CN115102192A - Power distribution network elastic control method for dealing with extreme weather - Google Patents

Power distribution network elastic control method for dealing with extreme weather Download PDF

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CN115102192A
CN115102192A CN202210880891.9A CN202210880891A CN115102192A CN 115102192 A CN115102192 A CN 115102192A CN 202210880891 A CN202210880891 A CN 202210880891A CN 115102192 A CN115102192 A CN 115102192A
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power distribution
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CN115102192B (en
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夏世威
王睿
张茜
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State Grid Corp of China SGCC
North China Electric Power University
KME Sp zoo
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State Grid Corp of China SGCC
North China Electric Power University
KME Sp zoo
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention relates to a distribution network elastic control method for dealing with extreme weather, which belongs to the field of distribution networks and is characterized in that firstly, the extreme weather condition is predicted, and the fault rate of each component in the distribution network is analyzed according to the weather condition to obtain the condition of the fault of the distribution network component along with time; and then analyzing the time-space transfer energy characteristics of the mobile energy storage system, aiming at minimizing the load reduction weight, the mobile energy storage movement and charge-discharge weight and the distributed power generation comprehensive weight, establishing a power distribution network operation control model with the coordination of mobile energy storage flexible movement and power distribution network reconstruction, scheduling at three levels of the front, middle and rear, fully regulating and controlling power distribution network resources to reduce the influence of extreme weather on the power distribution network, fully exerting the energy transfer time-space flexibility of the mobile energy storage system, improving the time-space distribution of active output, reducing network loss, reducing load reduction, effectively improving the load recovery rate, reducing the operation voltage safety risk and reducing the system operation cost.

Description

Power distribution network elastic control method for dealing with extreme weather
Technical Field
The invention relates to the field of power distribution networks, in particular to a power distribution network elastic control method for dealing with extreme weather.
Background
In recent years, the climate change is continuously intensified, natural disasters and extreme weather frequently occur, and great challenges are brought to the safe and reliable operation of a power system. Meanwhile, active power of a power system under large-scale new energy infiltration has extremely high uncontrollable property and time-varying property, power transmission lines and towers are damaged and even collapsed due to typhoons, strong rainfall and other extreme weather, the power grid is impacted to operate safely, and loads are difficult to recover after the power distribution network fails. Therefore, it is also one of the hot spots of current research to improve the elasticity of the power distribution network and flexibly regulate and control the resources such as the energy storage system and the distributed power supply to reduce the damage caused by extreme events. The existing research shows that the active power instability problem in the load recovery problem of the power distribution network can be better solved by predicting extreme weather and coordinating and controlling resources in the system, particularly a mobile energy storage system and a distributed power supply, in a multi-time scale, and the influence of the extreme weather on the power distribution network can be reduced. In addition, the coordinated control of multiple time scales can also reduce the network loss of the system, optimize the power distribution of the power distribution network and improve the operation safety of the power grid.
In related researches at home and abroad, in the aspect of dealing with extreme weather, people propose to identify a high-risk area of a power distribution system and strengthen a line tower so as to improve the reliability of the power distribution system; the large power grid is also divided into a plurality of isolated islands, and the distributed power supply supplies power; and the output of the unit is regulated and controlled in advance, and the network structure and other resources are changed.
In the application aspect of mobile energy storage, the existing research explains a basic model, a coordination strategy and a control method of mobile energy storage; respectively adopting different modes to construct a power distribution network economy and resilience balance model, and making a decision on the capacity allocation of the mobile energy storage; and (4) by combining the mobile energy storage with the post-disaster topological change of the traffic network, performing load recovery on the determined line breaking scene after the disaster.
However, the above researches do not fully consider the uncertainty and time-varying property of the influence of extreme weather on the components of the power distribution network, and only start with fixed resources and regulation and control in advance, although the reliability of the power distribution network can be improved, in the face of complicated and variable extreme weather and unstable active power output caused by the fact that large-scale new energy is connected into the power distribution network, the power supply level in extreme weather cannot be improved, and the safety of the power distribution network cannot be effectively improved.
Disclosure of Invention
The invention aims to provide a power distribution network elastic control method for dealing with extreme weather, which is used for fully regulating and controlling power distribution network resources to reduce the influence of the extreme weather on a power distribution network, effectively improving the load recovery rate and reducing the running voltage safety risk.
In order to achieve the purpose, the invention provides the following scheme:
a distribution network elastic control method for dealing with extreme weather comprises the following steps:
constructing an extreme weather prediction model for predicting an extreme weather travel route and intensity;
establishing a power distribution network component fault model of a power distribution network component which fails under the influence of extreme weather;
constructing a mobile space-time characteristic model and an energy model of the mobile energy storage system;
establishing a power distribution network operation control model based on an extreme weather prediction model, a mobile space-time characteristic model and an energy model and aiming at minimizing load reduction weight, mobile energy storage movement and charge-discharge weight and distributed power generation comprehensive weight;
converting the power distribution network operation control model into a mixed integer second-order cone model through linearization and second-order cone relaxation;
before extreme weather reaches the power distribution network, predicting a traveling route and intensity of the extreme weather by using an extreme weather prediction model according to meteorological data;
according to the predicted travelling route and intensity of extreme weather, predicting the power transmission line with fault under the influence of extreme weather by using a power distribution network component fault model;
carrying out network reconstruction on the power distribution network on the power transmission lines which do not have faults under the influence of extreme weather in the power distribution network;
solving the mixed integer second-order cone model based on the reconstructed power distribution network to obtain an optimization result of the operation of the power distribution network before the extreme weather reaches the power distribution network; the optimization result of the operation of the power distribution network comprises the output of the distributed power supply at each time interval, a target station of the mobile energy storage vehicle at each moment, a charge-discharge plan of the mobile energy storage vehicle at each time interval and the on-off state of each contact switch in the power distribution network at each moment;
after extreme weather reaches the power distribution network, updating the meteorological data into real-time meteorological data, and returning to the step of predicting the travelling route and the strength of the extreme weather by using an extreme weather prediction model according to the meteorological data to obtain a real-time optimization result of the operation of the power distribution network after the extreme weather reaches the power distribution network;
and after the power distribution network leaves in extreme weather, solving the mixed integer second-order cone model according to the real-time power consumption requirement to obtain a real-time optimization result of the power distribution network operation afterwards.
Optionally, the constructing an extreme weather prediction model for predicting the extreme weather travel route and the extreme weather travel strength specifically includes:
the typhoon tracking model after the typhoon is formed is established as
Δlnc＝a 1 (t)+a 2 (t)ψ(t)+a 3 (t)λ(t)+a 4 (t)lnc(t)+a 5 (t)θ(t)+ε c
Δθ＝b 1 (t)+b 2 (t)ψ(t)+b 3 (t)λ(t)+b 4 (t)c(t)+b 5 (t)θ(t)+b 6 (t)θ(t-Δt)+ε θ
lnI(t+Δt)＝d 1 (t)+d 2 (t)lnI(t)+d 3 (t)lnI(t-Δt)+d 4 (t)lnI(t-2Δt)
+d 5 (t)T s (t)+d 6 (t)(T s (t+Δt)-T s (t))+ε l
Wherein c is the moving speed of the typhoon, a 1 (t)、a 2 (t)、a 3 (t)、a 4 (t) and a 5 (t) are the first, second, third, fourth and fifth velocity model parameters, respectively, ψ (t), λ (t) are the longitude and latitude of the typhoon center at time t, Δ θ is the typhoon moving direction with the due north direction as a reference, I (t) is the relative intensity of the typhoon at time t, Δ t is the time interval, b 1 (t)、b 2 (t)、b 3 (t)、b 4 (t) and b 5 (t) first, second, third, fourth and fifth orientation model parameters, respectively, d 1 (t)、d 2 (t)、d 3 (t)、d 4 (t) and d 5 (t) first, second, third, fourth and fifth relative intensity model parameters, ε c 、ε θ 、ε l Respectively the random errors of the moving speed, direction and relative intensity of the typhoon, c (T) is the moving speed of the typhoon at the time T, theta (T) is the moving direction of the typhoon at the time T, T s (t) is the ocean surface temperature at time t;
establishing a wind power field model under the influence of typhoon according to the typhoon tracking model;
according to the typhoon tracking model, a precipitation model under the influence of typhoon is established as
RA(r,t)＝k(t)k 1 (t)s(t)R(r,t)
k(t)＝0.0319Δp(t)-0.0395,k≥1
Figure BDA0003764129020000031
Figure BDA0003764129020000032
Wherein RA (r, t) is tThe rainfall at radius R, R (R, t) is the first auxiliary variable, k (t), k 1 (t) and s (t) are correction coefficients of typhoon intensity, central air pressure change rate and moving speed respectively,
Figure BDA0003764129020000033
as the rate of change of the central air pressure, R wm (t) is the maximum wind speed radius at time t,
Figure BDA0003764129020000041
Δ p (t) is the central pressure difference of the typhoon,
Figure BDA0003764129020000042
the first random error of the normal distribution.
Optionally, the typhoon tracking model further includes: the strength model of typhoon on at sea and the strength model of typhoon after landing;
the strength model of the typhoon on at sea is
Figure BDA0003764129020000043
Δp 1 (t)＝p a -p c (t)
Wherein, I 1 (t) is the relative intensity of the typhoon at sea, Δ p 1 (t) the central pressure difference of the typhoon at sea, p da Surface value, p, of the partial pressure of ambient dry air dc Surface value, p, of the central pressure of the drying air which can be maintained at a minimum a At ambient pressure, p c (t) typhoon central air pressure at time t;
the intensity model after typhoon landing is
Δp 2 (t)＝Δp(t ldf )exp(-a d ·(t-t ldf ))，t＞t ldf
Figure BDA0003764129020000044
Wherein, Δ p 2 (t) isTyphoon center pressure difference t at time t after typhoon landing, t ldf For typhoon landing time, a d Δ p (t) as a decay constant ldf )、c(t ldf )、R wm (t ldf ) Respectively the central pressure difference, the moving speed and the maximum wind speed radius at the landing moment of typhoon, a d0 、a d1 A first and a second attenuation parameter respectively,
Figure BDA0003764129020000045
is a zero mean normal distribution error.
Optionally, the wind farm model comprises: a first gradient wind speed model with gradient wind speed at the height of a gradient and above and a second gradient wind speed model with gradient wind speed below the height of the gradient;
the first gradient wind speed model is
Figure BDA0003764129020000046
Figure BDA0003764129020000047
B(t)＝1.881-0.00557R wm (t)-0.01295ψ(t)+ε B
Figure BDA0003764129020000051
Figure BDA0003764129020000052
Wherein, V G (r, t) is the gradient wind speed at a distance r from the center of the typhoon at time t, A (r, t) is the second auxiliary variable, f r Is the Coriolis coefficient; b (t) is the Holland pressure profile shape parameter, ρ is the air density, ε B Is the second random error of normal distribution, omega is the earth's rotation speed,
Figure BDA0003764129020000053
the latitude at a distance r from the center of the typhoon;
the second gradient wind speed model is
Figure BDA0003764129020000054
Wherein V (r, t, h) is the gradient wind speed at the position of r and h from the center radius of hurricane at the time t, and alpha r Is a power law index, h, related to terrain roughness G Is the gradient height.
Optionally, the establishing a power distribution network component fault model in which a power distribution network component fails under the influence of extreme weather specifically includes:
according to the second gradient wind speed model, determining a gradient wind speed model of the position of the power distribution network component as
Figure BDA0003764129020000055
Figure BDA0003764129020000056
Wherein, V (r) cm (t),t,h d ) At a distance h from the distribution network component d At 3 seconds gust wind speed, V G (r cm (t), t) gradient wind speed at the location of the distribution network component, r cm (t) distance, α, from distribution network component position to typhoon center cm Is the power law exponent of the part position, (x) cm ,y cm ) (x (t), y (t)) are the coordinates of the transmission means and the centre of the typhoon, respectively, G τ Is a gust factor; the power distribution network component comprises a transmission tower and a transmission line;
combining the gradient wind speed model of the position of the distribution network component with the precipitation model, establishing a transmission tower fault model of the transmission tower which has faults under the influence of typhoons as
Figure BDA0003764129020000057
Figure BDA0003764129020000058
V * (r tw (t),t,h d )＝V(r tw (t),t,h d )+1.4411f RA f V
f RA ＝0.09376(RA(r tw (t),t)) 0.7087
f V ＝exp(0.004484)V(r tw (t),t)-1.2486exp(-0.1921)V(r tw (t),t,h d ))
Wherein, P tw,i (t) is the fault probability of the transmission tower i at the time t,
Figure BDA0003764129020000061
σ tw,i respectively, logarithmic mean and standard deviation, x tw,i (t) is the natural logarithm of the equivalent wind speed, V * (r tw (t),t,h d ) For transmission towers in h d At an equivalent wind speed, RA (r) tw (t), t) is the rainfall rate of the position of the transmission tower, r tw (t) is the distance from the position of the transmission tower to the center of the typhoon, f RA Is an intermediate variable, f V Is a transition variable;
combining the gradient wind speed model of the position of the power distribution network component with the precipitation model, and establishing a power transmission line fault model of the power transmission line which has a fault under the influence of typhoon as
P sg,j (t)＝(1-P sg,j (t-Δt))(1-exp(-λ sg,j (t)Δt))+P sg,j (t-Δt),t≥Δt
λ sg,j (t)＝L sg exp(f wr )
Figure BDA0003764129020000062
Wherein, P sg,j (t)、P sg,j (t-delta t) is the fault of the jth section in the power transmission line at the time t and the time t-delta tRate, L sg Is the length of the segment, f wr Is a third auxiliary variable, V (r) sg (t),t,h d )、RA(r sg (t) and t) are respectively the wind speed and precipitation at the jth section position in the power transmission line, V sgd 、RA sgd Respectively the design wind speed and rainfall, a, of the j-th section in the transmission line sg 、b sg 、c sg Respectively a first, a second and a third parameter, lambda sg,j (t) is the initial failure rate of the jth section in the power transmission line at the moment t;
according to the transmission tower fault model and the transmission line fault model, determining a fault probability model of a transmission path where the transmission tower and the transmission line are located as
Figure BDA0003764129020000063
P sg,j (t)＝(1-P sg,j (t-Δt))(1-exp(-λ sg,j (t)Δt))+P sg,j (t-Δt),t≥Δt
Wherein, P l (t) probability of failure of the transmission path at time t, n tw 、n sg The number of the transmission tower and the number of the transmission line sections are respectively.
Optionally, the model of the mobile space-time characteristics of the mobile energy storage system is
Figure BDA0003764129020000071
Figure BDA0003764129020000072
Figure BDA0003764129020000073
Figure BDA0003764129020000074
Figure BDA0003764129020000075
Wherein R is a road network node and a power station changing node set,
Figure BDA0003764129020000076
an arc between a power change station a and a power change station b is defined, M is a set of mobile energy storage vehicles, and T is a set of time;
Figure BDA0003764129020000077
if the energy storage vehicle m moves from the power changing station a to the power changing station b at the time t, 0 means not going to, and 1 means going to; r - 、R + Respectively a departure time and an arrival time,
Figure BDA0003764129020000078
at the time 1, the energy storage vehicle m is in a state from the power change station a to the power change station b,
Figure BDA0003764129020000079
the state that the energy storage vehicle m departs from the power changing station a at the time 0,
Figure BDA00037641290200000710
at the time T, the energy storage vehicle m is in a state from the power change station a to the power change station b,
Figure BDA00037641290200000711
the state that the energy storage vehicle m reaches the battery replacement station b at the moment T;
the energy model is
Figure BDA00037641290200000712
Figure BDA00037641290200000713
Figure BDA0003764129020000081
Figure BDA0003764129020000082
Figure BDA0003764129020000083
Figure BDA0003764129020000084
Figure BDA0003764129020000085
Figure BDA0003764129020000086
Figure BDA0003764129020000087
Wherein the content of the first and second substances,
Figure BDA0003764129020000088
for the charging power of the mobile energy storage vehicle m at the power changing station a at the time t,
Figure BDA0003764129020000089
for the maximum charging power allowed by the mobile energy storage vehicle m,
Figure BDA00037641290200000810
the discharging power of the energy storage vehicle m at the power changing station a is moved at the time t,
Figure BDA00037641290200000811
in order to move the maximum discharge power allowed by the energy storage vehicle m,
Figure BDA00037641290200000812
the energy storage vehicle m is always in the state of the power change station a at the moment t;
Figure BDA00037641290200000813
in order to supply the maximum charging power allowed by the charging station,
Figure BDA00037641290200000814
maximum discharge power allowed for the charging station, δ t CH，m For changing the state of charge of the station, delta t DCH，m Discharging state quantity for the power change station;
Figure BDA00037641290200000815
the electric quantity SOC, eta of the energy storage vehicle m is moved at the moment of t +1 CH,m 、η DCH,m Respectively the charging and discharging efficiency of the mobile energy storage vehicle m,
Figure BDA00037641290200000816
in order to move the SOC of the energy storage vehicle m at the end time,
Figure BDA00037641290200000817
for moving the SOC, E of the energy storage vehicle m at the initial moment min 、E max Respectively the upper limit, the lower limit, eta of the SOC of the mobile energy storage vehicle DCH，m For storing m discharge power of the vehicle, eta CH，m And charging power for the energy storage vehicle m.
Optionally, the objective function of the power distribution network operation control model is
Figure BDA0003764129020000091
Wherein Z is a target function, N is a power distribution network node set, and C cut Reducing the weighting factor for a unit of load, C gen Weight factor for unit generation of distributed power, C mob For storing energy for movementWeight factor for single movement of vehicle, C CH 、C DCH Is a unit charging and discharging weight factor of the mobile energy storage vehicle,
Figure BDA0003764129020000092
for the load power of the node when the system is normally operated at the time t,
Figure BDA0003764129020000093
for the load power of the node at the actual operation time t,
Figure BDA0003764129020000094
and the output of the distributed unit of the node a at the moment t.
Optionally, the constraint conditions of the power distribution network operation control model include: power distribution network reconstruction constraint, power flow constraint, DG constraint, voltage amplitude constraint and line capacity constraint;
the power distribution network is constrained by reconfiguration
Figure BDA0003764129020000095
Figure BDA0003764129020000096
Figure BDA0003764129020000097
Figure BDA0003764129020000098
Wherein N is the number of nodes of the power distribution network, D is the number of distributed power supplies, and alpha ij The state of the circuit is open and closed; beta is a ij And beta ji Representing a parent-child relationship between the node i and the node j; if i is the parent of j, then β ij Is 1, conversely beta ij Is 0; if j is the parent node of i, then β ji Is 1, conversely beta ji Is 0; psi (i) is a bus connected to node i;
the power flow constraint is
Figure BDA0003764129020000099
Figure BDA00037641290200000910
Figure BDA0003764129020000101
Figure BDA0003764129020000102
Figure BDA0003764129020000103
Figure BDA0003764129020000104
Wherein the content of the first and second substances,
Figure BDA0003764129020000105
respectively the active power and the reactive power injected by the distributed power supply at the node i at the time t,
Figure BDA0003764129020000106
respectively the active power and the reactive power of the load of the node i at the time t,
Figure BDA0003764129020000107
respectively the active power and the reactive power transmitted from the node i to the node j of the line at the time t,
Figure BDA0003764129020000108
are respectively at tInjecting active power and reactive power of the node i,
Figure BDA0003764129020000109
active and reactive losses, V, respectively, of the injection line i t 、
Figure BDA00037641290200001010
Respectively the node voltage and the line current at time t,
Figure BDA00037641290200001011
the square values r of the voltages of the node j and the node i at the time t ij Is the resistance between node i and node j, x ij Is the reactance between node i and node j, V i Is the voltage of node i;
the DG is constrained to
Figure BDA00037641290200001012
Figure BDA00037641290200001013
Figure BDA00037641290200001014
Figure BDA00037641290200001015
Wherein the content of the first and second substances,
Figure BDA00037641290200001016
respectively the upper limit of active power output, the upper limit of reactive power output and the upper limit of capacity, pf, of the distributed power supply DG,i The power factor of the distributed power supply connected with the node i at the moment t;
the voltage amplitude is constrained to
Figure BDA00037641290200001017
Wherein the content of the first and second substances,
Figure BDA00037641290200001018
the upper limit and the lower limit of the square value of the voltage of the node i are defined;
the line capacity constraint is
Figure BDA0003764129020000111
Figure BDA0003764129020000112
Wherein the content of the first and second substances,
Figure BDA0003764129020000113
in order to be the upper limit of the line capacity,
Figure BDA0003764129020000114
is the square of the current value of the line at time t,
Figure BDA0003764129020000115
the upper limit of the squared line current value.
Optionally, the operation control model of the power distribution network is converted into a mixed integer second-order cone model through linearization and second-order cone relaxation, and the method specifically includes the following steps:
constraining the power flow by the large M method
Figure BDA0003764129020000116
Is converted into
Figure BDA0003764129020000117
And
Figure BDA0003764129020000118
wherein M is a positive number;
will tideIn the flow constraint
Figure BDA0003764129020000119
And
Figure BDA00037641290200001110
performing second-order cone relaxation to obtain a second-order cone constraint of
Figure BDA00037641290200001111
And
Figure BDA00037641290200001112
constrain DG
Figure BDA00037641290200001113
Linearization to
Figure BDA00037641290200001114
Figure BDA00037641290200001115
Figure BDA00037641290200001116
Figure BDA00037641290200001117
Constrain DG
Figure BDA00037641290200001118
Linearization to
Figure BDA00037641290200001119
Wherein the content of the first and second substances,
and forming a mixed integer second-order cone model by the target function, the untransformed constraint in the constraint condition and the transformed constraint in the constraint condition.
Optionally, the power distribution network based on reconstruction solves the mixed integer second-order cone model to obtain an optimization result of the operation of the power distribution network before the extreme weather reaches the power distribution network, and specifically includes:
and calling a Guobi 9.5.0 solver to solve the mixed integer second-order cone model through a YALMIP tool package based on the reconstructed power distribution network so as to obtain an optimization result of the operation of the power distribution network before extreme weather reaches the power distribution network.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a distribution network elastic control method for dealing with extreme weather, which comprises the steps of firstly predicting the extreme weather condition, analyzing the fault rate of each component in a distribution network according to the weather condition, and further obtaining the condition of the fault of the distribution network components along with time; and then analyzing the space-time transfer energy characteristics of the mobile energy storage system, aiming at minimizing the load reduction weight, the mobile energy storage movement and charge-discharge weight and the distributed power generation comprehensive weight, establishing a power distribution network operation control model with the coordination of mobile energy storage flexible movement and power distribution network reconstruction, converting the model into a mixed integer second-order cone programming problem through linearization and relaxation, solving the problem, scheduling the power distribution network on the three aspects of the front, middle and rear, fully regulating and controlling power distribution network resources to reduce the influence of extreme weather on the power distribution network, fully exerting the energy transfer space-time flexibility of the mobile energy storage system, improving the active power space-time distribution, reducing the network loss, reducing the load reduction, effectively improving the load recovery rate, reducing the operating voltage safety risk and reducing the system operation cost.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for flexibly controlling a power distribution network in response to extreme weather according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a traffic network of a mobile energy storage system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of spatiotemporal movement of a mobile energy storage system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a power distribution network elastic control method for dealing with extreme weather, which is used for fully regulating and controlling power distribution network resources to reduce the influence of the extreme weather on a power distribution network, effectively improving the load recovery rate and reducing the running voltage safety risk.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a power distribution network elastic operation control method for dealing with extreme weather, which is mainly based on extreme weather prediction, a distributed power supply, a mobile energy storage system and power distribution network reconstruction. A mobile space-time characteristic model and an energy characteristic model of the mobile energy storage system are established, a network reconstruction model and a power flow model are combined through a large M method, and finally the operation control method provided by the invention solves the problem by converting the linearization and relaxation of the solved model into a mixed integer second-order cone programming problem. Through the prediction of the power distribution network component fault condition under the extreme weather, the space-time flexibility and the distributed power supply of the mobile energy storage system are reasonably utilized, the power distribution network is subjected to optimal control in the three layers of the front, the middle and the back, the space-time distribution of active power is improved, the network loss is reduced, the load recovery rate is effectively improved, the safety risk of operating voltage is reduced, the system operation cost is reduced, and especially the influence of the extreme weather on the power distribution network is reduced.
The following describes in detail an implementation process of a distribution network elasticity control method for dealing with extreme weather, as shown in fig. 1, including the following steps:
step S1, an extreme weather prediction model for predicting the extreme weather travel route and intensity is constructed.
Extreme weather prediction models include typhoon tracking models, windfarm models, and precipitation models.
(1) Typhoon tracking model
After the typhoon is formed, the intensity, the moving direction and the moving speed of the typhoon change along with time, and the model is shown as the following formula:
Δlnc＝a 1 (t)+a 2 (t)ψ(t)+a 3 (t)λ(t)+a 4 (t)lnc(t)+a 5 (t)θ(t)+ε c (1)
Δθ＝b 1 (t)+b 2 (t)ψ(t)+b 3 (t)λ(t)+b 4 (t)c(t)+b 5 (t)θ(t)+b 6 (t)θ(t-Δt)+ε θ (2)
Figure BDA0003764129020000141
where c is the moving speed of the typhoon, Δ t is the time interval, ψ (t), λ (t) are the longitude, latitude of the center of the typhoon, Δ θ is the moving direction of the typhoon with the north direction as a reference, I (t) is the relative intensity of the typhoon, a 1 (t) to a 5 (t)、b 1 (t) to b 6 (t)、d 1 (t) to d 6 (t) is a model parameter, ε, derived from historical data of the location of the typhoon c 、ε θ 、ε l Respectively, the random errors of the typhoon moving speed, direction and relative intensity.
Typhoon has different relative intensity models at sea and on land because when typhoon moves on land, the relative intensity can be weakened along with the time, resulting in the increase of the central air pressure of the typhoon. The strength model of typhoon at sea is as follows:
Figure BDA0003764129020000142
Δp(t)＝p a -p c (t) (5)
wherein I (t) is the relative strength of the typhoon at sea, Δ p (t) is the central pressure difference of the typhoon, p da Surface value, p, for the partial pressure of the ambient drying air dc At a minimum surface value, p, of the central pressure of the drying air that can be maintained a At ambient pressure, p c (t) is typhoon central air pressure.
The model after typhoon landing is as follows:
Δp(t)＝Δp(t ldf )exp(-a d ·(t-t ldf ))(t＞t ldf ) (6)
Figure BDA0003764129020000143
where Δ p (t) is the typhoon center pressure difference with exponential decay, t ldf Is typhoon landing time, a d Is the decay constant, Δ p (t) ldf )、c(t ldf )、R wm (t ldf ) Are each t ldf Center pressure difference, moving speed, maximum wind speed radius at time, a d0 、a d1 Is an attenuation parameter derived from historical data of the typhoon location,
Figure BDA0003764129020000144
is a zero-mean normal distribution error.
(2) Wind farm model
Power distribution network operation control strategy based on distributed power supply, mobile energy storage and network reconstruction
The gradient wind speed is a function of radius at and above the gradient height, and at a distance r from the centre of the typhoon, the formula for the gradient wind speed is as follows:
Figure BDA0003764129020000151
Figure BDA0003764129020000152
B(t)＝1.881-0.00557R wm (t)-0.01295ψ(t)+ε B (10)
Figure BDA0003764129020000153
Figure BDA0003764129020000154
wherein, V G (r, t) is the gradient wind speed, A (r, t) is the auxiliary variable, f r Is the Coriolis coefficient; r wm (t) is the maximum wind velocity radius, which is a function of Δ p (t) and ψ (t), B (t) is the Holland pressure profile shape parameter, ρ is the air density, Ω is the earth rotation speed,
Figure BDA0003764129020000155
is the latitude of the location, epsilon B 、
Figure BDA0003764129020000156
Respectively, a random error of a normal distribution.
The wind speed below the gradient height varies vertically as the surface roughness varies. At radius r, height h from hurricane center, the average wind speed is calculated from a widely accepted power law profile model:
Figure BDA0003764129020000157
wherein alpha is r Is a power law index, h, related to terrain roughness G Is the gradient height.
(3) Precipitation model
The precipitation model is related to typhoon intensity, central air pressure change rate and moving speed, and the model formula is as follows:
RA(r,t)＝k(t)k 1 (t)s(t)R(r,t) (14)
k(t)＝0.0319Δp(t)-0.0395,k≥1 (15)
Figure BDA0003764129020000161
Figure BDA0003764129020000162
wherein R (R, t) represents the rainfall at radius R, R (R, t) is an auxiliary variable,
Figure BDA0003764129020000163
is the rate of change of the central air pressure, k (t), k 1 (t), s (t) are correction factors for hurricane intensity, rate of change of central air pressure, and speed of movement, respectively.
And step S2, establishing a power distribution network component fault model of the power distribution network component which fails under the influence of extreme weather.
Along with the movement of typhoon, the transmission tower and the transmission line are influenced by wind and precipitation and can change along with the space-time change, if the influence exceeds the construction standard, a fault risk exists, and therefore, the modeling of the fault probability of the component is very important, and whether the component is damaged or not is judged.
1) Typhoon intensity of component position
In the case of typhoon position and intensity information determination, it is first necessary to calculate the distance r of the component position to the center of the typhoon cm (t), then calculating the typhoon wind speed and the rainfall rate of the part position, wherein the calculation formula is as follows:
Figure BDA0003764129020000164
wherein (x) cm ,y cm ) And (x (t), y (t)) are the coordinates of the power transmission component and the center of the typhoon, respectively.
For example, typhoon is in (x (t) 1 ),y(t 1 ) Time start versus distance r from the center of the typhoon cm (t 1 ) Has an influence on the tower of (1), at t 2 The time is affected most at t 3 After that moment there is no influence on the tower.
Will r is cm (t 1 ) Substituting the formula (8) into the formula (8), the gradient wind speed V of the component position can be obtained G (r cm (t, t), the basic wind speed at 10 meters height is selected as the 3 second gust wind speed, therefore, V is assigned G (r cm (t), t) is converted to formula (19) with the gust factor as follows:
Figure BDA0003764129020000165
wherein, V (r) cm (t),t,h d ) Is the 3 second gust wind speed alpha at the position of the component 10 meters cm Is the power law index of the position of the component, h d ＝10m，G τ Is a gust factor.
2) Transmission tower fault model
Herein, the probability of tower damage in typhoons is analyzed using a vulnerability function based on structural features. The relation curve of the tower fault rate and the wind speed is called a fragility curve and is a cumulative distribution function of the tower resistance to the bad extreme condition. In the tower brittleness analysis, the influence of precipitation on the tower is introduced by the concept of equivalent wind speed, as follows:
V * (r tw (t),t,h d )＝V(r tw (t),t,h d )+1.4411f RA f V (20)
f RA ＝0.09376(RA(r tw (t),t)) 0.7087 (21)
f V ＝exp(0.004484)V(r tw (t),t)-1.2486exp(-0.1921)V(r tw (t),t,h d )) (22)
wherein r is tw And (t) is the distance from the position of the tower to the center of the typhoon. V * (r tw (t),t,h d )、V(r tw (t),t,h d ) And RA (r) tw And (t) and t) are respectively equivalent wind speed, wind speed and rainfall rate of the tower position.
Therefore, the failure probability of tower i can be mathematically represented by a lognormal vulnerability curve as follows:
Figure BDA0003764129020000171
wherein the content of the first and second substances,
Figure BDA0003764129020000172
is the natural logarithm of the equivalent wind speed,
Figure BDA0003764129020000173
σ tw,i respectively, log mean and standard deviation.
The attack angle of typhoon also influences the failure probability of the part, and the angle influence is mainly reflected in parameters in the model
Figure BDA0003764129020000174
And σ tw,i In the above-described manner.
3) Transmission line fault model
The fault rate of the power transmission line under the action of the typhoon is calculated by using a regression method, and the power transmission line is divided into a plurality of sections to reflect the spatial influence of the typhoon. Assuming that each segment is short and the wind speed and precipitation acting on the segment are the same, the failure rate of the j-th conductor can be expressed as:
λ sg,j (t)＝L sg exp(f wr )
Figure BDA0003764129020000175
wherein L is sg For the length of the segment, f wr Is an auxiliary variable, V (r) sg (t),t,h d )、RA(r sg (t), t) represent wind speed and precipitation at the segment position, respectively. V sgd 、RA sgd Is the designed wind speed and rainfall for that segment of the line. a is sg 、b sg 、c sg Respectively, the section of parameters obtained from the historical data.
When the typhoon passes through the border, the damaged part is not considered to be repaired, namely the power transmission line cannot be repaired after being in fault when the power transmission line is in extreme weather, so that the fault rate of the jth section is as follows:
P sg,j (t)＝(1-P sg,j (t-Δt))(1-exp(-λ sg,j (t)Δt))+P sg,j (t-Δt),t≥Δt (25)
in the invention, the faults of the transmission line and the tower are independent from each other, namely the fault rate of each component is not influenced by other components. According to the series connection model of the probability, if one line or tower in one transmission path has a fault, the transmission path has a power failure, so that the fault probability of the transmission path is as follows:
Figure BDA0003764129020000181
wherein n is tw 、n sg The number of the transmission tower and the number of the line sections are respectively.
According to the fault scene assumption, when the line is in fault, the fault state mu of the line ij,t When the network is reconfigured, the segment of the line is considered to be always in the disconnected state.
And step S3, constructing a mobile space-time characteristic model and an energy model of the mobile energy storage system.
Space-time movement model of mobile energy storage system
In order to efficiently handle the scheduling problem of a plurality of mobile energy storage vehicles in time and space, a discrete multi-layer space-time network model is adopted to represent the space-time movement characteristics and the charging and discharging states of the mobile energy storage vehicles, as shown in fig. 2, each node represents one power switching station, a bidirectional communication path exists only between adjacent power switching stations, and non-adjacent power switching stations cannot be directly communicated by a road network. The passing time of each path is a time span, and the influence of road network damage caused by extreme weather is not considered in the model.
The moving behavior of the mobile energy storage vehicle is represented by arcs, and is divided into a moving arc and a fixed arc as shown in fig. 3, wherein the moving arc represents that the mobile energy storage vehicle moves between power exchange stations through a road network; the fixed arc represents that the mobile energy storage vehicle exchanges energy with the power distribution network at the power exchanging station, and the space position conversion of active power is realized. Each layer of schematic model represents the space-time movement characteristic of one mobile energy storage vehicle, and the plurality of layers of models are superposed to form the space-time movement characteristic of the whole mobile energy storage system. In fig. 3, Swap station represents a power change station, park arc represents a stationary arc, Transit arc represents a moving arc, Time Span represents a Time Span, and Position represents a Position.
The spatiotemporal motion characteristics of the mobile energy storage system described above may be expressed as:
Figure BDA0003764129020000191
Figure BDA0003764129020000192
Figure BDA0003764129020000193
Figure BDA0003764129020000194
Figure BDA0003764129020000195
wherein R represents a collection of moving arcs and fixed arcs,
Figure BDA0003764129020000196
an arc between the power change stations is shown, and a represents the power change station. M represents a set of mobile energy storage vehicles. T represents a set of times.
Figure BDA0003764129020000197
And the state scalar quantity represents whether the mobile energy storage vehicle m moves from the power change station a to the power change station b at the time t, 0 represents no forward movement, and 1 represents forward movement. The expression (27) indicates that the same mobile energy storage vehicle can only be located in the power exchange station or move in the road network at any time. Equation (28) indicates that the end position of the mobile energy storage vehicle at time t is the start position of the mobile energy storage vehicle at time t + 1. Expressions (29) and (30) respectively represent the start and end positions of the mobile energy storage vehicle; the expression (31) indicates that the mobile energy storage vehicle cannot reciprocate between two battery replacement stations and does not participate in energy exchange.
Energy storage system energy model considering space-time movement characteristics
When the mobile energy storage vehicle is positioned in the battery replacement station, the charging, discharging and energy requirements of the mobile energy storage vehicle simultaneously satisfy the following various constraints.
Figure BDA0003764129020000198
Figure BDA0003764129020000199
Wherein the content of the first and second substances,
Figure BDA0003764129020000201
the charging power of the mobile energy storage vehicle m at the power changing station a at the time t is shown,
Figure BDA0003764129020000202
represents the maximum charging power allowed by the mobile energy storage vehicle m,
Figure BDA0003764129020000203
the discharge power of the mobile energy storage vehicle m at the power changing station a at the time t is shown,
Figure BDA0003764129020000204
and the maximum discharge power allowed by the mobile energy storage vehicle m is shown.
Figure BDA0003764129020000205
Figure BDA0003764129020000206
Figure BDA0003764129020000207
In the formula (I), the compound is shown in the specification,
Figure BDA0003764129020000208
represents the charging power allowed by the power changing station where the mobile energy storage vehicle m is located at the time t,
Figure BDA0003764129020000209
for the maximum charging power allowed by the power change station,
Figure BDA00037641290200002010
represents the discharge power allowed by the power changing station where the mobile energy storage vehicle m is located at the time t,
Figure BDA00037641290200002011
the maximum discharge power allowed for the charging station. The constraint means that the mobile energy storage vehicle can only be in one state of charging or discharging at most.
Figure BDA00037641290200002012
Figure BDA00037641290200002013
Figure BDA00037641290200002014
Figure BDA00037641290200002015
In a constrained manner
Figure BDA00037641290200002016
The electric quantity SOC of the energy storage vehicle m is moved at the moment of t +1 and is related to the charging and discharging power and efficiency of the last time span, eta CH,m 、η DCH,m The charge and discharge efficiency of the mobile energy storage vehicle m is shown.
Figure BDA00037641290200002017
In order to move the SOC of the energy storage vehicle m at the initial time,
Figure BDA00037641290200002018
the SOC is the SOC at the end time of the mobile energy storage vehicle m. E min 、E max The upper limit and the lower limit of the SOC of the mobile energy storage vehicle.
And step S4, establishing a power distribution network operation control model based on the extreme weather prediction model, the mobile space-time characteristic model and the energy model and with the aim of minimizing the load reduction weight, the mobile energy storage movement and charging and discharging weight and the distributed power generation comprehensive weight.
The elastic control method for the power distribution network coping with the extreme weather mainly comprises extreme weather prediction, a distributed power supply, a mobile energy storage system and power distribution network reconstruction, wherein the extreme weather prediction model, the mobile model of the mobile energy storage system and the energy model are as described in the above sections. The objective function can be expressed as the following formula, and the load reduction weight, the mobile energy storage movement and charge-discharge weight and the distributed power generation comprehensive weight are minimum as targets. Wherein
Figure BDA0003764129020000211
In order to move the energy storage vehicle to a moving state,
Figure BDA0003764129020000212
the charging and discharging power of the energy storage vehicle is provided.
An objective function of
Figure BDA0003764129020000213
In the above formula, T is a time set, N is a power distribution network node set, and R is a road network node and a power change station node set. C cut The method comprises the following steps of (1) reducing weight factors for units of loads, and dividing the units into three levels according to the importance degree of the loads; c gen Dividing unit power generation weight factors of the distributed power supply into different values according to the type of the power supply; c mob A single movement weight factor for the mobile energy storage vehicle; c CH 、C DCH The weight factor is a unit charge and discharge weight factor of the mobile energy storage vehicle.
Figure BDA0003764129020000214
Load power of a node when a system normally operates at time t,
Figure BDA0003764129020000215
Load power of a node during actual operation at the time t,
Figure BDA0003764129020000216
And the output of the distributed unit of the node a at the moment t. The constraint conditions include the following 5 sections.
Wherein 5 weight factors are determined by the selected network topology and the sum of the 5 weight factors is 1.
1) Power distribution network reconfiguration constraint
Figure BDA0003764129020000217
Figure BDA0003764129020000218
Figure BDA0003764129020000221
Figure BDA0003764129020000222
Wherein N is the number of nodes of the power distribution network, including the number of balance nodes with the node number of 1, and D is the number of distributed power supplies; beta is a ij Auxiliary variables for radial networks are used to represent parent-child relationships between nodes if i is the parent node β of j ij Is 1, conversely beta ij Is 0. The constraint formula (42) indicates that the network topology is to be kept as a radial network, namely the number of closed branches is less than or equal to the difference between the number of nodes of the power distribution network and the number of distributed power supplies; constraint (43) indicates that when there is a unique parent-child relationship between node i and node j, the branch is in a closed state; constraint (44) indicates that there is at most one parent per node; and the constraint formula (45) represents that after the network is reconstructed, the node where the distributed power supply is located has no father node.
2) Flow restraint
The invention uses a Disflow power flow model, and the physical characteristic quantity selected by the power flow model is different from the traditional alternating current power flow model and is expressed in the form of a fourth power function. When the voltage of each node is given, the nonlinearity degree of a power function item in the DistFlow model is further reduced, a fourth power function is converted into a second power function, the mathematical recursion property is achieved, and the method can be well applied to radial networks, particularly power distribution networks. The power balance constraint and the node voltage relation constraint can be obtained by combining the power flow model, the mobile energy storage system model and the network reconstruction model
Figure BDA0003764129020000223
Figure BDA0003764129020000224
Figure BDA0003764129020000225
Figure BDA0003764129020000226
Figure BDA0003764129020000227
Figure BDA0003764129020000231
The above formulas are respectively the active power balance, the reactive power balance, the voltage relation of adjacent nodes, the relation of line power and current voltage, the active power loss of the line, the reactive power loss of the line, and V i t 、
Figure BDA0003764129020000232
For the node voltage and line current at time t,
Figure BDA0003764129020000233
the active power and reactive power injected by the distributed power supply for node i at time t,
Figure BDA0003764129020000234
for the active and reactive power of the load at node i at time t,
Figure BDA0003764129020000235
the charging and discharging power of the movable energy storage vehicle m at the moment t respectively,
Figure BDA0003764129020000236
for the active and reactive power of the line flowing out of node i at time t,
Figure BDA0003764129020000237
for injection at time tThe active power and the reactive power of the node i,
Figure BDA0003764129020000238
active and reactive losses are injected into the line.
3) DG constraints
Figure BDA0003764129020000239
Figure BDA00037641290200002310
Figure BDA00037641290200002311
Figure BDA00037641290200002312
In the formula
Figure BDA00037641290200002313
Respectively the active output and the reactive output of the distributed power supply connected with the node i at the time t,
Figure BDA00037641290200002314
respectively the upper limit of active power output, the upper limit of reactive power output and the upper limit of capacity, pf, of the distributed power supply DG,i The power factor of the distributed power supply connected to node i at time t.
4) Voltage amplitude constraint
Figure BDA00037641290200002315
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037641290200002316
the square of the voltage at node i at time t,
Figure BDA00037641290200002317
the upper and lower limits of the square value of the voltage of the node i.
5) Line capacity constraint
Figure BDA00037641290200002318
Figure BDA00037641290200002319
In the formula (I), the compound is shown in the specification,
Figure BDA00037641290200002320
the real and reactive power delivered for the i to j lines,
Figure BDA00037641290200002321
in order to be the upper limit of the line capacity,
Figure BDA00037641290200002322
is the square of the current value of the line at time t,
Figure BDA00037641290200002323
the upper limit of the squared line current value.
And step S5, converting the power distribution network operation control model into a mixed integer second-order cone model through linearization and second-order cone relaxation.
1) Model transformation based on M method
Combining the node voltage relationship of the power flow model and the line state can be constrained by the following inequality, namely the node voltage relationship after network reconstruction
Figure BDA0003764129020000241
Figure BDA0003764129020000242
The inequality considers the voltage relation between nodes by using a large M method, wherein M is a large positive number when alpha is ij When the line is in a closed state, the equality constraint of the equation (48) is equivalent to the inequality constraints of the equations (59) - (60), otherwise, the inequality constraints (59) - (60) do not work, and the method can reduce the number of decision variables and accelerate the solution speed.
2) Second order cone relaxation
Figure BDA0003764129020000243
Figure BDA0003764129020000244
In a power distribution network, voltage deviation is not large and power supply capacity is small, so that constraint equations (48) - (49) can be relaxed to (61) - (62), constraint represents the relationship between line power and voltage current, and U and I are square terms of voltage and current for convenience of calculation. The method can be further rewritten into a two-norm form represented by a constraint expression, and the original constraint is changed into a second-order cone constraint.
3) Linearization
In the DG constraint, the constraint formula and the constraint formula are nonlinear constraints, and the whole body is still a mixed integer second-order cone programming model by linearizing the part of the constraint formula. The constraint on the capacity of the distributed power source can be converted into a formula,
Figure BDA0003764129020000251
Figure BDA0003764129020000252
Figure BDA0003764129020000253
the constraint formula of the distributed power supply power factor can be rewritten into a constraint formula
Figure BDA0003764129020000254
As with the distributed power capacity constraint, the nonlinear constraint can be converted to a linear constraint by the same method.
Through the three mathematical transformations, the recovery strategy provided by the invention comprises the formulas (1) - (47), (52) - (53) and (55) - (64), and the converted model is a mixed integer second-order cone model.
And step S6, before the extreme weather reaches the power distribution network, predicting the travel route and the intensity of the extreme weather by using the extreme weather prediction model according to the meteorological data.
And step S7, predicting the power transmission line which has faults under the influence of extreme weather by using a power distribution network component fault model according to the predicted travelling route and intensity of the extreme weather.
And step S8, reconstructing the network of the power distribution network of the power transmission line which does not have faults under the influence of extreme weather in the power distribution network.
Step S9, solving the mixed integer second-order cone model based on the reconstructed power distribution network to obtain an optimization result of the operation of the power distribution network before the extreme weather reaches the power distribution network; the optimization result of the operation of the power distribution network comprises the output of the distributed power supply in each time period, the target station of the mobile energy storage vehicle at each moment, the charge-discharge plan of the mobile energy storage vehicle in each time period and the on-off state of each contact switch in the power distribution network at each moment.
The Gurobi9.5.0 solver was called by the YALMIP toolkit to solve.
And step S10, after the extreme weather reaches the power distribution network, updating the meteorological data into real-time meteorological data, and returning to the step of predicting the advancing route and the strength of the extreme weather by using the extreme weather prediction model according to the meteorological data to obtain the real-time optimization result of the operation of the power distribution network after the extreme weather reaches the power distribution network.
And step S11, after the power distribution network is separated in extreme weather, solving the mixed integer second-order cone model according to real-time power consumption requirements, and obtaining a real-time optimization result of the power distribution network operation afterwards.
The known meteorological data and the power distribution network data are substituted into the control strategy, and the following system optimization scheduling results can be obtained:
1. influence of extreme weather on the change of the power distribution network along with time;
2. fault conditions of each line and tower;
3. distributed power supply output arrangement in each time interval in one day;
4. moving the energy storage vehicle in a day to plan the movement (moving the target station of the energy storage vehicle at each moment);
5. the charging and discharging plan (including charging and discharging states and charging and discharging power) of the mobile energy storage vehicle at each time interval;
6. the network topology of the distribution network changes (the on-off state of each interconnection switch) at each moment.
The power distribution system may then be operated according to the above optimization results.
The invention provides a strategy for flexibly controlling the power distribution network in the extreme weather, which predicts the advancing route and the strength of the extreme weather in advance to obtain the potential fault condition of each part of the power distribution network, changes the network topology and the distributed resource output and improves the space-time distribution of the active output; in the event, according to extreme weather change conditions, the network topology is changed in real time, the system is divided into islands to continue to operate, the mobile energy storage system is regulated and controlled, and the space-time flexibility of the mobile energy storage system is fully utilized to maintain important load operation; and the mobile energy storage system and the distributed power supply are regulated and controlled again, so that the post-disaster load recovery is accelerated, the load recovery rate can be effectively improved, and the system operation cost is reduced.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A distribution network elastic control method for dealing with extreme weather is characterized by comprising the following steps:
constructing an extreme weather prediction model for predicting an extreme weather travel route and intensity;
establishing a power distribution network component fault model in which a power distribution network component fails under the influence of extreme weather;
constructing a mobile space-time characteristic model and an energy model of the mobile energy storage system;
establishing a power distribution network operation control model based on an extreme weather prediction model, a mobile space-time characteristic model and an energy model and aiming at minimizing load reduction weight, mobile energy storage movement and charge-discharge weight and distributed power generation comprehensive weight;
converting the power distribution network operation control model into a mixed integer second-order cone model through linearization and second-order cone relaxation;
before extreme weather reaches the power distribution network, predicting a traveling route and intensity of the extreme weather by using an extreme weather prediction model according to meteorological data;
according to the predicted travelling route and intensity of extreme weather, predicting the power transmission line with fault under the influence of extreme weather by using a power distribution network component fault model;
carrying out network reconstruction on the power distribution network on the power transmission lines which do not have faults under the influence of extreme weather in the power distribution network;
solving the mixed integer second-order cone model based on the reconstructed power distribution network to obtain an optimization result of the operation of the power distribution network before the extreme weather reaches the power distribution network; the optimization result of the operation of the power distribution network comprises the output of the distributed power supply at each time interval, a target station of the mobile energy storage vehicle at each moment, a charge-discharge plan of the mobile energy storage vehicle at each time interval and the on-off state of each contact switch in the power distribution network at each moment;
after extreme weather reaches the power distribution network, updating the meteorological data into real-time meteorological data, and returning to the step of predicting the travelling route and the strength of the extreme weather by using an extreme weather prediction model according to the meteorological data to obtain a real-time optimization result of the operation of the power distribution network after the extreme weather reaches the power distribution network;
and after the power distribution network leaves in extreme weather, solving the mixed integer second-order cone model according to the real-time power consumption requirement to obtain a real-time optimization result of the power distribution network operation afterwards.
2. The method for flexibly controlling the power distribution network in response to extreme weather according to claim 1, wherein the constructing of the extreme weather prediction model for predicting the travel route and the intensity of the extreme weather specifically comprises:
the typhoon tracking model after the typhoon is formed is established as
Δlnc＝a 1 (t)+a 2 (t)ψ(t)+a 3 (t)λ(t)+a 4 (t)lnc(t)+a 5 (t)θ(t)+ε c
Δθ＝b 1 (t)+b 2 (t)ψ(t)+b 3 (t)λ(t)+b 4 (t)c(t)+b 5 (t)θ(t)+b 6 (t)θ(t-Δt)+ε θ
lnI(t+Δt)＝d 1 (t)+d 2 (t)lnI(t)+d 3 (t)lnI(t-Δt)+d 4 (t)lnI(t-2Δt)+d 5 (t)T s (t)+d 6 (t)(T s (t+Δt)-T s (t))+ε l
Wherein c is the moving speed of the typhoon, a 1 (t)、a 2 (t)、a 3 (t)、a 4 (t) and a 5 (t) are the first, second, third, fourth and fifth velocity model parameters, respectively, ψ (t), λ (t) are the longitude and latitude of the typhoon center at time t, Δ θ is the typhoon moving direction with the due north direction as a reference, I (t) is the relative intensity of the typhoon at time t, Δ t is the time interval, b 1 (t)、b 2 (t)、b 3 (t)、b 4 (t) and b 5 (t) are respectively a first, a second,Third, fourth and fifth orientation model parameters, d 1 (t)、d 2 (t)、d 3 (t)、d 4 (t) and d 5 (t) first, second, third, fourth and fifth relative intensity model parameters, ε c 、ε θ 、ε l Respectively the random errors of the moving speed, direction and relative intensity of the typhoon, c (T) is the moving speed of the typhoon at the time T, theta (T) is the moving direction of the typhoon at the time T, T s (t) is the ocean surface temperature at time t;
establishing a wind power field model under the influence of typhoon according to the typhoon tracking model;
according to the typhoon tracking model, a precipitation model under the influence of typhoon is established as
RA(r,t)＝k(t)k 1 (t)s(t)R(r,t)
k(t)＝0.0319Δp(t)-0.0395,k≥1
Figure FDA0003764129010000021
Figure FDA0003764129010000022
Wherein RA (R, t) is the rainfall at radius R at time t, R (R, t) is the first auxiliary variable, k (t), k 1 (t) and s (t) are correction coefficients of typhoon intensity, central air pressure change rate and moving speed respectively,
Figure FDA0003764129010000023
as the rate of change of the central air pressure, R wm (t) is the maximum wind speed radius at time t,
Figure FDA0003764129010000024
Δ p (t) is the central pressure difference of the typhoon,
Figure FDA0003764129010000025
the first random error of the normal distribution.
3. The method for resilient control of power distribution networks in response to extreme weather as claimed in claim 2, wherein the typhoon tracking model further comprises: the intensity model of typhoon on sea and the intensity model of typhoon after landing;
the strength model of the typhoon on at sea is
Figure FDA0003764129010000031
Δp 1 (t)＝p a -p c (t)
Wherein, I 1 (t) is the relative intensity of the typhoon at sea, Δ p 1 (t) the central pressure difference of the typhoon at sea, p da Surface value, p, of the partial pressure of ambient dry air dc Surface value, p, of the central pressure of the drying air which can be maintained at a minimum a At ambient pressure, p c (t) typhoon central air pressure at time t;
the intensity model after typhoon landing is
Δp 2 (t)＝Δp(t ldf )exp(-a d ·(t-t ldf ))，t＞t ldf
Figure FDA0003764129010000032
Wherein, Δ p 2 (t) is typhoon center pressure difference at t moment after typhoon landing, t ldf For typhoon landing time, a d Δ p (t) as a decay constant ldf )、c(t ldf )、R wm (t ldf ) Respectively the central pressure difference, the moving speed and the maximum wind speed radius at the typhoon landing time, a d0 、a d1 A first and a second attenuation parameter respectively,
Figure FDA0003764129010000033
is a zero-mean normal distribution error.
4. The method for controlling elasticity of a power distribution network in response to extreme weather according to claim 2, wherein the wind farm model comprises: a first gradient wind speed model with gradient wind speed at the height of a gradient and above and a second gradient wind speed model with gradient wind speed below the height of the gradient;
the first gradient wind speed model is
Figure FDA0003764129010000034
Figure FDA0003764129010000035
B(t)＝1.881-0.00557R wm (t)-0.01295ψ(t)+ε B
Figure FDA0003764129010000036
Figure FDA0003764129010000041
Wherein, V G (r, t) is the gradient wind speed at a distance r from the center of the typhoon at time t, A (r, t) is the second auxiliary variable, f r Is the Coriolis coefficient; b (t) is the Holland pressure profile shape parameter, ρ is the air density, ε B Is the second random error of normal distribution, omega is the earth's rotation speed,
Figure FDA0003764129010000042
the latitude at a distance r from the center of the typhoon;
the second gradient wind speed model is
Figure FDA0003764129010000043
Wherein V (r, t, h) is the gradient wind speed at the position of r and h from the center radius of hurricane at the moment t, and alpha r Is a power law index, h, related to terrain roughness G Is the gradient height.
5. The method for flexibly controlling the power distribution network in response to extreme weather according to claim 4, wherein the establishing of the power distribution network component fault model in which the power distribution network component fails under the influence of the extreme weather specifically comprises:
according to the second gradient wind speed model, determining a gradient wind speed model of the position of the power distribution network component as
Figure FDA0003764129010000044
Figure FDA0003764129010000045
Wherein, V (r) cm (t),t,h d ) Is a distance h from the distribution network component d At 3 seconds gust wind speed, V G (r cm (t), t) gradient wind speed at the location of the distribution network component, r cm (t) distance, α, from distribution network component position to typhoon center cm Is the power law index of the part position, (x) cm ,y cm ) And (x), (t), y (t)) are coordinates of the transmission unit and the typhoon center, respectively, G τ Is a gust factor; the power distribution network component comprises a transmission tower and a transmission line;
combining the gradient wind speed model of the position of the distribution network component with the precipitation model, establishing a transmission tower fault model of the transmission tower which has faults under the influence of typhoons as
Figure FDA0003764129010000046
Figure FDA0003764129010000047
V * (r tw (t),t,h d )＝V(r tw (t),t,h d )+1.4411f RA f V
f RA ＝0.09376(RA(r tw (t),t)) 0.7087
f V ＝exp(0.004484)V(r tw (t),t)-1.2486exp(-0.1921)V(r tw (t),t,h d ))
Wherein, P tw,i (t) is the fault probability of the transmission tower i at the moment t,
Figure FDA0003764129010000051
σ tw,i respectively, logarithmic mean and standard deviation, x tw,i (t) is the natural logarithm of the equivalent wind speed, V * (r tw (t),t,h d ) For transmission tower at h d At an equivalent wind speed, RA (r) tw (t), t) is the rainfall at the transmission tower location, r tw (t) is the distance from the position of the transmission tower to the center of the typhoon, f RA Is an intermediate variable, f V Is a transition variable;
combining the gradient wind speed model of the position of the distribution network component with the precipitation model, and establishing a transmission line fault model of the transmission line which has faults under the influence of typhoons as
P sg,j (t)＝(1-P sg,j (t-Δt))(1-exp(-λ sg,j (t)Δt))+P sg,j (t-Δt),t≥Δt
λ sg,j (t)＝L sg exp(f wr )
Figure FDA0003764129010000052
Wherein, P sg,j (t)、P sg,j (t-delta t) is the fault rate of the jth section in the power transmission line at the time t and the time t-delta t, L sg For the length of the segment, f wr Is a thirdAuxiliary variable, V (r) sg (t),t,h d )、RA(r sg (t) and t) are respectively the wind speed and precipitation at the jth section position in the power transmission line, V sgd 、RA sgd Respectively the design wind speed and the rainfall a of the jth section in the transmission line sg 、b sg 、c sg Respectively a first, a second and a third parameter, lambda sg,j (t) is the initial failure rate of the jth section in the power transmission line at the time t;
according to the transmission tower fault model and the transmission line fault model, determining a fault probability model of a transmission path where the transmission tower and the transmission line are located as
Figure FDA0003764129010000053
P sg,j (t)＝(1-P sg,j (t-Δt))(1-exp(-λ sg,j (t)Δt))+P sg,j (t-Δt),t≥Δt
Wherein, P l (t) probability of failure of the transmission path at time t, n tw 、n sg The number of the transmission tower and the number of the transmission line sections are respectively.
6. The method for flexibly controlling the power distribution network in response to the extreme weather as claimed in claim 1, wherein the model of the mobile energy storage system based on the time-space characteristics is
Figure FDA0003764129010000061
Figure FDA0003764129010000062
Figure FDA0003764129010000063
Figure FDA0003764129010000064
Figure FDA0003764129010000065
Wherein R is a road network node and a power station changing node set,
Figure FDA0003764129010000066
an arc between a power change station a and a power change station b is defined, M is a set of mobile energy storage vehicles, and T is a set of time;
Figure FDA0003764129010000067
if the energy storage vehicle m moves from the power changing station a to the power changing station b at the time t, 0 means not going to, and 1 means going to; r - 、R + Respectively a departure time and an arrival time,
Figure FDA0003764129010000068
at the time 1, the energy storage vehicle m is in a state from the power change station a to the power change station b,
Figure FDA0003764129010000069
the state that the energy storage vehicle m departs from the power changing station a at the time 0,
Figure FDA00037641290100000610
at the time T, the energy storage vehicle m is in a state from the power change station a to the power change station b,
Figure FDA00037641290100000611
the state that the energy storage vehicle m reaches the battery replacement station b at the moment T;
the energy model is
Figure FDA00037641290100000612
Figure FDA00037641290100000613
Figure FDA00037641290100000614
Figure FDA0003764129010000071
Figure FDA0003764129010000072
Figure FDA0003764129010000073
Figure FDA0003764129010000074
Figure FDA0003764129010000075
Figure FDA0003764129010000076
Wherein the content of the first and second substances,
Figure FDA0003764129010000077
for the charging power of the mobile energy storage vehicle m at the power changing station a at the time t,
Figure FDA0003764129010000078
for storing energy for movementThe maximum charging power allowed by the vehicle m,
Figure FDA0003764129010000079
the discharging power of the energy storage vehicle m at the power changing station a is moved at the time t,
Figure FDA00037641290100000710
in order to move the maximum discharge power allowed by the energy storage vehicle m,
Figure FDA00037641290100000711
the energy storage vehicle m is always in the state of the power change station a at the moment t;
Figure FDA00037641290100000712
in order to obtain the maximum charging power allowed by the power station,
Figure FDA00037641290100000713
maximum discharge power allowed for the charging station, δ t CH，m For changing the state of charge of the station, delta t DCH，m Discharging state quantity for the power change station;
Figure FDA00037641290100000714
the electric quantity SOC and eta of the energy storage vehicle m are moved at the moment of t +1 CH,m 、η DCH,m Respectively the charging and discharging efficiency of the mobile energy storage vehicle m,
Figure FDA00037641290100000715
for the SOC at the end of the moving energy storage vehicle m,
Figure FDA00037641290100000716
for moving the SOC, E of the energy storage vehicle m at the initial moment min 、E max Respectively the upper limit, the lower limit, eta of the SOC of the mobile energy storage vehicle DCH，m For storing m discharge power of the vehicle, eta CH，m And charging power for the energy storage vehicle m.
7. The method for flexibly controlling the power distribution network in response to the extreme weather as claimed in claim 6, wherein the objective function of the power distribution network operation control model is
Figure FDA00037641290100000717
Wherein Z is a target function, N is a power distribution network node set, and C cut Reducing the weighting factor for a unit of load, C gen Weight factor for unit generation of distributed power, C mob For moving the weight factor of a single movement of the energy-storing vehicle, C CH 、C DCH Is a unit charging and discharging weight factor of the mobile energy storage vehicle,
Figure FDA0003764129010000081
for the load power of the node when the system is normally operated at the time t,
Figure FDA0003764129010000082
for the load power of the node at the actual operation time t,
Figure FDA0003764129010000083
and the output of the distributed unit of the node a at the moment t.
8. The method for flexibly controlling the power distribution network in response to the extreme weather according to claim 7, wherein the constraint conditions of the power distribution network operation control model comprise: power distribution network reconstruction constraint, power flow constraint, DG constraint, voltage amplitude constraint and line capacity constraint;
the power distribution network is constrained by reconfiguration
Figure FDA0003764129010000084
Figure FDA0003764129010000085
Figure FDA0003764129010000086
Figure FDA0003764129010000087
Wherein N is the number of nodes of the power distribution network, D is the number of distributed power supplies, and alpha ij The state is a circuit opening and closing state; beta is a ij And beta ji Representing a parent-child relationship between the node i and the node j; if i is the parent of j, then β ij Is 1, conversely beta ij Is 0; if j is the parent node of i, then β ji Is 1, conversely beta ji Is 0; psi (i) is a bus connected to node i;
the power flow constraint is
Figure FDA0003764129010000088
Figure FDA0003764129010000089
Figure FDA00037641290100000810
Figure FDA00037641290100000811
Figure FDA00037641290100000812
Figure FDA0003764129010000091
Wherein the content of the first and second substances,
Figure FDA0003764129010000092
respectively the active power and the reactive power injected by the distributed power supply at the node i at the time t,
Figure FDA0003764129010000093
respectively the active power and the reactive power of the load of the node i at the time t,
Figure FDA0003764129010000094
respectively the active power and the reactive power transmitted from the node i to the node j of the line at the time t,
Figure FDA0003764129010000095
respectively injecting active power and reactive power of the node i at the time t,
Figure FDA0003764129010000096
active and reactive losses, V, respectively, of the injection line i t 、
Figure FDA0003764129010000097
Respectively the node voltage and the line current at time t,
Figure FDA0003764129010000098
the square values r of the voltages of the node j and the node i at the time t ij Is the resistance between node i and node j, x ij Is the reactance between node i and node j, V i Is the voltage at node i;
the DG is constrained to
Figure FDA0003764129010000099
Figure FDA00037641290100000910
Figure FDA00037641290100000911
Figure FDA00037641290100000912
Wherein the content of the first and second substances,
Figure FDA00037641290100000913
respectively the upper limit of active power output, the upper limit of reactive power output and the upper limit of capacity, pf, of the distributed power supply DG,i The power factor of the distributed power supply connected with the node i at the moment t;
the voltage amplitude is constrained to
Figure FDA00037641290100000914
Wherein the content of the first and second substances,
Figure FDA00037641290100000915
the upper limit and the lower limit of the square value of the voltage of the node i are defined;
the line capacity constraint is
Figure FDA00037641290100000916
Figure FDA00037641290100000917
Wherein the content of the first and second substances,
Figure FDA00037641290100000918
in order to be the upper limit of the line capacity,
Figure FDA00037641290100000919
is the square of the current value of the line at time t,
Figure FDA00037641290100000920
the upper limit of the squared line current value.
9. The method for flexibly controlling the power distribution network in response to extreme weather as claimed in claim 8, wherein the converting the power distribution network operation control model into the mixed integer second order cone model by linearization and second order cone relaxation specifically comprises:
constraining the power flow by the large M method
Figure FDA0003764129010000101
Is converted into
Figure FDA0003764129010000102
And
Figure FDA0003764129010000103
wherein M is a positive number;
constraining power flow
Figure FDA0003764129010000104
And
Figure FDA0003764129010000105
performing second-order cone relaxation to obtain a second-order cone constraint of
Figure FDA0003764129010000106
And
Figure FDA0003764129010000107
constrain DG
Figure FDA0003764129010000108
Linearization to
Figure FDA0003764129010000109
Figure FDA00037641290100001010
Figure FDA00037641290100001011
Figure FDA00037641290100001012
Constrain DG
Figure FDA00037641290100001013
Linearization to
Figure FDA00037641290100001014
Wherein the content of the first and second substances,
and forming a mixed integer second-order cone model by using the target function, the untransformed constraint in the constraint condition and the transformed constraint in the constraint condition.
10. The method for flexibly controlling the power distribution network in response to extreme weather as claimed in claim 1, wherein the step of solving the mixed integer second order cone model based on the reconstructed power distribution network to obtain the optimization result of the operation of the power distribution network before the extreme weather reaches the power distribution network specifically comprises the steps of:
based on the reconstructed power distribution network, calling a Gurobi9.5.0 solver to solve the mixed integer second-order cone model through a YALMIP tool packet, and obtaining an optimization result of the operation of the power distribution network before extreme weather reaches the power distribution network.
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