and S43, solving the power distribution network sub-problem optimization model, the electric bus company sub-problem optimization model and the electric bus sub-problem optimization model by using a solver to obtain the power distribution network and the optimal emergency dispatching strategy of the electric bus company.
8. The distributed emergency dispatching method for the power distribution network considering cooperation of the electric buses as claimed in claim 7, wherein: let the iteration number of the upper and lower layer interaction problem be k respectively 1 、k 2 (ii) a Specifically, the step S42 is,
s421, initializing upper layer interaction problem parameters:
Figure FDA0003543486930000061
k 1 ＝1、
Figure FDA0003543486930000062
s422, solving the following distribution network subproblem P13, and optimizing to obtain
Figure FDA0003543486930000063
Figure FDA0003543486930000064
S423, initializing the parameters of the lower layer interaction problem:
Figure FDA0003543486930000065
k 2 ＝1、
Figure FDA0003543486930000066
s424, solving the following subproblem P23 of the electric bus company, and optimizing to obtain
Figure FDA0003543486930000067
Figure FDA0003543486930000071
S425, solving the following electric bus subproblem P33 and optimizing to obtain
Figure FDA0003543486930000072
Figure FDA0003543486930000073
S426, updating the lower Lagrange multiplier and the penalty coefficient, wherein the formula is as follows:
Figure FDA0003543486930000074
Figure FDA0003543486930000075
wherein, beta is a penalty factor iteration coefficient;
s427, judging whether the lower layer error meets the preset solving precision requirement xi lw I.e. by
Figure FDA0003543486930000076
Whether the result is true or not; if yes, go to S428, otherwise let k 2 ＝k 2 +1, jumping to execute the S424;
s428, updating the upper layer Lagrange multiplier and the penalty coefficient, wherein the formula is as follows:
Figure FDA0003543486930000077
Figure FDA0003543486930000078
s429, judging whether the upper layer error meets preset solving precision requirement xi up I.e. by
Figure FDA0003543486930000079
Whether the result is true or not; if yes, ending the optimization process, otherwise, making k 1 ＝k 1 +1, jump execution said S422.
9. The utility model provides a take into account electric bus company collaborative distribution network distributed emergency dispatch system which characterized in that: comprises the following modules which are used for realizing the functions of the system,
the power distribution network self-scheduling submodel building module is used for calculating power distribution network constraint conditions based on a diesel generator, an energy storage system, a load shedding device and renewable energy in the power distribution network and building a power distribution network self-scheduling submodel;
the electric public transport company self-scheduling submodel building module is used for calculating constraint conditions of electric public transport vehicles according to electric public transport vehicle parameters and the number of the electric public transport vehicles which can be scheduled of the electric public transport company and building an electric public transport company self-scheduling submodel for supporting the power distribution network to run by the electric public transport company;
the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model building module is used for building a power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting an augmented Lagrange method based on the power distribution network self-dispatching submodel and the electric public transport company self-dispatching submodel;
the distributed iterative optimization module is used for performing distributed iterative optimization on the power distribution network-electric public transport company-electric public transport vehicle collaborative emergency dispatching double-layer distributed model by adopting a target cascade method, and solving by utilizing a solver to obtain the optimal emergency dispatching strategy of the power distribution network and the electric public transport company.
10. A computer storage medium, characterized in that: comprising a memory and a computer program stored in said memory, said computer program, when executed by a processor, implementing a method of distributed emergency dispatch of a power distribution network taking into account the cooperation of electric buses according to any of claims 1 to 8.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117422206A (en) * 2023-12-18 2024-01-19 中国科学技术大学 Method, equipment and storage medium for improving engineering problem decision and scheduling efficiency
CN117422206B (en) * 2023-12-18 2024-03-29 中国科学技术大学 Method, equipment and storage medium for improving engineering problem decision and scheduling efficiency

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