CN113469750A - Charging station and power distribution network coordinated planning method and system considering extreme weather - Google Patents
Charging station and power distribution network coordinated planning method and system considering extreme weather Download PDFInfo
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Abstract
The invention relates to a coordinated planning method for a charging station and a power distribution network considering extreme weather, which comprises the following steps: determining the probability of the extreme weather by adopting a probability distribution function fitting mode according to the rule of the extreme weather; constructing a target function containing investment cost, operation cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather; constructing a constraint condition model comprising power distribution network operation constraint, traffic network operation constraint and investment and construction constraint; and solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network. The invention considers the possibility that the distributed power supply and the electric vehicle charging station jointly form an island in extreme weather, thereby continuously supplying power to the load as much as possible and being better suitable for the extreme weather.
Description
Technical Field
The invention relates to the technical field of power distribution network-charging station-traffic network planning, in particular to a charging station and power distribution network coordinated planning method and system considering extreme weather.
Background
The electric automobile uses clean electric power as the energy, can not produce the emission in the use, and the noise is also extremely low, uses renewable energy power generation to make electric power production process realize the zero release, reduces emission and non-renewable energy consumption that traditional energy power generation brought.
The development of the electric automobile power battery technology is slow, the endurance mileage of the electric automobile is not as good as that of a fuel vehicle, and the time required by conventional charging is longer, so that the quick charging technology is used, the use experience of a user is improved, and the method becomes an inevitable choice for popularizing the electric automobile, but the charging power of dozens to hundreds of kilowatts of each electric automobile and the randomness and the fluctuation of the power generated and output of renewable energy can greatly influence the safe and stable operation of a power distribution network during quick charging. In addition, due to climate change, extreme weather with low probability and high loss occurs more frequently, the distribution network is greatly affected, multiple faults easily cause long-time power failure and even network breakdown of users, the influence of the extreme weather is not considered in the original distribution network planning method, and the capacity of a charging station, renewable energy sources and the like serving as emergency power supplies to supply power to loads in the extreme weather is not considered, so that the applicability of the distribution network-charging station-traffic network planning scheme to the extreme weather is low.
Disclosure of Invention
The invention aims to provide a charging station and power distribution network coordinated planning method and system considering extreme weather so as to improve the applicability of a power distribution network-charging station-traffic network planning scheme to the extreme weather.
In order to achieve the purpose, the invention provides the following scheme:
the invention provides a coordinated planning method for a charging station and a power distribution network considering extreme weather, which comprises the following steps:
determining the probability of the extreme weather by adopting a probability distribution function fitting mode according to the rule of the extreme weather;
constructing a target function containing investment cost, operation cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather;
constructing a constraint condition model comprising power distribution network operation constraint, traffic network operation constraint and investment and construction constraint;
and solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
Optionally, according to the rule of extreme weather occurrence, a mode of probability distribution function fitting is adopted, and the probability of extreme weather occurrence is determined as follows: p is 1-P [ n ]ext＝0]＝1-e-λ；
Where P is the probability of the occurrence of end weather, Pnext＝0]Representing the probability of no extreme weather occurring within 1 year, and lambda is a poisson distribution parameter.
Optionally, the objective function is:
minF＝afinv+fope＝afinv+fope,nor+fope,ext；
wherein F is an objective function value, a represents an annual investment value coefficient, finv represents investment cost, and FopeRepresenting the running cost, fope,norFor the operating costs under normal conditions, fope,extRepresenting the distribution network-traffic network elasticity cost in extreme weather.
Optionally, the solving the objective function based on the constraint condition model to obtain an optimal planning scheme for coordination planning of the charging station, the power distribution network, and the traffic network specifically includes:
and solving the objective function by adopting a CPLEX solver or a Gurobi solver based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
A charging station and distribution network coordinated planning system that accounts for extreme weather, the planning system comprising:
the extreme weather occurrence probability determining module is used for determining the occurrence probability of extreme weather by adopting a probability distribution function fitting mode according to the extreme weather occurrence rule;
the target function construction module is used for constructing a target function containing investment cost, running cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather;
the constraint condition model building module is used for building a constraint condition model containing power distribution network operation constraint, traffic network operation constraint and investment and construction constraint;
and the objective function solving module is used for solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
Optionally, the module for determining the probability of the extreme weather occurrence includes:
the probability determination submodule for determining the extreme weather occurrence probability is used for adopting a mode of probability distribution function fitting according to the extreme weather occurrence rule, and determining the extreme weather occurrence probability as follows: p is 1-P [ n ]ext＝0]＝1-e-λ；
Where P is the probability of the occurrence of end weather, Pnext＝0]Representing the probability of no extreme weather occurring within 1 year, and lambda is a poisson distribution parameter.
Optionally, the objective function is:
minF＝afinv+fope＝afinv+fope,nor+fope,ext；
wherein F is an objective function value, a represents an annual investment value coefficient, and FinvRepresenting investment costs, fopeRepresenting the running cost, fope,norFor the operating costs under normal conditions, fope,extRepresenting the distribution network-traffic network elasticity cost in extreme weather.
Optionally, the objective function solving module specifically includes:
and the objective function solving submodule is used for solving the objective function by adopting a CPLEX solver or a Gurobi solver based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a coordinated planning method for a charging station and a power distribution network considering extreme weather, which comprises the following steps: determining the probability of the extreme weather by adopting a probability distribution function fitting mode according to the rule of the extreme weather; constructing a target function containing investment cost, operation cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather; constructing a constraint condition model comprising power distribution network operation constraint, traffic network operation constraint and investment and construction constraint; and solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network. The invention considers the possibility that the distributed power supply and the electric vehicle charging station jointly form an island in extreme weather, thereby continuously supplying power to the load as much as possible and being better suitable for the extreme weather.
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 coordinated planning method for a charging station and a power distribution network considering extreme weather according to the present invention;
fig. 2 is a schematic diagram of a coordinated planning method of a charging station and a power distribution network considering extreme weather according to the present invention;
fig. 3 is a schematic diagram of a charging path of a vehicle entering a charging station and a virtual path of the vehicle without the charging station according to 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 charging station and power distribution network coordinated planning method and system considering extreme weather so as to improve the applicability of a power distribution network-charging station-traffic network planning scheme to the extreme weather.
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 coordinated planning method for a charging station, a power distribution network and a traffic network in consideration of extreme weather, which is characterized by establishing a coordinated planning model with the lowest annual investment and operation cost as a target, considering the influence in the extreme weather, modeling the extreme weather occurrence rule and the influence of the extreme weather on the power distribution network and the traffic network, and dividing the operation cost into two parts of operation cost under normal conditions and distribution network-traffic network elastic cost under the extreme weather. The constraint conditions comprise power distribution network operation constraint, traffic network operation constraint, investment and construction constraint.
Specific embodiments of the present invention will be described below with reference to ice and snow weather as an example.
As shown in fig. 1 and 2, the present invention provides a coordinated planning method for a charging station and a power distribution network considering extreme weather, wherein the planning method comprises the following steps:
and 101, determining the probability of the extreme weather by adopting a probability distribution function fitting mode according to the rule of the extreme weather.
The probability of k occurrences in extreme weather in 1 year is expressed by poisson distribution:
p [ n ] in formula (1)ext＝k]Represents the probability of k extreme events within 1 year, and lambda is a Poisson distribution parameter
Summary of extreme weather occurrencesRate piceThe probability of subtracting k to 0 from 1 can be used to obtain equation (2), and the intensity of the extreme weather occurrence can be represented by fitting a probability distribution according to historical data statistics.
pice＝1-P[next＝0]＝1-e-λ (2)
The influence of ice and snow weather on the power grid is mainly reflected in ice load and wind load applied to the line, wherein the ice load is determined by the thickness of ice, and the thickness r of the iceiceThe calculation is as follows:
in the formula (3), T is the duration of extreme weather, pi is the circumferential rate, rhoiAnd ρwDensity of ice and water, respectively, v denotes wind speed, prFor rainfall, W is the liquid water content, and W can be 0.067 (p)r)0.846And (4) calculating.
Ice load l caused by ice adhering to the lineiceThe calculation is as follows:
lice＝9.8×10-3×ρiπ(d+rice)rice (4)
in the formula (4), d is the wire diameter.
Wind load lwindThe calculation is as follows:
lwind＝CS(vm)2(d+2rtce) (5)
in the formula (5), C is taken as a constant of 6.964x10-3S is the rotation coefficient 1, vmIs the maximum wind speed.
Since wind loads are generally horizontal and ice loads are generally vertical, the total load on the wire is as follows:
the influence of ice and snow weather on the power distribution network is mainly reflected in ice load and wind load applied to a line, so that the fault rate of a wire is influenced, and the calculation is as follows:
p in formula (7)fail(lline) For the fault rate of the conductor under the total load of equation (6), PnorThe failure rate under normal conditions, a and b are respectively the lower limit value of stress when the failure rate begins to rise and the upper limit value of stress when the failure rate is 1, and are provided by the experiment of a lead manufacturer.
The impact on the traffic network is mainly reflected on the federal Road administration (BPR) function in the united states, specifically, the coefficients multiplied before the free transit time and the Road capacity, which are related to the extreme weather intensity, and can be calculated by using historical data fitting as follows:
the equations (8) and (9) are the coefficients of the influence on the free passage time and on the road traffic capacity, a1、b1、a2、b2The number is positive, the historical data of each road affected by extreme weather is fitted to determine, E is extreme weather intensity, and 24-hour cumulative precipitation can be used.
And 102, constructing an objective function comprising investment cost, running cost under normal conditions and distribution network-traffic network elasticity cost under extreme weather according to the probability of extreme weather occurrence.
The objective function is the minimum annual cost, and comprises two parts of investment cost and operation cost, as follows:
minF＝αfinv+fope (10)
in the formula (10)
The investment annual value coefficient is shown, r is the discount rate, and T is the service life of the planning equipment. The investment cost is as follows:
in formula (11), Ω (road, cd, DG) represents a device set including roads, charging station candidate points, and a Distributed Generation (DG) candidate point set, Λ represents a DG category set (DG may include various types such as wind power, photovoltaic, micro gas turbine or diesel generator), Γ represents a DG candidate model or specification set, x (cs, DG) is a binary decision variable representing whether a corresponding type of device is built at a candidate location, c (road, cs, f, cs, c, DG) is an investment cost coefficient including road unit lane investment, fixed investment of charging station, variable investment of charging station, unit capacity investment of DG, n (a, cs) is an integer decision variable for determining road expansion capacity (i.e. increasing number of lanes) and number of charging stations,
maximum capacity for a certain type of DG of a certain kind
The operation cost comprises two parts of operation cost under normal conditions and distribution network-traffic network elastic cost under extreme weather, and the operation cost under normal conditions is as follows:
in the formula (12), the normal operation cost is obtained by weighted summation of 6 scenes in 3 seasons (90 days in winter, 92 days in summer and 183 days in transition season), which respectively consider working days and rest days, and psThe ratio of the scene is obtained by dividing the number of days of the scene by 365 days, and the ratio comprises the following parts: the cost of purchasing electricity from the upper-level power grid,
for purchasing electric power, the power generation cost (fuel consumption, in formula) of Micro Turbine (MT) in DG
Multiplying 1 to generate active power shows that the time interval is 1 hour, namely, the electric energy generated in 1 hour is obtained, etamtFor micro gas turbine efficiency, QfuelThe heat value of the fuel, the power generation cost of the photovoltaic wind power is not counted), and the travel time cost of the transportation network. And c is a cost coefficient which comprises unit electricity price, unit fuel cost and unit time cost of the vehicle owner.
To know
The transit time spent by the owner who does not need to be charged and needs to be charged on the k path of rs, respectively, as the starting and ending point pairs.
The extreme weather considered by the invention is ice and snow weather, which only occurs in winter, so that only 90 days in winter need to be considered: the elastic costs of the distribution network and the traffic network are as follows:
in the formula (13), piceProbability of extreme weather occurrence, NextAccording to the Monte Carlo method, the probability distribution of the intensity obtained by modeling in the previous extreme weather is sampled and simulated for a plurality of times, and a plurality of fault scenes (each fault scene represents a topology after the fault of the power distribution network) with the front occurrence probability obtained by modeling according to the fault rate of the lead are obtained, wherein p iseFor each probability of failure scenario occurrence. t is tfailThe time of the distribution network and the traffic network in a fault state under the influence of ice and snow weather is comprehensively determined by factors such as maintenance time, re-grid connection time, road snow removal time and the like, and 4 hours are simply assumed to be needed from fault to complete recovery. ComprisesThe following parts: weighted loss load value, voltage offset penalty, traffic network time elastic cost (proportion exceeding normal traffic cost). And c is a cost coefficient, and comprises unit load loss cost, voltage offset punishment cost and traffic network elasticity cost. Gamma rayi，eA binary variable for indicating whether the load has power failure, 1 indicates that the i-node load has power failure, and vi，e，tAnd v0Representing the i-node voltage and the nominal voltage, respectively, where only the still powered node will take part in the calculation, nnode，linkedWhich indicates the number of powered-on nodes,
represents the passing cost of a vehicle without charging under the normal condition of the k path of the rs pair,
represents the traffic cost of a vehicle without charging under the extreme weather of the k path of the rs pair,
the number of vehicles that need not be charged for k path of rs pair, and the upper labeled nc is replaced by c, then the traffic elasticity cost of the vehicles that need to be charged is obtained.
And 103, constructing a constraint condition model comprising power distribution network operation constraint, traffic network operation constraint and investment and construction constraint.
The constraint conditions of the operation of the power distribution network are as follows:
equation (14) and equation (15) are the active power and reactive power balance constraints, respectively. Set of downstream nodes where δ (j) is j, set of upstream nodes where π (j) is j, iijOf line ijCurrent, rijAnd xijResistance and reactance of the line respectively, P is active power, including grid injection, gas turbine, photovoltaic, wind-powered electricity generation, active load and electric automobile load, wherein electric automobile is the load under normal condition, supplies power for the grid load for emergency power source under extreme weather, and Q is reactive power, including grid injection, gas turbine, photovoltaic, wind-powered electricity generation, reactive load.
The formula (16) and the formula (17) are respectively the power demand of the electric vehicle charging station under the normal condition of the distribution network node j and the limit of the power which can be provided for the power grid under the extreme weather, wherein omeganet，jSet of charging stations, η, representing access to j nodespFor the charging or discharging power of the vehicle per unit flow rate,
in order for the vehicle to enter the charging station,
a binary variable for whether the charging station is built or not.
Equations (18) to (24) are power grid operation constraints, wherein equation (18) is a voltage drop constraint, equation (19) is a constraint of a relation between line power and line voltage and current, equation (20) is a constraint of upper and lower node voltage limits under normal conditions, equations (21) and (22) are constraints of current and capacity of a line and a substation respectively, equation (23) is a radial constraint under normal conditions of a power distribution network, equation (24) is a radial constraint that the power distribution network is divided into a plurality of blocks due to line faults under extreme weather, parts disconnected with a main network need radial constraint of island operation, and n is a radial constraintislandThe number of blocks (including islands and parts connected to the main network) formed after the power distribution network fault. x is the number ofijAnd the binary variable of the connected state of the line is 1, the line is connected, and the line is disconnected when the binary variable is 0.
The formulae (25) to (27) areDG related constraint, formula (25) is active power constraint, which is determined by the DG model invested by the node, and formula (26) is reactive power constraint, which is related to the maximum value of active power
Current active power
And maximum power factor angle
In relation to the above, the formula (27) is an uncontrollable DG permeability constraint, the photovoltaic power and the wind power are uncontrollable DGs, and the proportion of the uncontrollable DGs to the total power of the load cannot exceed psimax。
The constraints on the operation of the traffic network are as follows:
the traffic network operation is assumed to be such that a certain proportion of the vehicles on each route k of each OD pair are to be charged to reach, and that the vehicle batteries are sufficiently large that at most one charge is required to reach any destination of the traffic network under consideration, so that for the vehicles to be charged, in addition to the transit time spent on the road, the waiting time in line at the charging station is also taken into account. If a certain road is a candidate road section constructed by a charging station, the traffic flow of the road is divided into two parts, for the part of the traffic flow entering the charging station, an access node is added at the position of the charging station, then a charging road section is used for representing a vehicle driving into the charging station, the time spent is the sum of waiting time and charging time, the charging time of the vehicle is not large, the charging time is considered to be a constant, and a virtual road section with the time of 0 represents the vehicle not entering the charging station.
The invention uses User Equalization (UE) to solve the traffic flow distribution problem of the traffic network, and realizes the following objective function of the UE:
Ω in equation (28)roadFor traffic network road set, omegacsFor the charging path set formed by the candidate charging stations, only one charging station can be established on one road, so the same letter a is used for representation. y isaIs the traffic flow of the road a and,
the traffic flow rate of a charging route formed by a Charging Station (CS) on a road a, ta(w) and
the time spent on the road and the charging path, respectively, as a function of the traffic flow, as shown in fig. 3, the time spent on the charging path also includes the waiting time and the charging time, respectively calculated using the federal highway administration (BPR) function and the davison function as follows:
formula (29) is a BPR function, wherein
For free flow transit time, caFor road capacity, Δ caIs unit lane capacity, gt(E) And gc(E) The coefficient is calculated by the equations (8) and (9). Equation (30) is the charging station charge and queuing time,
j is a Theisen function parameter for the charging time,
for the charging station capacity calculated from equation (31), PcpThe active power rating is for a single charging pile,
for charging the quantity of the electric pile CbatTo vehicle battery capacity, SOCneedThe State of Charge (SOC) that is the required Charge of the vehicle, i.e., the percentage of the required Charge to the battery capacity.
Formula (32) is a constraint that road traffic flow cannot exceed road capacity, formula (33) is a road traffic flow calculation formula, formula (34) gives charging route traffic flow, formulas (35) and (36) respectively give relations between starting and stopping points of vehicles which do not need to be charged and route traffic, formula (37) gives coefficients of extreme weather influences on trip demand, formula (38) shows that vehicles which need to be charged occupy epsilon in starting and stopping points, formula (39) and formula (40) respectively give time spent by vehicles which do not need to be charged and need to be charged on a certain route k of a certain starting and stopping point pair rs, delta is a binary variable, 1 shows that a certain route is contained in the route k, formula (41) shows that charging vehicles only need to be charged once to complete trip, and formula (42) shows that travel can be completed by charging vehicles only once) The paths which restrict the charging action of the charging vehicles to occur must be included in the currently running paths, equations (43) and (44) are conditions for realizing the charging action without the charging vehicle user in an equalizing way, equations (45) and (46) are conditions for realizing the charging action with the charging vehicle user in an equalizing way, namely the used (the traffic flow is greater than 0) paths have equal and minimum passing time, and the unused (the traffic flow is 0) paths have passing time greater than the minimum passing time (mu)rs，ncOr murs，c) M is a sufficiently large positive number and α is a binary variable.
There are constraints on the investment and construction of charging stations and distributed power supplies, as follows:
formula (47) shows that only one DG can be built at most on one DG candidate node, formula (48) shows that the number of charging piles in the charging station cannot exceed the limit, and formula (49) shows that the number of charging stations built in the region is determined in the early stage of planning.
And 104, solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordination planning of the charging station, the power distribution network and the traffic network.
The objective function of the invention can be solved by adopting a CPLEX solver or a Gurobi solver and the like.
The invention also provides a coordinated planning system for the charging station and the power distribution network considering extreme weather, which comprises the following components:
and the extreme weather occurrence probability determining module is used for determining the extreme weather occurrence probability by adopting a probability distribution function fitting mode according to the extreme weather occurrence rule.
The probability determination module for the extreme weather occurrence comprises:
the probability determination submodule for determining the extreme weather occurrence probability is used for adopting a mode of probability distribution function fitting according to the extreme weather occurrence rule, and determining the extreme weather occurrence probability as follows: p is 1-P [ n ]ext＝0]＝1-e-λ；
Where P is the probability of the occurrence of end weather, Pnext＝0]Representing the probability of no extreme weather occurring within 1 year, and lambda is a poisson distribution parameter.
And the objective function construction module is used for constructing an objective function containing investment cost, running cost under normal conditions and distribution network-traffic network elasticity cost under extreme weather according to the probability of occurrence of extreme weather.
The objective function is:
min F＝afinv+fope＝afinv+fope,nor+fope,ext；
wherein F is an objective function value, a represents an annual investment value coefficient, and FinvRepresenting investment costs, fopeRepresenting the running cost, fope,norFor the operating costs under normal conditions, fope,extRepresenting the distribution network-traffic network elasticity cost in extreme weather.
The constraint condition model building module is used for building a constraint condition model containing power distribution network operation constraint, traffic network operation constraint and investment and construction constraint;
and the objective function solving module is used for solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
The objective function solving module specifically includes:
and the objective function solving submodule is used for solving the objective function by adopting a CPLEX solver or a Grubi solver based on the constraint condition model to obtain an optimal planning scheme of the coordinated planning of the charging station, the power distribution network and the traffic network.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
on the basis of the existing coordinated planning of the power distribution network and the charging stations, the possibility of traffic network, namely road capacity expansion is introduced, and in addition, the possibility that a distributed power supply and an electric vehicle charging station jointly form an island under extreme weather is considered, so that the continuous power supply is performed on the load as much as possible. In conclusion, the invention can better adapt to more frequent extreme weather and the trend that a large number of distributed power sources and electric vehicles are connected to a power distribution network in the future.
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 (8)
1. A coordinated planning method for a charging station and a power distribution network considering extreme weather is characterized by comprising the following steps:
determining the probability of the extreme weather by adopting a probability distribution function fitting mode according to the rule of the extreme weather;
constructing a target function containing investment cost, operation cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather;
constructing a constraint condition model comprising power distribution network operation constraint, traffic network operation constraint and investment and construction constraint;
and solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
2. The method for coordinately planning a charging station and a power distribution network in consideration of extreme weather as claimed in claim 1, wherein according to the rule of occurrence of extreme weather, the probability of occurrence of extreme weather is determined by adopting a mode of probability distribution function fitting: p is 1-P [ n ]ext＝0]＝1-e-λ；
Where pp is the probability of end weather occurrence, Pnext＝0]Representing the probability of no extreme weather occurring within 1 year, and lambda is a poisson distribution parameter.
3. The method for coordinated planning of a charging station and a power distribution network considering extreme weather as claimed in claim 1, wherein the objective function is:
minF＝afinv+fope＝afinv+fope,nor+fope,ext；
wherein F is an objective function value, a represents an annual investment value coefficient, and FinvRepresenting investment costs, fopeRepresenting the running cost, fope,norFor the operating costs under normal conditions, fope,extRepresenting the distribution network-traffic network elasticity cost in extreme weather.
4. The method for coordinately planning a charging station and a power distribution network in consideration of extreme weather as claimed in claim 1, wherein the solving the objective function based on the constraint condition model to obtain an optimal planning scheme for coordinately planning a charging station, a power distribution network and a traffic network specifically comprises:
and solving the objective function by adopting a CPLEX solver or a Gurobi solver based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
5. A system for coordinated planning of charging stations and distribution networks in view of extreme weather, the system comprising:
the extreme weather occurrence probability determining module is used for determining the occurrence probability of extreme weather by adopting a probability distribution function fitting mode according to the extreme weather occurrence rule;
the target function construction module is used for constructing a target function containing investment cost, running cost under normal conditions and distribution network-traffic network elastic cost under extreme weather according to the probability of occurrence of extreme weather;
the constraint condition model building module is used for building a constraint condition model containing power distribution network operation constraint, traffic network operation constraint and investment and construction constraint;
and the objective function solving module is used for solving the objective function based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
6. The system of claim 5, wherein the module for determining the probability of the occurrence of extreme weather comprises:
the probability determination submodule for determining the extreme weather occurrence probability is used for adopting a mode of probability distribution function fitting according to the extreme weather occurrence rule, and determining the extreme weather occurrence probability as follows: p is 1-P [ n ]ext＝0]＝1-e-λ；
Where P is the probability of the occurrence of end weather, Pnext＝0]Representing the probability of no extreme weather occurring within 1 year, and lambda is a poisson distribution parameter.
7. The system of claim 5, wherein the objective function is:
minF＝afinv+fope＝afinv+fope,nor+fope,ext；
wherein F represents an objective function value, a represents an annual investment value coefficient, and FinvRepresenting investment costs, fopeRepresenting the running cost, fope,norFor the operating costs under normal conditions, fope,extRepresenting the distribution network-traffic network elasticity cost in extreme weather.
8. The system for coordination planning of a charging station and a power distribution network considering extreme weather according to claim 5, wherein the objective function solving module specifically comprises:
and the objective function solving submodule is used for solving the objective function by adopting a CPLEX solver or a Gurobi solver based on the constraint condition model to obtain an optimal planning scheme for the coordinated planning of the charging station, the power distribution network and the traffic network.
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