CN114825399A - Distributed energy emergency scheduling method and system for power distribution network considering mobile energy storage - Google Patents

Distributed energy emergency scheduling method and system for power distribution network considering mobile energy storage Download PDF

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CN114825399A
CN114825399A CN202210238910.8A CN202210238910A CN114825399A CN 114825399 A CN114825399 A CN 114825399A CN 202210238910 A CN202210238910 A CN 202210238910A CN 114825399 A CN114825399 A CN 114825399A
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power
distribution network
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mobile energy
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周德智
武传涛
许璇
随权
黄启元
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 distributed energy emergency scheduling method and a distributed energy emergency scheduling system for a power distribution network considering mobile energy storage, wherein the method comprises the steps of constructing a mobile energy storage constraint model; constructing a power distribution network constraint model; constructing a distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking a distribution network constraint model and a mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the distribution network; and carrying out iterative solution on the distribution type energy emergency scheduling model of the power distribution network by adopting a self-adaptive penalty coefficient-alternative direction multiplier method to obtain an optimal emergency scheduling scheme of the power distribution network. The method takes the participation of the mobile energy storage resource into consideration, and can effectively improve the flexibility, economy and reliability of emergency power supply of the urban power distribution network; meanwhile, a distributed optimization framework and a self-adaptive penalty coefficient-alternative direction multiplier method are adopted, economic optimality and algorithm convergence are considered, and benign development of the urban power distribution network is promoted.

Description

Distributed energy emergency scheduling method and system for power distribution network considering mobile energy storage
Technical Field
The invention relates to the technical field of power system operation and energy planning, in particular to a distributed energy emergency scheduling method and system for a power distribution network considering mobile energy storage.
Background
Due to the small-probability high-risk extreme disaster event, a large-area and long-time power failure accident can be caused to the urban power distribution network, so that each micro-grid in the urban power distribution network enters an isolated network operation state. In this scenario, relying solely on distributed power generation and storage systems may not meet the power supply requirements of some critical loads, limited by the shortages of the energy storage system and the diesel engine or the stored electricity and fuel. Therefore, the mobile energy storage is flexibly applied to carry out emergency power supply on all power loads of the power distribution network, the deficiency of power resources in each micro-grid is made up, and the method becomes a research hotspot.
At present, a great deal of research is carried out by domestic and foreign scholars aiming at the problem that mobile energy storage resources participate in emergency scheduling of urban distribution network energy. However, the existing studies have two disadvantages. The method has the advantages that firstly, the researched power distribution network is used as a whole for centralized optimization, and objective conditions of different operation main bodies of each microgrid and the requirement for important power load privacy information protection are omitted. The existing research often fails to improve the problem and design a distributed optimal scheduling framework, so that the proposed method and model are difficult to be practically applied. Secondly, in the process of performing distributed optimization scheduling by applying a traditional cross direction multiplier (ADMM), the selection of the penalty coefficient has a large influence on the convergence of the algorithm, the small penalty coefficient generally causes slow convergence of the penalty coefficient, and the large penalty coefficient easily causes slow convergence of the decision variable, so that the convergence of the standard ADMM algorithm is seriously influenced if the penalty coefficient is not properly selected.
Disclosure of Invention
The invention aims to solve the technical problem of providing a distributed energy emergency scheduling method and system for a power distribution network, which take movable energy storage into account, can overcome the defects of a traditional model, provide a more flexible emergency power supply scheme for an urban power distribution network, simultaneously give consideration to economic optimality and algorithm convergence, and promote the benign development of the urban power distribution network.
The technical scheme for solving the technical problems is as follows: a distributed energy emergency dispatching method for a power distribution network considering mobile energy storage comprises the following steps,
s1, constructing a mobile energy storage constraint model according to the mobile energy storage discrete energy flow characteristic and the traffic flow characteristic;
s2, constructing a power distribution network constraint model consisting of a diesel engine, wind power, photovoltaic and energy storage systems and containing mobile energy storage access according to an IEEE power distribution network topological structure;
s3, constructing a distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the distribution network;
and S4, carrying out iterative solution on the distribution network distributed energy emergency dispatching model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain an optimal emergency dispatching scheme of the distribution network.
Based on the power distribution network distributed energy emergency dispatching method considering the mobile energy storage, the invention also provides a power distribution network distributed energy emergency dispatching system considering the mobile energy storage.
A distributed energy emergency dispatching system for a power distribution network considering mobile energy storage comprises the following modules,
the mobile energy storage constraint model building module is used for building a mobile energy storage constraint model according to the discrete energy flow characteristic and the traffic flow characteristic of mobile energy storage;
the distribution network constraint model building module is used for building a distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and comprises a mobile energy storage access according to an IEEE distribution network topological structure;
the power distribution network distributed energy emergency scheduling model building module is used for building a power distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the power distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the power distribution network;
and the iterative solution module is used for iteratively solving the power distribution network distributed energy emergency scheduling model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain the optimal emergency scheduling scheme of the power distribution network.
Based on the power distribution network distributed energy emergency scheduling method considering the mobile energy storage, the invention also provides a computer storage medium.
A computer storage medium comprising a memory and a computer program stored in the memory, which when executed by a processor, implements a method for distributed energy conforming to a power distribution network involving mobile energy storage as described above.
The invention has the beneficial effects that: the invention relates to a distributed energy emergency scheduling method and system for a power distribution network considering mobile energy storage and a computer storage medium, wherein a mobile energy storage constraint model is constructed by combining a discrete energy flow model and a traffic flow model under the situation that mobile energy storage resources participate in the emergency scheduling of the energy of an urban power distribution network; based on an IEEE power distribution network topological structure, a power distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and comprises a mobile energy storage access is constructed; therefore, the distributed energy emergency scheduling model of the urban distribution network considering the mobile energy storage resources is established by taking the minimum running cost of the distribution network as a target and taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions; considering that the convergence characteristic of the traditional alternating direction multiplier method is seriously influenced by the iteration coefficient, the penalty coefficient is adaptively adjusted in the iteration process by adopting an adaptive penalty coefficient-alternating direction multiplier method, and the urban distribution network distributed energy emergency scheduling model is subjected to iterative solution by means of a gurobi commercial solver to obtain an optimal emergency scheduling scheme of the urban distribution network. Therefore, compared with the traditional urban distribution network energy emergency scheduling scheme, the urban distribution network energy emergency scheduling method takes the participation of mobile energy storage resources into consideration, can effectively improve the flexibility, economy and reliability of the urban distribution network emergency power supply, adopts a distributed optimization framework, effectively realizes the privacy information protection of important loads of the urban distribution network, provides an optimal strategy for the urban distribution network energy emergency scheduling, can better accord with the development direction of a future urban distribution system, and promotes the benign development of the urban distribution network.
Drawings
Fig. 1 is a flowchart of a distributed energy emergency scheduling method for a power distribution network with consideration of mobile energy storage according to the present invention;
FIG. 2 is a schematic diagram of an improved IEEE 33 node power distribution network system topology employed by an embodiment;
FIG. 3 is a wind power output, photovoltaic output and load prediction graph used in the embodiments;
FIG. 4 is a schematic diagram of a simulated mobile energy storage optimal scheduling path;
fig. 5 is a schematic diagram of a simulated microgrid load supply strategy;
FIG. 6 is a schematic diagram of node voltage variation under different simulation scenarios;
FIG. 7 is a diagram illustrating iterative convergence under different algorithms;
fig. 8 is a block diagram illustrating a distributed energy emergency dispatching system of a power distribution network with consideration of mobile energy storage according to the present invention.
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 energy emergency dispatching method for a power distribution network considering mobile energy storage comprises the following steps,
s1, constructing a mobile energy storage constraint model according to the mobile energy storage discrete energy flow characteristic and the traffic flow characteristic;
s2, constructing a power distribution network constraint model consisting of a diesel engine, wind power, photovoltaic and energy storage systems and containing mobile energy storage access according to an IEEE power distribution network topological structure;
s3, constructing a distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the distribution network;
and S4, carrying out iterative solution on the distribution network distributed energy emergency dispatching model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain an optimal emergency dispatching scheme of the distribution network.
S1-S4 are specifically described below:
and S1, constructing a mobile energy storage constraint model according to the mobile energy storage discrete energy flow characteristics and the traffic flow characteristics.
After a power failure accident occurs, after receiving fault early warning, the urban distribution network emergency command center needs to quickly perform emergency response, make a decision and distribute local adjustable emergency resources to generate a current optimal urban distribution network energy emergency scheduling scheme. The method comprises the following steps that for a local adjustable and controllable mobile energy storage resource, the dual characteristics of discrete energy flow and traffic flow are considered, and an obtained mobile energy storage constraint model consisting of the discrete energy flow and the traffic flow is shown in formulas (1) to (4); specifically, the mobile energy storage constraint model comprises a mobile energy storage traffic flow constraint model and a mobile energy storage discrete energy flow constraint model;
the mobile energy storage traffic flow constraint model is as follows,
Figure BDA0003543483920000051
wherein, the initial time of the power failure accident is set as 0 time period,
Figure BDA0003543483920000052
all mobile energy storage sets are provided, and n is a mobile energy storage variable;
Figure BDA0003543483920000053
the method comprises the steps that a node set in a power distribution network is represented, and i and j are node variables;
Figure BDA0003543483920000054
h is a time interval variable for the time interval set of emergency scheduling after the power failure accident;
Figure BDA0003543483920000055
marking a bit variable for a 0-1 position of the mobile energy storage, specifically indicating whether the mobile energy storage n reaches the node i in the h time period, if so, then
Figure BDA0003543483920000056
If not, then
Figure BDA0003543483920000057
θ ji Represents the actual distance between node j and node i, Δ H represents the length of each time period;
the mobile energy storage discrete energy flow constraint model is as follows,
Figure BDA0003543483920000061
Figure BDA0003543483920000062
Figure BDA0003543483920000063
wherein,
Figure BDA0003543483920000064
are all constant and are all provided with constant values,
Figure BDA0003543483920000065
Figure BDA0003543483920000066
respectively the maximum active discharge power, the maximum active charge power and the maximum reactive power of the power battery in the mobile energy storage n,
Figure BDA0003543483920000067
respectively the maximum active discharge power, the maximum active charge power and the maximum reactive power of the energy storage battery in the movable energy storage n,
Figure BDA0003543483920000068
representing the active discharge power of the power battery in the mobile energy storage n in the h period,
Figure BDA0003543483920000069
respectively representing the active power absorbed by the power battery in the mobile energy storage n through the node i and the reactive power absorbed by the power battery in the h time period,
Figure BDA00035434839200000610
respectively representing the active power absorbed, the released active power and the absorbed reactive power of the energy storage battery in the mobile energy storage n through the node i in the h period,
Figure BDA00035434839200000611
are all constants and respectively represent the capacities of the power battery and the energy storage battery in the mobile energy storage n,
Figure BDA00035434839200000612
Figure BDA00035434839200000613
are all constant and are all provided with the same power,
Figure BDA00035434839200000614
respectively representing the initial state of charge, the minimum value of the state of charge and the maximum value of the state of charge of the power battery in the mobile energy storage n,
Figure BDA00035434839200000615
Figure BDA00035434839200000616
respectively representing the initial charge state and the minimum charge state of the energy storage battery in the mobile energy storage nA maximum value of the state of charge,
Figure BDA0003543483920000071
are all constant and are all provided with the same power,
Figure BDA0003543483920000072
respectively representing the charge-discharge efficiency coefficient and the active capacity of the power battery in the mobile energy storage n,
Figure BDA0003543483920000073
respectively representing the charge-discharge efficiency coefficient and the active capacity of the energy storage battery in the mobile energy storage n,
Figure BDA0003543483920000074
respectively representing the charge states of the power battery in the mobile energy storage n in the H time period and the H time period,
Figure BDA0003543483920000075
the charge states of the energy storage battery in the mobile energy storage n in the H time period and the H time period are respectively.
Specifically, formula (1) represents a mobile energy-storage traffic flow constraint comprising a location uniqueness constraint and a transfer time constraint, and formulas (2) to (4) represent mobile energy-storage discrete energy flow constraints, and variable quantity of mobile energy-storage 0-1 location flag bit
Figure BDA0003543483920000076
And (3) limiting, wherein the formula (2) represents the active power and reactive power constraints of the mobile energy storage power battery and the energy storage battery, the formula (3) represents the capacity constraints of the mobile energy storage power battery and the energy storage battery, and the formula (4) represents the charge state constraint of the mobile energy storage.
And S2, constructing a power distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and contains mobile energy storage access according to the IEEE power distribution network topological structure.
In an optimal urban distribution network energy emergency scheduling scheme after a power failure accident occurs, aiming at local adjustable and controllable access of diesel engines, wind power, photovoltaic and energy storage system resources and mobile energy storage, based on an IEEE distribution network topological structure, a distribution network constraint model which is composed of the diesel engines, the wind power, the photovoltaic and the energy storage system and contains the mobile energy storage access is obtained as shown in the following formulas (5) - (8); specifically, the power distribution network constraint model comprises a diesel engine operation constraint model, a wind power and photovoltaic operation constraint model, an energy storage system operation constraint model and a power distribution network power flow constraint model;
the diesel engine operation constraint model is that,
Figure BDA0003543483920000077
wherein,
Figure BDA0003543483920000078
the output running state variable of the diesel engine configured for the node i in the power distribution network in the h time period, if the diesel engine configured for the node i in the power distribution network is in the running state in the h time period, the diesel engine is in the running state
Figure BDA0003543483920000079
If the diesel engine configured by the node i in the power distribution network is in a shutdown state in the h period of time, the diesel engine is in a shutdown state
Figure BDA0003543483920000081
Starting and stopping state variables of the diesel engine configured for the node i in the power distribution network in the h-1 time period, if the diesel engine configured for the node i in the power distribution network is in the starting action in the h-1 time period, then the diesel engine is started and stopped
Figure BDA0003543483920000082
If the diesel engine configured by the node i in the power distribution network is in stop action in the h-1 time period, the diesel engine is started to stop acting
Figure BDA0003543483920000083
Are all constants and respectively represent the minimum active power, the maximum active power, the minimum reactive power, the maximum reactive power and the maximum active power change rate of the diesel engine configured at the node i,
Figure BDA0003543483920000084
the active power and the reactive power injected by the diesel engine configured at the node i in the h period are represented;
the wind power and photovoltaic operation constraint model is as follows,
Figure BDA0003543483920000085
wherein,
Figure BDA0003543483920000086
the maximum output of the wind power and the photovoltaic power which are respectively configured for the node i in the h time period,
Figure BDA0003543483920000087
Figure BDA0003543483920000088
actual output of wind power and photovoltaic power configured for the node i in the h time period respectively;
the operation constraint model of the energy storage system is as follows,
Figure BDA0003543483920000089
wherein,
Figure BDA00035434839200000810
are all constants and respectively represent the initial charge state, the minimum charge state, the maximum charge state, the minimum charge power, the maximum charge power, the minimum discharge power, the maximum discharge power, the capacity, the charge-discharge efficiency coefficient and the active capacity of the energy storage system configured by the node i,
Figure BDA00035434839200000811
respectively representing the active power absorbed by the energy storage system through the node i, the released active power and the released reactive power in the h period,
Figure BDA00035434839200000812
respectively representing node i configurationThe charge states of the energy storage system in H and H periods;
the power flow constraint model of the power distribution network is as follows,
Figure BDA0003543483920000091
wherein,
Figure BDA0003543483920000092
representing a set of line configurations in the distribution network, and (i, m) representing lines formed by nodes i and m
Figure BDA0003543483920000093
r ji 、x ji 、
Figure BDA0003543483920000094
Are all constant and respectively represent the line
Figure BDA0003543483920000095
Resistance, reactance, maximum transmission capacity, square value of maximum transmission complex current,
Figure BDA0003543483920000096
respectively representing the square value of the minimum complex voltage and the square value of the maximum complex voltage of the node i,
Figure BDA0003543483920000097
active power demand, reactive power demand, P, of load configured for node i during period h ji,h 、Q ji,h 、l ji,h Respectively representing lines
Figure BDA0003543483920000098
Square value of active transmission power, reactive transmission power, transmission current, v, in h period i,h Square value v representing complex voltage of node i in h period j,h Representing the squared value of the complex voltage of node j over period h.
Specifically, formula (5) represents the operation constraints of the diesel engine configured by the node i, formula (6) represents the operation constraints of the wind power and the photovoltaic configured by the node i, formula (7) represents the operation constraints of the energy storage system configured by the node i, including the state of charge constraints, the power constraints and the capacity constraints, and formula (8) represents the operation constraints based on the Distflow power flow equation, including the power balance constraints, the voltage amplitude constraints, the transmission capacity constraints, the transmission current constraints and the transmission power constraints of the branch.
And S3, constructing a distribution network distributed energy emergency scheduling model considering the mobile energy storage resources by taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the distribution network.
The objective function aiming at minimizing the operating cost of the distribution network is,
Figure BDA0003543483920000099
Figure BDA0003543483920000101
wherein,
Figure BDA0003543483920000102
intrinsic configuration operating costs for node i during period h, including energy storage system operating costs
Figure BDA0003543483920000103
Operating cost of firewood-mixing machine
Figure BDA0003543483920000104
Operating cost of the mobile energy storage n in h period, including power battery operating cost
Figure BDA0003543483920000105
And energy storage battery operating costs
Figure BDA0003543483920000106
p G 、a i 、b i 、c i 、
Figure BDA0003543483920000107
The values are constants and respectively represent unit fuel oil price, diesel engine start-stop cost coefficient, diesel engine running cost coefficient, diesel engine power generation cost coefficient, energy storage system breaking cost coefficient, power battery breaking cost coefficient and energy storage battery breaking cost coefficient;
the distributed energy emergency dispatching model of the power distribution network is,
Figure BDA0003543483920000108
Figure BDA0003543483920000109
s.t.(1)-(8)
and S4, carrying out iterative solution on the distribution network distributed energy emergency dispatching model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain an optimal emergency dispatching scheme of the distribution network.
The S4 specifically includes the following S41-S43,
and S41, converting the distribution network distributed energy emergency dispatching model into an augmented Lagrange form.
The distribution network distributed energy emergency dispatching model has the form of augmented Lagrange,
Figure BDA0003543483920000111
Figure BDA0003543483920000112
wherein,
Figure BDA0003543483920000113
in order to enlarge the Lagrange function, x and y respectively represent nodes in the power distribution networki and optimization decision variables of the mobile energy storage n; both alpha and beta are given penalty coefficients;
Figure BDA0003543483920000114
respectively representing active power of the mobile energy storage n injected into the node i in the h time period and active power of the node i collected from the mobile energy storage n in the h time period;
Figure BDA0003543483920000115
respectively representing reactive power of the mobile energy storage n injected into the node i in the h time period and reactive power of the node i collected from the mobile energy storage n in the h time period; delta is a Lagrange multiplier vector between the active power actual interaction variable and the active power virtual interaction variable,
Figure BDA0003543483920000116
is a Lagrange multiplier vector between the reactive power actual interaction variable and the reactive power virtual interaction variable, specifically delta n,i,h Representing a Lagrange multiplier vector between reactive power injected into the node i by the mobile energy storage n in the h period and reactive power collected from the mobile energy storage n by the node i in the h period,
Figure BDA00035434839200001110
and a Lagrange multiplier vector representing the reactive power injected into the node i by the mobile energy storage n in the h period and the reactive power collected from the mobile energy storage n by the node i in the h period.
And S42, decomposing the energy emergency scheduling problem into a node optimization subproblem and a mobile energy storage optimization subproblem based on the distribution network distributed energy emergency scheduling model in the augmented Lagrange form.
The expression of the node optimization sub-problem is,
Figure BDA0003543483920000118
the expression of the mobile energy storage optimization sub-problem is,
Figure BDA0003543483920000119
wherein K ∈ {1, 2., K } is the number of iterations,
Figure BDA0003543483920000121
for the optimization decision variable set of the node i in the power distribution network,
Figure BDA0003543483920000122
set of optimized decision variables, x, for mobile energy storage n i (k +1) is an optimization decision variable x of a node i in the power distribution network during the (k +1) th iteration i Optimization decision variable, y, for node i in a power distribution network n (k +1) is the optimization decision variable of the mobile energy storage n in the k +1 iteration, y n Optimized decision variables for mobile energy storage n, δ (k),
Figure BDA0003543483920000123
Are penalty coefficients at the k +1 th iteration,
Figure BDA0003543483920000124
is an active power actual interactive variable at the kth iteration,
Figure BDA0003543483920000125
Is an active power virtual interaction variable at the k +1 th iteration,
Figure BDA0003543483920000126
is the reactive power actual interaction variable at the k-th iteration,
Figure BDA0003543483920000127
is a reactive power virtual interaction variable at the k +1 th iteration,
Figure BDA0003543483920000128
is a problem P 1 The augmented Lagrangian function given α, β.
And S43, performing iterative solution on the node optimization subproblem and the mobile energy storage optimization subproblem by adopting a self-adaptive penalty coefficient-alternating direction multiplier method to obtain an optimal emergency scheduling scheme of the power distribution network.
Specifically, the step S43 is,
s431, initializing the node optimization subproblem and the mobile energy storage optimization subproblem: k is equal to 1, and k is equal to 1,
Figure BDA0003543483920000129
alpha (K), beta (K) > 0, K > 1; defining three constants λ 1 、λ 2 M, and λ 1 ,λ 2 More than 0, M more than 1; wherein, alpha (k) and beta (k) are penalty coefficients in the k iteration;
s432, collecting power demands configured by each node in h time period after power failure accident through a power distribution network
Figure BDA00035434839200001210
Maximum output value of wind power and photovoltaic power configured for each node
Figure BDA00035434839200001211
State of charge of energy storage system configured for each node
Figure BDA00035434839200001212
Diesel engine state variable configured for each node
Figure BDA00035434839200001213
Initial position of each mobile energy storage
Figure BDA00035434839200001214
And the state of charge of the power battery and the energy storage battery
Figure BDA00035434839200001215
S433, all node collection
Figure BDA00035434839200001216
And solving a node optimization decision variable x according to the expression of the node optimization subproblem by combining the parameters collected in the S432 i (k +1) and interaction variables
Figure BDA00035434839200001217
And will interact with the variables
Figure BDA00035434839200001218
Sending the data to a mobile energy storage;
s434, all the mobile energy storage collection sent interactive variables
Figure BDA00035434839200001219
And combining the parameters collected in the S432, and solving a mobile energy storage optimization decision variable y according to the expression of the mobile energy storage optimization subproblem n (k +1) and interaction variables
Figure BDA0003543483920000131
S435, judging the interaction variables obtained by solving in the S433
Figure BDA0003543483920000132
And the interaction variables solved in S434
Figure BDA0003543483920000133
Whether or not the convergence of the following formula (15) is satisfied;
Figure BDA0003543483920000134
wherein,
Figure BDA0003543483920000135
as a function of the interaction variable
Figure BDA0003543483920000136
In betweenThe original residual error is then compared to the original residual error,
Figure BDA0003543483920000137
as a function of the interaction variable
Figure BDA0003543483920000138
Original residual error between, phi 1 (k +1) is an interaction variable
Figure BDA0003543483920000139
Figure BDA00035434839200001310
Dual residual between, phi 2 (k +1) is an interaction variable
Figure BDA00035434839200001311
Dual residuals between;
if the interactive variable obtained by solving in the S433 is obtained
Figure BDA00035434839200001312
And the interaction variables solved in S434
Figure BDA00035434839200001313
If the convergence of the formula (15) is satisfied, the node optimization decision variable x obtained by solving in S433 is obtained i (k +1) and the mobile energy storage optimization decision variable y obtained by solving in the S434 n (k +1) is the optimal emergency scheduling scheme of the power distribution network in the h period after the power failure accident occurs;
if the interactive variable obtained by solving in S433
Figure BDA00035434839200001314
And the interaction variables solved in S434
Figure BDA00035434839200001315
If the convergence of the formula (15) is not satisfied, the penalty coefficients α, β and the Lagrangian multiplier vector δ,
Figure BDA00035434839200001316
And based on the updated penalty coefficients alpha, beta and the Lagrange multiplier vector delta,
Figure BDA00035434839200001317
Returning to the step S433, and continuing to perform distributed iteration until the convergence of the formula (15) is met or the maximum iteration number K is reached;
wherein, the formula for updating the penalty coefficients alpha and beta is as follows,
Figure BDA0003543483920000141
updating the Lagrange multiplier vector delta,
Figure BDA0003543483920000142
The formula of (a) is as follows,
Figure BDA0003543483920000143
the S4 can be solved by using a gurobi solver, and after the model is solved, the optimal emergency scheduling scheme of the urban distribution network for calculating the mobile energy storage resources in the H time period after the power failure fault is obtained, wherein the optimal emergency scheduling scheme comprises the conditions of output of diesel engines, wind power, photovoltaic systems and energy storage systems, the conditions of traffic transfer and output of the mobile energy storage systems, the conditions of node voltage, branch current, transmission power and the like of the distribution network.
The following are specific embodiments of the present invention:
in this embodiment, an improved IEEE 33 node power distribution network topology structure shown in fig. 2 is adopted, where nodes #1 to 13 and #19 to 28 form a microgrid 1, nodes #14 to 18 form a microgrid 2, nodes #29 to 33 form a microgrid 3, and access conditions of a diesel engine, wind power, photovoltaic, energy storage power station system, and mobile energy storage are shown in fig. 2. The time scale of emergency scheduling of the urban distribution network is 4 hours, and the unit scheduling time is 10 minutes. And after a power failure accident occurs in a test scene, the connection between the power distribution network represented by the test system and the large power grid is interrupted, and simultaneously each micro-grid enters an isolated network operation state.
The charge-discharge efficiency of the energy storage system and the mobile energy storage is set to be 90%, the ternary lithium battery and the lead-acid storage battery are respectively used as a power battery and an energy storage battery of the mobile energy storage, and the specifications are respectively 200kWh (capacity)/120 kW (rated charge power)/50 kW (rated discharge power), 3000kWh (capacity)/800 kW (rated charge power)/800 kW (rated discharge power). The correlation coefficient of the diesel engine is designed to be 0.0083, 0.05 and 0.2 respectively, and the price of the diesel oil is 9 yuan/L. Further, the typical data of the power load, the wind power and the photovoltaic shown in fig. 3 are used as the prediction data of 3 micro-grids after the power failure accident occurs, the distances between the #28 node (micro-grid 1), the #18 node (micro-grid 2) and the #33 node (micro-grid 3) of the mobile energy storage are respectively set to be 10km, 10km and 20km, and the driving speed is set to be 60km/h, so the driving time of the mobile energy storage at three micro-grid access points is respectively: 10 minutes, 20 minutes. The optimal scheduling path using 2 mobile energy storages as an embodiment is shown in fig. 4, and the operation costs of the urban distribution network taking the participation of the mobile energy storage resources into account and not taking the participation of the mobile energy storage resources into account are respectively calculated and shown in table 1, so that the operation cost of the mobile energy storage can be flexibly applied by about 22.22%. The load supply conditions of the 3 micro-grids are shown in fig. 5, and it can be seen that the dependence on diesel engines and energy storage resources in each micro-grid can be effectively reduced by flexibly using the mobile energy storage resources, and the power supply requirements of key loads are effectively improved. The nodes of the microgrid 1 are renumbered according to the power flow direction, and images of the voltage change of each node of the microgrid 1 along with the time are respectively obtained in consideration of the participation of the mobile energy storage resources and in spite of the participation of the mobile energy storage resources, as shown in fig. 6. Compared with the method without considering the participation of the mobile energy storage resource, the participation of the mobile energy storage resource can effectively supplement electric energy and stabilize the node voltage. Based on algorithm parameter lambda 1 ＝λ 2 Fig. 7 shows an iterative convergence image of the conventional ADMM algorithm and the adaptive penalty coefficient-ADMM algorithm, where α (1) ═ β (1) ═ 0.0002, and M ═ K ═ 3, and the adaptive penalty coefficient-ADMM algorithm can effectively overcome the problem of non-convergence due to improper penalty coefficient selection.
TABLE 1
Figure BDA0003543483920000151
Based on the power distribution network distributed energy emergency dispatching method considering the mobile energy storage, the invention also provides a power distribution network distributed energy emergency dispatching system considering the mobile energy storage.
As shown in fig. 8, a distributed energy emergency dispatching system for a power distribution network with consideration of mobile energy storage comprises the following modules,
the mobile energy storage constraint model building module is used for building a mobile energy storage constraint model according to the discrete energy flow characteristic and the traffic flow characteristic of mobile energy storage;
the distribution network constraint model building module is used for building a distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and comprises a mobile energy storage access according to an IEEE distribution network topological structure;
the power distribution network distributed energy emergency scheduling model building module is used for building a power distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the power distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the power distribution network;
and the iterative solution module is used for iteratively solving the distribution type energy emergency scheduling model of the power distribution network by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain an optimal emergency scheduling scheme of the power distribution network.
Based on the power distribution network distributed energy emergency scheduling method considering the mobile energy storage, the invention also provides a computer storage medium.
A computer storage medium comprising a memory and a computer program stored in the memory, which when executed by a processor, implements a method for distributed energy emergency dispatch for a power distribution network taking into account mobile energy storage as described above.
The invention relates to a distributed energy emergency scheduling method and system for a power distribution network considering mobile energy storage and a computer storage medium, wherein a mobile energy storage constraint model is constructed by combining a discrete energy flow model and a traffic flow model under the situation that mobile energy storage resources participate in the emergency scheduling of the energy of an urban power distribution network; based on an IEEE power distribution network topological structure, a power distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and comprises mobile energy storage access is constructed; therefore, the distributed energy emergency scheduling model of the urban distribution network considering the mobile energy storage resources is established by taking the minimum running cost of the distribution network as a target and taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions; considering that the convergence characteristic of the traditional alternative direction multiplier method is seriously influenced by the iteration coefficient, the adaptive penalty coefficient-alternative direction multiplier method is adopted to adaptively adjust the penalty coefficient in the iteration process, and the optimal emergency scheduling scheme of the urban distribution network is obtained by means of iterative solution of a gurobi commercial solver on the distributed energy emergency scheduling model of the urban distribution network. Therefore, compared with the traditional urban distribution network energy emergency scheduling scheme, the urban distribution network energy emergency scheduling method takes the participation of mobile energy storage resources into consideration, can effectively improve the flexibility, economy and reliability of the urban distribution network emergency power supply, adopts a distributed optimization framework, effectively realizes the privacy information protection of important loads of the urban distribution network, provides an optimal strategy for the urban distribution network energy emergency scheduling, can better accord with the development direction of a future urban distribution system, and promotes the benign development of the urban distribution network.
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 distributed energy emergency scheduling method for a power distribution network considering mobile energy storage is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, constructing a mobile energy storage constraint model according to the mobile energy storage discrete energy flow characteristic and the traffic flow characteristic;
s2, constructing a power distribution network constraint model consisting of a diesel engine, wind power, photovoltaic and energy storage systems and containing mobile energy storage access according to an IEEE power distribution network topological structure;
s3, constructing a distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the distribution network;
and S4, carrying out iterative solution on the distribution network distributed energy emergency dispatching model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain an optimal emergency dispatching scheme of the distribution network.
2. The distributed energy emergency dispatching method for the power distribution network considering mobile energy storage as claimed in claim 1, wherein: in the S1, the mobile energy storage constraint model comprises a mobile energy storage traffic flow constraint model and a mobile energy storage discrete energy flow constraint model;
the mobile energy storage traffic flow constraint model is as follows,
Figure FDA0003543483910000011
wherein,
Figure FDA0003543483910000012
all mobile energy storage sets are provided, and n is a mobile energy storage variable;
Figure FDA0003543483910000013
the method comprises the steps that a node set in a power distribution network is represented, and i and j are node variables;
Figure FDA0003543483910000014
h is a time interval variable for the time interval set of emergency scheduling after the power failure accident;
Figure FDA0003543483910000015
marking a bit variable for a 0-1 position of the mobile energy storage, specifically indicating whether the mobile energy storage n has arrived in the h time periodTo node i, if it has arrived, then
Figure FDA0003543483910000016
If not, then
Figure FDA0003543483910000017
θ ji Represents the actual distance between node j and node i, Δ H represents the length of each time period;
the mobile energy storage discrete energy flow constraint model is as follows,
Figure FDA0003543483910000021
Figure FDA0003543483910000022
Figure FDA0003543483910000023
wherein,
Figure FDA0003543483910000024
respectively the maximum active discharge power, the maximum active charge power and the maximum reactive power of the power battery in the mobile energy storage n,
Figure FDA0003543483910000025
respectively the maximum active discharge power, the maximum active charge power and the maximum reactive power of the energy storage battery in the mobile energy storage n,
Figure FDA0003543483910000026
representing the active discharge power of the power battery in the mobile energy storage n in the h period,
Figure FDA0003543483910000027
respectively representing the active power absorbed by the power battery in the mobile energy storage n through the node i and the reactive power absorbed by the power battery in the h time period,
Figure FDA0003543483910000028
respectively representing the active power absorbed, the released active power and the absorbed reactive power of the energy storage battery in the mobile energy storage n through the node i in the h period,
Figure FDA0003543483910000029
respectively representing the capacities of a power battery and an energy storage battery in the mobile energy storage n,
Figure FDA00035434839100000210
respectively representing the initial state of charge, the minimum value of the state of charge and the maximum value of the state of charge of the power battery in the mobile energy storage n,
Figure FDA00035434839100000211
respectively representing the initial charge state, the minimum charge state and the maximum charge state of the energy storage battery in the mobile energy storage n,
Figure FDA00035434839100000212
respectively representing the charge-discharge efficiency coefficient and the active capacity of the power battery in the mobile energy storage n,
Figure FDA0003543483910000031
respectively representing the charge-discharge efficiency coefficient and the active capacity of the energy storage battery in the mobile energy storage n,
Figure FDA0003543483910000032
respectively the charge states of the power battery in the mobile energy storage n in the H time period and the H time period,
Figure FDA0003543483910000033
respectively an energy storage battery in the mobile energy storage n in the h time periodH-time state of charge.
3. The distributed energy emergency dispatching method for the power distribution network considering the mobile energy storage as claimed in claim 2, wherein: in the step S2, the power distribution network constraint model includes a diesel engine operation constraint model, a wind power and photovoltaic operation constraint model, an energy storage system operation constraint model and a power distribution network power flow constraint model;
the diesel engine operation constraint model is that,
Figure FDA0003543483910000034
wherein,
Figure FDA0003543483910000035
the output running state variable of the diesel engine configured for the node i in the power distribution network in the h time period, if the diesel engine configured for the node i in the power distribution network is in the running state in the h time period, the diesel engine is started to run, and the diesel engine is started to run
Figure FDA0003543483910000036
If the diesel engine configured by the node i in the power distribution network is in a stop state in the h period, the diesel engine is started to be started
Figure FDA0003543483910000037
Figure FDA0003543483910000038
Starting and stopping state variables of the diesel engine configured for the node i in the power distribution network in the h-1 time period, and if the diesel engine configured for the node i in the power distribution network is in the starting action in the h-1 time period, starting and stopping state variables of the diesel engine configured for the node i in the power distribution network in the h-1 time period
Figure FDA0003543483910000039
If the diesel engine configured by the node i in the power distribution network is in stop action in the h-1 time period, the diesel engine is started to stop acting
Figure FDA00035434839100000310
Figure FDA00035434839100000311
Respectively representing the minimum active power, the maximum active power, the minimum reactive power, the maximum reactive power and the maximum active power change rate of the diesel engine configured at the node i,
Figure FDA00035434839100000312
the active power and the reactive power injected by the diesel engine configured at the node i in the h period are represented;
the wind power and photovoltaic operation constraint model is as follows,
Figure FDA00035434839100000313
wherein,
Figure FDA00035434839100000314
the maximum output of the wind power and the photovoltaic power which are respectively configured for the node i in the h time period,
Figure FDA00035434839100000315
Figure FDA0003543483910000041
actual output of wind power and photovoltaic power configured for the node i in the h time period respectively;
the operation constraint model of the energy storage system is as follows,
Figure FDA0003543483910000042
wherein,
Figure FDA0003543483910000043
respectively representing initial charge state, minimum charge state, maximum charge state, minimum charge power, maximum charge power and discharge of the energy storage system configured by the node iMinimum power, maximum discharge power, capacity, charge-discharge efficiency coefficient, active capacity,
Figure FDA0003543483910000044
respectively represents the active power absorbed by the energy storage system through the node i, the released active power and the released reactive power in the h period,
Figure FDA0003543483910000045
respectively representing the charge states of an energy storage system configured by the node i in the H time period and the H time period;
the power flow constraint model of the power distribution network is as follows,
Figure FDA0003543483910000046
wherein,
Figure FDA0003543483910000047
representing a set of line configurations in the distribution network, and (i, m) representing lines formed by nodes i and m
Figure FDA0003543483910000048
r ji 、x ji 、
Figure FDA0003543483910000049
Respectively representing lines
Figure FDA00035434839100000410
Resistance, reactance, maximum transmission capacity, square value of maximum transmission complex current,
Figure FDA00035434839100000411
respectively representing the square value of the minimum complex voltage and the square value of the maximum complex voltage of the node i,
Figure FDA00035434839100000412
active power demand, reactive power demand, P, of load configured for node i during period h ji,h 、Q ji,h 、l ji,h Respectively representing lines
Figure FDA00035434839100000413
Square value of active transmission power, reactive transmission power, transmission current, v, in h period i,h Square value v representing complex voltage of node i in h period j,h Representing the square of the complex voltage of node j over period h.
4. The distributed energy emergency dispatching method for the power distribution network considering mobile energy storage as claimed in claim 3, wherein: in S3, the objective function for minimizing the operation cost of the distribution network is,
Figure FDA0003543483910000051
Figure FDA0003543483910000052
wherein,
Figure FDA0003543483910000053
intrinsic configuration operating costs for node i during period h, including energy storage system operating costs
Figure FDA0003543483910000054
Operating cost of firewood-mixing machine
Figure FDA0003543483910000055
Figure FDA0003543483910000056
Operating cost of the mobile energy storage n in h period, including power battery operating cost
Figure FDA0003543483910000057
And energy storage battery operating costs
Figure FDA0003543483910000058
p G 、a i 、b i 、c i 、
Figure FDA0003543483910000059
Respectively representing a unit fuel price, a diesel engine starting and stopping cost coefficient, a diesel engine running cost coefficient, a diesel engine generating cost coefficient, an energy storage system breaking cost coefficient, a power battery breaking cost coefficient and an energy storage battery breaking cost coefficient;
the distributed energy emergency dispatching model of the power distribution network is,
Figure FDA00035434839100000510
Figure FDA00035434839100000511
s.t.(1)-(8)。
5. the distributed energy emergency dispatching method for the power distribution network considering mobile energy storage as claimed in claim 4, wherein: specifically, the step S4 is,
s41, converting the distribution network distributed energy emergency dispatching model into an augmented Lagrange form;
s42, decomposing the energy emergency scheduling problem into a node optimization subproblem and a mobile energy storage optimization subproblem based on the distribution network distributed energy emergency scheduling model in the augmented Lagrange form;
and S43, performing iterative solution on the node optimization subproblem and the mobile energy storage optimization subproblem by adopting a self-adaptive penalty coefficient-alternating direction multiplier method to obtain an optimal emergency scheduling scheme of the power distribution network.
6. The distributed energy emergency dispatching method for the power distribution network considering the mobile energy storage as claimed in claim 5, wherein: the distribution network distributed energy emergency dispatching model has the form of augmented Lagrange,
Figure FDA0003543483910000061
Figure FDA0003543483910000062
wherein,
Figure FDA0003543483910000063
in order to increase Lagrange functions, x and y respectively represent optimization decision variables of a node i and a mobile energy storage n in the power distribution network; both alpha and beta are given penalty coefficients;
Figure FDA0003543483910000064
respectively representing active power of the mobile energy storage n injected into the node i in the h time period and active power of the node i collected from the mobile energy storage n in the h time period;
Figure FDA0003543483910000065
respectively representing reactive power of the mobile energy storage n injected into the node i in the h time period and reactive power of the node i collected from the mobile energy storage n in the h time period; delta is a Lagrange multiplier vector between an active power actual interaction variable and an active power virtual interaction variable,
Figure FDA0003543483910000066
for actual interactive variable of reactive power and virtual interchange of reactive powerLagrange multiplier vectors between quantities, in particular delta n,i,h Represents a Lagrange multiplier vector between the reactive power injected into the node i by the mobile energy storage n in the h period and the reactive power collected from the mobile energy storage n by the node i in the h period,
Figure FDA0003543483910000071
and a Lagrange multiplier vector representing the reactive power injected into the node i by the mobile energy storage n in the h period and the reactive power collected from the mobile energy storage n by the node i in the h period.
7. The distributed energy emergency dispatching method for the power distribution network considering mobile energy storage as claimed in claim 6, wherein: the expression of the node optimization sub-problem is,
Figure FDA0003543483910000072
the expression of the mobile energy storage optimization sub-problem is,
Figure FDA0003543483910000073
wherein K ∈ {1, 2., K } is the number of iterations,
Figure FDA0003543483910000074
for the optimization decision variable set of the node i in the power distribution network,
Figure FDA0003543483910000075
set of optimized decision variables, x, for mobile energy storage n i (k +1) is an optimization decision variable x of a node i in the power distribution network during the (k +1) th iteration i Optimization decision variable, y, for node i in a power distribution network n (k +1) is the optimization decision variable of the mobile energy storage n in the k +1 iteration, y n Optimized decision variables for mobile energy storage n, δ (k),
Figure FDA0003543483910000076
Are penalty coefficients at the k +1 th iteration,
Figure FDA0003543483910000077
is an active power actual interactive variable at the kth iteration,
Figure FDA0003543483910000078
Is an active power virtual interaction variable at the k +1 th iteration,
Figure FDA0003543483910000079
is the reactive power actual interaction variable at the k-th iteration,
Figure FDA00035434839100000710
is a reactive power virtual interaction variable at the k +1 th iteration,
Figure FDA00035434839100000711
is a problem P 1 The augmented Lagrangian function given α, β.
8. The distributed energy emergency dispatching method for the power distribution network considering the mobile energy storage as claimed in claim 7, wherein: specifically, the step S43 is,
s431, initializing the node optimization subproblem and the mobile energy storage optimization subproblem: k is equal to 1, and k is equal to 1,
Figure FDA00035434839100000712
alpha (K), beta (K) > 0, K > 1; defining three constants λ 1 、λ 2 M, and λ 1 ,λ 2 More than 0, M more than 1; wherein, alpha (k) and beta (k) are penalty coefficients in the k iteration;
s432, collecting power demands configured by each node in h time period after power failure accident through a power distribution network
Figure FDA0003543483910000081
Maximum output value of wind power and photovoltaic power configured for each node
Figure FDA0003543483910000082
State of charge of energy storage system configured for each node
Figure FDA0003543483910000083
Diesel engine state variable configured for each node
Figure FDA0003543483910000084
Initial position of each mobile energy storage
Figure FDA0003543483910000085
And the state of charge of the power battery and the energy storage battery
Figure FDA0003543483910000086
S433, all node collection
Figure FDA0003543483910000087
And solving a node optimization decision variable x according to the expression of the node optimization subproblem by combining the parameters collected in the S432 i (k +1) and interaction variables
Figure FDA0003543483910000088
And will interact with the variables
Figure FDA0003543483910000089
Sending the data to a mobile energy storage;
s434, all the mobile energy storage collection sent interactive variables
Figure FDA00035434839100000810
And combining the parameters collected in the S432 to express the mobile energy storage optimization subproblemFormula solving mobile energy storage optimization decision variable y n (k +1) and interaction variables
Figure FDA00035434839100000811
S435, judging the interaction variables obtained by solving in the S433
Figure FDA00035434839100000812
And the interaction variables solved in S434
Figure FDA00035434839100000813
Whether or not the convergence of the following formula (15) is satisfied;
Figure FDA00035434839100000814
wherein,
Figure FDA00035434839100000815
as a function of the interaction variable
Figure FDA00035434839100000816
The original residual error between the two is determined,
Figure FDA00035434839100000817
as a function of the interaction variable
Figure FDA00035434839100000818
Original residual error between, phi 1 (k +1) is an interaction variable
Figure FDA00035434839100000819
Figure FDA00035434839100000820
Dual residual between, phi 2 (k +1) is an interaction variable
Figure FDA00035434839100000821
Dual residuals between;
if the interactive variable obtained by solving in the S433 is obtained
Figure FDA00035434839100000822
And the interaction variables solved in S434
Figure FDA00035434839100000823
If the convergence of the formula (15) is satisfied, the node optimization decision variable x obtained by solving in S433 is obtained i (k +1) and the mobile energy storage optimization decision variable y obtained by solving in the S434 n (k +1) is the optimal emergency scheduling scheme of the power distribution network in the h period after the power failure accident occurs;
if the interactive variable obtained by solving in the S433 is obtained
Figure FDA0003543483910000091
And the interaction variables solved in S434
Figure FDA0003543483910000092
If the convergence of the formula (15) is not satisfied, the penalty coefficients α, β and the Lagrangian multiplier vector δ,
Figure FDA0003543483910000093
And based on the updated penalty coefficients alpha, beta and the Lagrange multiplier vector delta,
Figure FDA0003543483910000094
Returning to the step S433, and continuing to perform distributed iteration until the convergence of the formula (15) is met or the maximum iteration number K is reached;
wherein, the formula for updating the penalty coefficients alpha and beta is as follows,
Figure FDA0003543483910000095
updating the Lagrange multiplier vector delta,
Figure FDA0003543483910000096
The formula of (a) is as follows,
Figure FDA0003543483910000097
9. the utility model provides a take into account distribution network distributed energy emergency dispatch system of portable energy storage which characterized in that: comprises the following modules which are used for realizing the functions of the system,
the mobile energy storage constraint model building module is used for building a mobile energy storage constraint model according to the discrete energy flow characteristic and the traffic flow characteristic of mobile energy storage;
the distribution network constraint model building module is used for building a distribution network constraint model which consists of a diesel engine, wind power, photovoltaic and energy storage systems and comprises a mobile energy storage access according to an IEEE distribution network topological structure;
the power distribution network distributed energy emergency scheduling model building module is used for building a power distribution network distributed energy emergency scheduling model considering mobile energy storage resources by taking the power distribution network constraint model and the mobile energy storage constraint model as constraint conditions with the aim of minimizing the operation cost of the power distribution network;
and the iterative solution module is used for iteratively solving the power distribution network distributed energy emergency scheduling model by adopting a self-adaptive penalty coefficient-alternate direction multiplier method to obtain the optimal emergency scheduling scheme of the power distribution network.
10. A computer storage medium, characterized in that: comprising a memory and a computer program stored in said memory, which computer program, when being executed by a processor, carries out the method of distributed energy emergency dispatch for a power distribution network taking account of mobile energy storage according to any of claims 1 to 8.
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