CN116362523A - Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability - Google Patents
Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability Download PDFInfo
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Abstract
The invention belongs to the technical field of traffic control systems, and relates to a coordinated optimization method for site selection and operation of a power exchange station considering temperature adaptability, which comprises the following steps: and acquiring historical track information and regional historical temperature information of the electric automobile, extracting a historical trip chain and a trip, and matching the temperature information. And excavating a travel characteristic model based on the travel chain and the travel space-time variable. And establishing an electric automobile energy consumption model based on the journey space-time variable and the temperature information. And simulating the travel and power conversion behaviors of the electric automobile in an expected scale by combining the travel characteristics and the energy consumption model of the electric automobile to obtain the power conversion requirement space-time distribution under different seasonal scenes. And constructing a two-stage site selection and operation strategy collaborative optimization model based on the space-time distribution of the power conversion requirements under different seasonal scenes. And solving the model by adopting an integer L-shaped algorithm. The invention has the advantages that: a balance between a fast increase in power conversion requirements and a gradient expansion of the power conversion infrastructure supply is achieved.
Description
Technical Field
The invention relates to the technical field of electric vehicle power conversion; in particular to a coordinated optimization method for site selection and operation of a power exchange station considering temperature adaptability.
Background
Electric Vehicles (EV) are widely applied due to the technical and economic superiority of energy conservation, zero exhaust emission and low maintenance cost, and have good application prospects in the fields of personal travel, network taxi service, public service fleet service, urban distribution and the like. In view of the technical bottlenecks of limited Battery life, slow charging speed and the like existing in the current state of the art of power batteries, a new Battery Swapping (Battery Swapping) technology is considered as an effective and innovative solution for alleviating mileage anxiety, and the technology exchanges a discharged electric automobile Battery with a charged Battery instead of charging, so that long waiting of Battery charging is avoided, and the core challenge is to lay out and establish a Battery Swapping station infrastructure in advance.
The energy consumption of the power battery of the electric automobile is affected by low temperature, the endurance is fast in attenuation and high in power conversion frequency, and the popularization and application of the power conversion type electric automobile in areas with worse air temperature and larger year are limited. For this real problem, when the infrastructure of the power exchange station is laid out and built in advance, consideration is needed to scientifically predict the power exchange requirement of low-temperature adaptability, dynamically plan the network of the power exchange station and optimize the operation strategy of the power exchange station. The existing power exchange station site selection and operation optimization methods are not considered, and the influence of low temperature on the power exchange requirement is not quantized.
Regarding the prediction of the power change demand of an electric automobile, the method is an important decision premise of the layout of a power change infrastructure, the expansion of a network and the determination of the scale, the existing prediction method of the power change demand obtains the total amount of the future power change demand by adopting single equivalent substitution conversion or average growth rate prediction through indexes such as population, vehicle conservation quantity, traffic flow and the like, and the power change demand has the characteristics of planning guiding performance, scale certainty, low granularity of demand measurement and the like, and has certain rationality in the early layout stage of a power change station. However, as the maturity of the market increases rapidly, the existing method will cause the layout of the power exchange station to fail to respond to the dynamic change of the actual demand. Therefore, based on real large data analysis such as the travel history track of the electric vehicle, the electricity change record, the air temperature report and the like, the travel chain of the electric vehicle and the distribution rule thereof are accurately depicted, the problem that the mechanism of the electricity change requirement rule of the electric vehicle is not clear under the low-temperature condition is solved, and the method has important academic value and practical significance. The reported application patent CN202011623663.0 obtains potential residence addresses of driving users and frequent parking addresses of vehicles based on running information of network-access vehicles and vehicle types, determines a power change area with power change requirements, and realizes power change station site selection by adopting an area clustering method according to the power change area. The technical characteristics of the patent are that a power conversion area with power conversion requirements is obtained based on vehicle running information and vehicle type excavation, so that the site selection time can be effectively shortened, but the low temperature is not considered to obviously increase the power conversion frequency, aggravate the superposition influence of the energy source supplementing pressure of the power conversion station, and the network site selection of the power conversion station is not supported by comprehensively utilizing multi-source data such as travel chain tracks, power conversion records, historical air temperature reports and the like. There is also report such as application number CN202211248941.8, starting from travel survey data such as parking, charging and replacing events and Travel Chains (TC), simulating the travel behavior of the electric motorcycle in the road network, and performing power replacing selection based on the remorse theory so as to predict the distribution situation of power replacing demands in the power replacing station. The method for acquiring the power conversion requirement distribution can also effectively acquire the charge and conversion load under finer space and time resolution, but based on travel investigation data, the type of land is often used as a regional description characteristic, the association between the constructed travel chain model and actual traffic geographic information is not tight enough, the mileage energy consumption is not estimated for the performance of a power battery and the use of an air conditioner, and the important influence factor of low temperature is ignored in the power conversion energy compensation.
Except for the prediction of the power conversion requirement, from the angles of site selection and layout supply side optimization of a power conversion station, the existing optimization method mostly gives method guidance for network coverage site selection optimization and energy management and control optimization problems of the established power conversion station respectively, and as the application number CN202210828468.4 optimizes a charge and discharge instruction control method of the power conversion station, peak clipping and valley filling are realized, energy fluctuation is stabilized, and overall benefit of the power conversion station is improved; the application number CN202011623663.0 gives a method for locating the station according to the coverage rules. The high construction cost of the electricity exchanging infrastructure layout and the high battery dynamic inventory cost and maintenance cost of the electricity exchanging station operation seriously restrict the improvement of the network service capability of the electricity exchanging station.
Disclosure of Invention
In view of the above problems, the invention provides a coordinated planning method for site selection and operation strategies of a power exchange station taking temperature influence into consideration, which can predict the space-time distribution of the power exchange requirement under fine resolution, and provides a power exchange requirement space-time distribution acquisition method taking temperature factors into consideration, and a coordinated optimization model for the site selection and operation strategies of the power exchange station based on the seasonal space-time distribution of the power exchange requirement at two stages, so that the extraction and modeling of the seasonal space-time distribution rule of the power exchange requirement are realized, and the battery inventory plan and ordered charging plan under different seasonal situations are optimized while the site selection and scale configuration scheme of the power exchange station are determined, thereby solving the contradiction among large influence of temperature change on the power exchange requirement, large seasonal fluctuation of the requirement and insufficient ductility of the power exchange infrastructure.
The first object of the invention is to provide a method for acquiring the space-time distribution of the battery change demand in consideration of temperature factors, which is based on real travel history track, battery change record, and analysis of big data such as air temperature report of the electric automobile, and the method for acquiring the space-time distribution of the battery change demand in consideration of the temperature factors, wherein statistics, CTGAN (Conditional Tabular Generative Adversarial Networks) generation and other methods are applied. On the other hand, on the basis of lean prediction of the electric vehicle power change requirement, the problem that the synchronous optimization of the layout planning and the operation management of the power change station lacks a physical model and algorithm support is solved, so that the network expansion investment risk and the seasonal change operation risk of the temperature of the power change station are avoided. The invention further aims to provide a two-stage power exchange station site selection and operation strategy collaborative optimization method based on seasonal space-time distribution of power exchange requirements, which aims to provide a power exchange station layout planning and operation strategy collaborative optimization method in the early stage of large-scale popularization of a power exchange type electric automobile through fine power exchange requirement analysis, so that the total cost of a system is effectively reduced on the premise of meeting the requirements, and the balance between rapid increase of the power exchange requirements and gradient expansion of power exchange infrastructure supply is realized.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for acquiring space-time distribution of power conversion requirements in consideration of temperature factors specifically comprises the following steps:
Step 2, acquiring GPS track data of historical travel of the electric automobile, wherein the resolution is less than one minute, and the information of each time stamp comprises vehicle ID, longitude, latitude, speed, time and current state of charge
）；
Step 3, acquiring historical meteorological data of the target city, wherein the historical meteorological data comprise hour-level historical temperature data of the day with the lowest average temperature in each season in a corresponding track data time period;
step 4, screening historical effective travel from the acquired track data and matching the historical effective travel with temperature data to acquire travel chain length probability;
step 5, constructing a travel chain model based on the actual travel behavior of the electric automobile;
step 6, constructing a journey energy consumption model;
step 7, determining the characteristic quantity of the travel chains and the probability distribution function thereof, and determining the quantity of the travel chains on each grid
Departure time of first-stage journey ∈ ->
And initial battery state of charge +. >
；
Step 8, training a journey feature variable association distribution model and sampling by an application condition generator, adopting a CTGAN model in a Python open source synthesis database SDV to construct a journey feature variable association distribution model, and applying a CTGAN condition generator to sample journey feature variables under given journey departure time period and origin-destination grid conditions;
step 9, judging the power change requirement after each stroke is finished; at the end of each trip, in the current state of charge
Selecting an observation index for a power change, the lowest psychological threshold value of the driver +.>
Selecting a reference index for the power change, < >>
For season index, the judgment rule is as follows: tracking time->
Travel reaches grid cell TAZ j Generating a power change demand at the place; otherwise, the power-exchanging requirement is not generated, and the next travel of the travel chain is continued; here, winter +.>
The value is 0.3, and the spring is +.>
And autumn->
The value is 0.25, summer +.>
The value is 0.27;
step 10, calculating the power-changing bypass loss; the power conversion behavior is an energy supplementing process outside the normal travel behavior, and the power conversion vehicle returns to the original travel chain after finishing the power conversion process; assuming a battery-change travel distance
Obeying 0 ~ 4km of uniform distribution, the commutation detour loss time is calculated as +.>
，/>
For the average speed of the last travel, the battery after power change is +. >
Normal distribution N (0.9,0.05) obeying the upper limit of the value of 1;
and 11, based on travel mode simulation, forecasting space-time distribution of the power conversion requirement by considering the influence of air temperature change.
As a preferred aspect of the present invention, step 4 further comprises the steps of:
preprocessing GPS track data, namely eliminating invalid data according to whether longitude and latitude are in the range of a research area, whether abnormal values exist in information such as speed, time and the like;
A2. judging the travel, wherein the travel comprises a travel process and a stopping process of the travel ending, and if the speed is 0 and the duration exceeds 15min, the travel ending enters the stopping process; the starting time of the next speed which is not 0 is recorded as the beginning of the next stroke;
A3. calculating historical travel characteristic variables, and counting the track data frames of each travel to obtain travel characteristic variables, wherein the travel characteristic variables comprise a departure grid unit, a departure time and a departure
Travel distance, travel time, average speed, arrival at grid cell, arrival time, parking time, arrival +.>
；
A4. Acquiring the stroke average temperature information, and expanding the average temperature of the stroke into the history effective stroke information according to matching of the departure time and arrival time of the stroke with the history temperature data;
A5. And counting the length of the travel chain and probability distribution thereof, namely connecting all travel routes within one day of a single vehicle to form a travel chain, wherein the length of the travel chain is the number of travel routes in the travel chain, ignoring samples with the length of the travel chain being more than 10 and with extremely small occupied ratio, and obtaining the discrete probability distribution condition of the length of the travel chain.
As a preferred aspect of the present invention, step 5 further comprises the steps of:
B1. the travel chain represents the time and space coordinate change of the traveler within one day, and the travel chain with index is a set and comprises travel characteristic variables
；
B2. Space chain consists of variables
、/>
and />
Indicating (I)>
and />
Is->
First->
Grid cell TAZ corresponding to departure and destination of individual journey->
Is->
First->
Travel distance of each travel;
B3. the travel chain of the space dimension is regarded as a Markov chain, and a space transition probability matrix is used
Expressed as:
wherein ,
for the period to which any travel departure time belongs, 2h is taken as resolution, < >>
Is recorded as period->
Lower slave grid cell TAZ i The departure and destination are located in the grid cell TAZ j According to the history of journey->
Data set, obtaining a spatial transition probability matrix +.>
；
B4. The time chain variable includes
, wherein ,/>
and />
Is->
First->
Departure time and arrival time of the respective journey, +.>
Is->
Each goes outLine chain->
After the journey reaches the destination +.>
Arrival time to the next, i.e., +.>
The parking time between departure times of the strokes;
B5. for travel distance
，/>
For the current state of charge of the battery at the start, < >>
Current state of charge of the battery at the time of arrival at the destination;
B6. adjacent travel characteristic variable recurrence relation in the same travel chain:
namely the first
The departure time of the secondary journey is equal to +.>
The arrival time of the secondary journey plus the parking time +.>
The departure point of the secondary journey is/>
The arrival point of the secondary trip while ignoring the energy consumption during the stop.
As a preferred aspect of the present invention, step 6 further comprises the steps of:
C1. according to travel data
Distance of travel->
And battery capacity->
Calculating the energy consumption per unit mileage in the journey>
：
The energy consumption of unit mileage when the electric automobile runs mainly comprises two aspects of power system energy consumption and temperature control system energy consumption:
C2. 
the specific expression is as follows:
wherein ,
for average speed of journey>
Is the average temperature of the travel;
C3.
the energy consumption of the temperature control system, mainly an air conditioning system, can be expressed as:
wherein ,
for the mean temperature of the journey, +.>
Travel time, air conditioner on probability and +.>
The energy consumption value is related to +.>
Proportional to the ratio;
C4. travel time
For the journey arrival time +.>
And departure time->
Difference, travel speed->
For distance of travel->
Travel time->
The ratio of the travel characteristic variables is determined or calculated from the historical travel data +.>
；
C5. Integrating the C1-C4, constructing a multi-element nonlinear model describing the relation between the stroke energy consumption and the stroke characteristic variable:
according to the historical travel data, performing parameter fitting on the model to obtain coefficients
~/>
、/>
~/>
。
As a preferred aspect of the present invention, step 7 further comprises the steps of:
D1. grid cell TAZ j Number of travel chains going up
Regarding the expected scale of the battery-powered electric vehicle in the grid unit, the calculation formula is as follows:
wherein ,
for grid cells->
Population number (person) in (a) is->
The amount (vehicle/person) is kept for a person-average motor vehicle,>
in grid cell for the intended battery-change electric vehicle +.>
Permeability (%);
D2. departure time of first-stage travel in travel chain
According to the historical trip chain data, fitting to obtain normal distribution is as follows:
D3. first-stage travel departure time
Is +.>
According to the historical trip chain data, the exponential distribution is obtained by fitting:
As a preferred aspect of the present invention, step 8 further comprises the steps of:
E1. data preparation and single table Data (Tabular Data) definition, extracting feature variables of all R strokes from historical track Data
, wherein ,/>
The resolution is 2h for the time period of the travel departure time;
departure grid cell and arrival grid cell for journey, +.>
For distance of travel>
For journey time +.>
For the stopping time after the end of the journey, here, < > is given>
Is a discrete variable, +.>
Is a continuous variable, considered as interrelated single table data,/->
A data primary key, each row in the table representing a new entity (trip);
E2. the CTGAN model is created based on a single table metadata structure, in which,
the data type of (a) is set to "category", ">
The data type of (2) is set to "numeric";
e3.ctgan model parameters and constraint settings thereof,
"performance_min_max_values" =false so that the synthesized data may contain values smaller or larger than the actual data, "performance_rotation" =true so that the synthesized data has the same decimal number as the actual data, "epochs" =500 training iterations of the GAN model 500 times, "cuda" =true allows the use of the GPU to accelerate modeling time, adding scalar inequality constraints to all continuous variable single columns so that the values are within a reasonable range;
E4. Training a CTGAN model by using real historical travel data according to E1-E3, and extracting and storing a condition generator G;
E5. calling a condition generator G to perform condition sampling, creating a condition object containing three types of discrete feature variables
Selecting the most amount of historical travel dataChecking the training effect of the unbalanced data set by calling a condition generator G (z, condition) under a given discrete feature variable combination condition to generate 500 lines of data as generated travel data;
E6. and (3) generating travel data evaluation, namely commonly introducing the generated travel data and the historical travel data under different conditions into an evaluation_quality function, evaluating the quality of the generated travel data according to a single-column and column correlation index, and considering that the quality of the generated travel data is over-closed when the quality score of the weighted evaluation index is more than 85 percent, and the training model is qualified.
As a preferred aspect of the present invention, step 11 further comprises the steps of:
F1. according to step 7D1, determining the number of travel chains of each grid cell
；
F2. Given season
According to the historical air temperature data of the national weather science data center, extracting a characteristic day-hour air temperature set which is most sensitive to air temperature in the energy consumption of the electric automobile in the season >
；
F3. Simulation implementation of TAZ from grid cells j Start of the first step
The travel chains are extracted according to the step 4A5, and the travel chain travel number is +.>
And extracting the first trip time according to the step 7>
And go out for the first time->
Confirm->
Belonging to the period->
；
F4. Obtaining spatial transition probability matrices of different time periods according to step 5B3
Sampling confirms the destination of the first journey>
；
F5. In the determined and belonged to
，/>
and />
Next, the characteristic quantity ++of the first-stage stroke remainder generated by the condition generator G according to step 8>
，/>
，/>
And calculates the first-stage stroke average speed +.>
Arrival time->
；
F6. Combining the determined characteristic quantities according to the energy consumption model of the step 6
，/>
，/>
，/>
And the average temperature of the stroke->
Calculating +.>
；
F7. Judging according to the power changing requirement in the step 9, if
Less than or equal to the minimum psychological threshold->
When the power is required to be changed, the power is smoothly transferred to F8; otherwise, no electricity changing requirement is generated, a travel chain of travel is continued normally, and the process jumps to F10;
F8. if it is
Less than the minimum psychological threshold->
In the case of a journey, then at the arrival point +.>
Generating a power change demand according to the arrival time +.>
Distribution of the demand for a change of electricity accumulated to a time period taking into account the temperature>
；
F9. According to step 10, the power conversion loss time is calculated
The method comprises the steps of carrying out a first treatment on the surface of the Further calculating the parking time of the power change behavior >
The method comprises the steps of carrying out a first treatment on the surface of the Here, the first-stage stroke end-corrected parking time is a superposition of the power-change loss time and the parking time;
F10. determining the characteristic quantity of the departure place of the next travel according to the recurrence relation of the characteristic variables of adjacent travel in the same travel chain obtained in the step 5B6
、/>
，/>
；
F11. Repeating F4-F10 until all the travel chains are completed
A simulation process of the secondary journey;
F12. repeating F3-F11 until the grid cell TAZ is completed j Number n of battery-changing vehicles j Traversing all grid cells;
F13. F2-F12 is repeated, and traveling and power conversion processes of all vehicles are simulated for each seasonal scene.
The invention further aims to provide a two-stage power exchange station site selection and operation strategy collaborative optimization method based on seasonal space-time distribution of power exchange requirements, which specifically comprises the following steps:
step 12, dividing the area into S grid cells, wherein each grid cell is a power change requirement set and a power change station candidate site set, and the step 1 is performed. Wherein the power change requirement is consistent with the space-time distribution result of the predicted power change requirement obtained in the step 11, and the distribution is that
Characterizing seasonal scenario->
Down->
Time period is in grid->
The number of electricity changing demands generated on the battery;
step 13, determining a collaborative optimization target:
(1) The construction cost and expected operation cost of the power exchange station are reduced as much as possible;
(2) The impact of the electric automobile power exchange station on the power grid load is reduced as much as possible, and the electric automobile power exchange station is converted into economic indexes, namely the charging cost of the power battery is reduced as much as possible;
(3) The power conversion requirements from each time period to the power conversion station under different seasonal scenes are met;
step 14, model assumption:
(1) Dividing a natural year into continuous K period season scenes, and having different operation strategies under different season scenes, including an inventory plan and an ordered charging plan;
(2) The time of day is uniformly divided into T time periods, and the process that the undercharged battery is fully charged once can be assumed to be completed in a single time period T;
(3) Compared with the time period length, the power change process is short, the battery supply is sufficient, and the power change time length and the queuing are negligible;
(4) The electricity prices in one time period are the same, the peak-to-valley electricity prices exist in commercial electricity in one day, and the ordered charging plan is to determine the number of batteries to be charged in each time period;
(5) The battery is full, and all the remaining undercharged batteries are fully charged in the last period of one day;
Step 15, two-stage optimization model:
g1 Symbol description
I: candidate site (grid) set to
Is an index;
j: a set of power change requirements (grids) to
Is an index;
k: seasonal scenario classification set in natural years to
Is an index;
t: a set of time periods within a day to
Is an index;
g2 The first stage determines the site selection and grade decision x of the power exchange station which does not change along with the seasonal situation;
the objective function is:
i.e. minimizing the sum of investment construction costs (including land costs and fixed facility construction costs) and the operating costs of the power exchange station, wherein the chase function
For scene->
The lower operation cost is influenced by the decision of the second stage;
the constraint conditions are as follows:
g3 The second stage, optimizing the operation strategy of the inventory plan B and the charging plan B based on the seasonal scenario on the basis of the site selection and the level decision x determined in the first stage;
the objective function is:
wherein ,
cost of maintenance of facilities
；
Stock and transfer costs of backup batteries
；
Battery charging cost
；
Power conversion income of power conversion station
；
The constraint conditions are as follows:
step 16, designing a solving algorithm of an optimization model;
h1 Observing the two-stage model in the step 15, wherein the first-stage model belongs to a 0-1 plan Binary Programming, and the second-stage model belongs to an integer plan Integer Programming, and the improved integer L-shaped algorithm is suitable for solving;
H2 The two-stage model of step 15 is expressed as a general form of stochastic programming:
wherein ,
，/>
decision variables representing the first phase, +.>
Representing a solution space of a first stage;
introduction of H3 into a New variable
The main problem (first stage problem) is converted into:
s.t. 
；
wherein ,
；
the integer L-shaped algorithm is an improvement of the stochastic programming model based on a branching and cutting algorithm, and the center thinking is: according to the binary characteristic of the main problem decision variable x, searching a solution space by adopting a branch-and-bound tree, and solving the main problem by considering fixed values on the nodes; relaxation of
Sequentially adding the optimal cuttingSearching branch-and-bound tree, iteratively solving the main problem to better approximate +.>
Until a meeting +.>
Is the optimal solution of (a);
if the second phase is assumed to be linear programming, constraint is to be imposed
Relaxation to->
Then the linear optimality cut can be constructed directly according to the dual problem, namely
wherein ,
is the probability of a scene occurrence, < >>
Is->
During iteration, the second-stage sub-problem is optimally decorrelated as a dual variable;
if the second stage in the two-stage model is an integer programming problem, an integer optimality cut defined by the first stage for 0-1 programming is adopted, namely
wherein ,
is->
Solution vector of the first stage 0-1 variable at multiple iterations,>
the definition is as follows: />
Index ∈1>
Set of->
Is->
Lower bound of (2);
in addition, as the two-stage model has no upper cost limit constraint, any feasible solution of the first stage is feasible for the problem of the second stage, no sub-problem is unbounded, and a feasibility cut needs to be added;
h4 The integer L-shaped algorithm comprises the following specific steps:
(1) Order the
For the upper bound of the objective function, initialize +.>
，/>
Or appropriately (I)>
The initial value is set to +.>
Or a suitable lower bound, ignored in solution; taking an initial main problem without adding an optimality cut as a root node, and creating a node list of a branch-and-bound tree;
(2) Order the
Selecting a node from the list as the current master question, if not, terminating；
(3) Solving the current main problem, if no feasible solution exists, the node ascertains, and jumps to (2); otherwise, order 
Turning to (4) for the optimal solution;
(4) Judging if
The current node ascertains and jumps to (2); otherwise, go to (5);
(5) Checking integer constraint (whether the value is 0 or 1), if the constraint is not satisfied, creating two new branches according to the normal branching process, adding new nodes into the child node list, jumping to (2), otherwise, jumping to (6);
(6) Calculating the desired compensation function value of the second stage problem of linear relaxation
If so, the most non-optimal solution can be cut off through the linear optimality generated by the linear relaxation problem of the second stage, and the linear optimality cut in H3 is added to the main problem corresponding to the current node and is jumped (3); otherwise go to (7);
(7) For the following
Calculate +.>
And->
If->
Update the upper bound->
Judging whether or not +.>
If it meetsThe current node ascertains and jumps to (2); otherwise, adding integer optimality cuts in H3 to the main problem corresponding to the current node and jumping to (3).
The invention has the advantages and positive effects that:
1. the method is different from a method for predicting the power conversion requirement based on travel investigation data, solves the problem that the mechanism of the power conversion requirement rule of the electric automobile is not clear under the low-temperature condition, and is based on multi-source data analysis such as real travel history tracks, power conversion records and air temperature reports of the electric automobile, sampling is performed by using a CTGAN (Conditional Tabular Generative Adversarial Networks) condition generator, a method for acquiring the space-time distribution of the power conversion requirement taking temperature factors into consideration is provided, and extraction and modeling of the seasonal space-time distribution rule of the power conversion requirement are realized;
2. The invention is different from the existing supply side demand coverage site selection method and peak clipping and valley filling energy management and control method, the application constructs a two-stage power exchange station site selection and operation strategy collaborative optimization model based on seasonal space-time distribution of power exchange demands, and the first stage does not consider seasonal scene change to determine site selection and grade decision of the power exchange station; the second stage optimizes the operation strategy of the inventory plan B and the charging plan B based on the seasonal scene, so that the balance between the rapid increase of the power change demand and the gradient expansion of the power change infrastructure supply is realized;
3. aiming at solving the NP difficult problem by the collaborative optimization model of the two-stage power exchange station site selection and operation strategy, the invention designs an improved integer L-shaped algorithm flow to obtain an optimal solution in view of the fact that the first stage belongs to a 0-1 planning model and the second stage belongs to an integer planning model.
Drawings
Other objects and attainments together with a more complete understanding of the invention will become apparent and appreciated by referring to the following description taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 is a flow chart of a method for acquiring space-time distribution of power conversion requirements taking temperature factors into consideration in an embodiment of the invention;
fig. 2 is a flowchart of a method for constructing a collaborative optimization method for site selection and operation strategies of a two-stage power exchange station based on seasonal space-time distribution of power exchange requirements in an embodiment of the invention.
Detailed Description
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.
Example 1
Fig. 1 shows a schematic overall structure according to an embodiment of the present invention.
As shown in fig. 1, the method for obtaining the space-time distribution of the power conversion requirement taking into consideration the temperature factor according to the embodiment of the invention is characterized by comprising the following steps:
Step 2, acquiring GPS track data of historical travel of the electric automobile, wherein the resolution is less than one minute, and the information of each time stamp comprises vehicle ID, longitude, latitude, speed, time and current state of charge
）。/>
And step 3, acquiring historical meteorological data of the target city, wherein the historical meteorological data comprise hour-level historical temperature data of the day with the lowest average temperature in each season in a corresponding track data time period.
Step 4, screening historical effective travel from the acquired track data and matching the historical effective travel with temperature data to acquire travel chain length probability;
preprocessing GPS track data, namely eliminating invalid data according to whether longitude and latitude are in the range of a research area, whether abnormal values exist in information such as speed, time and the like;
A2. judging the travel, wherein the travel comprises a travel process and a stopping process of the travel ending, and if the speed is 0 and the duration exceeds 15min, the travel ending enters the stopping process; the starting time of the next speed which is not 0 is recorded as the beginning of the next stroke;
A3. calculating historical travel characteristic variables, and counting the track data frames of each travel to obtain travel characteristic variables, wherein the travel characteristic variables comprise a departure grid unit, a departure time and a departure
Travel distance, travel time, average speed, arrival at grid cell, arrival time, parking time, arrival +.>
；
A4. Acquiring the stroke average temperature information, and expanding the average temperature of the stroke into the history effective stroke information according to matching of the departure time and arrival time of the stroke with the history temperature data;
A5. and counting the length of the travel chain and probability distribution thereof, namely connecting all travel routes within one day of a single vehicle to form a travel chain, wherein the length of the travel chain is the number of travel routes in the travel chain, ignoring samples with the length of the travel chain being more than 10 and with extremely small occupied ratio, and obtaining the discrete probability distribution condition of the length of the travel chain.
Step 5, constructing a travel chain model based on the actual travel behavior of the electric automobile;
B1. the travel chain represents the time and space coordinate change of the traveler within one day, and the travel chain with index is a set and comprises travel characteristic variables
；
B2. Space chain consists of variables
、/>
and />
Indicating (I)>
and />
Is->
First->
Grid cell TAZ corresponding to departure and destination of individual journey->
Is->
First->
Travel distance of each travel;
B3. the travel chain of the space dimension is regarded as a Markov chain, and a space transition probability matrix is used
Expressed as:
wherein ,
for the period to which any travel departure time belongs, 2h is taken as resolution, < >>
Is recorded as period->
Lower slave grid cell TAZ i The departure and destination are located in the grid cell TAZ j According to the history of journey->
Data set, obtain space transformerShift probability matrix->
；
B4. The time chain variable includes
, wherein ,/>
and />
Is->
First->
Departure time and arrival time of the respective journey, +.>
Is->
First->
After the journey reaches the destination +.>
Arrival time to the next, i.e., +.>
The parking time between departure times of the strokes;
B5. for travel distance
，/>
For the current state of charge of the battery at the start, < > >
Current state of charge of the battery at the time of arrival at the destination;
B6. adjacent travel characteristic variable recurrence relation in the same travel chain:
namely the first
The departure time of the secondary journey is equal to +.>
The arrival time of the secondary journey plus the parking time +.>
The departure point of the secondary journey is +.>
The arrival point of the secondary trip while ignoring the energy consumption during the stop.
Step 6, constructing a journey energy consumption model;
C1. according to travel data
Distance of travel->
And battery capacity->
Calculating the energy consumption per unit mileage in the journey>
：
The energy consumption of unit mileage when the electric automobile runs mainly comprises two aspects of power system energy consumption and temperature control system energy consumption:
C2. 
the specific expression is as follows:
wherein ,
for average speed of journey>
Is the average temperature of the travel;
C3.
the energy consumption of the temperature control system, mainly an air conditioning system, can be expressed as:
wherein ,
for the mean temperature of the journey, +.>
Travel time, air conditioner on probability and +.>
The energy consumption value is related to +.>
Proportional to the ratio;
C4. travel time
For the journey arrival time +.>
And departure time->
Difference, travel speed->
For distance of travel->
Travel time->
The ratio of the travel characteristic variables is determined or calculated from the historical travel data +. >
；
C5. Integrating the C1-C4, constructing a multi-element nonlinear model describing the relation between the stroke energy consumption and the stroke characteristic variable:
according to the historical travel data, performing parameter fitting on the model to obtain coefficients
~/>
、/>
~/>
。
Step 7, determiningDetermining the number of travel chains on each grid by using the characteristic quantity of the travel chains and the probability distribution function of the characteristic quantity
Departure time of first-stage journey ∈ ->
And initial battery state of charge +.>
；
D1. Grid cell TAZ j Number of travel chains going up
Regarding the expected scale of the battery-powered electric vehicle in the grid unit, the calculation formula is as follows:
wherein ,
for grid cells->
Population number (person) in (a) is->
The amount (vehicle/person) is kept for a person-average motor vehicle,>
in grid cell for the intended battery-change electric vehicle +.>
Permeability (%);
D2. departure time of first-stage travel in travel chain
According to the historical trip chain data, fitting to obtain normal distribution is as follows:
D3. first-stage travel departure time
Is +.>
According to the historical trip chain data, the exponential distribution is obtained by fitting:
step 8, training a journey feature variable association distribution model and sampling by an application condition generator, adopting a CTGAN model in a Python open source synthesis database SDV to construct a journey feature variable association distribution model, and applying a CTGAN condition generator to sample journey feature variables under given journey departure time period and origin-destination grid conditions;
E1. Data preparation and single table Data (Tabular Data) definition, extracting feature variables of all R strokes from historical track Data
, wherein ,/>
The resolution is 2h for the time period of the travel departure time;
departure grid cell and arrival grid cell for journey, +.>
For distance of travel>
For journey time +.>
For the stopping time after the end of the journey, here，/>
Is a discrete variable, +.>
Is a continuous variable, is regarded as single table data associated with each other, r is a data primary key, and each row in the table represents a new entity (travel);
E2. the CTGAN model is created based on a single table metadata structure, in which,
the data type of (a) is set to "category", ">
The data type of (2) is set to "numeric";
e3.ctgan model parameters and constraint settings thereof,
"performance_min_max_values" =false so that the synthesized data may contain values smaller or larger than the actual data, "performance_rotation" =true so that the synthesized data has the same decimal number as the actual data, "epochs" =500 training iterations of the GAN model 500 times, "cuda" =true allows the use of the GPU to accelerate modeling time, adding scalar inequality constraints to all continuous variable single columns so that the values are within a reasonable range;
E4. Training a CTGAN model by using real historical travel data according to E1-E3, and extracting and storing a condition generator G;
E5. calling a condition generator G to perform condition sampling, creating a condition object containing three types of discrete feature variables
Selecting the 10 largest and the 10 smallest discrete characteristic variable combinations in the historical travel data, and checking the training effect of the unbalanced data set, namely calling a condition generator G (z, condition) under a given discrete characteristic variable combination condition to generate 500 lines of data as the travel data;
E6. and (3) generating travel data evaluation, namely commonly introducing the generated travel data and the historical travel data under different conditions into an evaluation_quality function, evaluating the quality of the generated travel data according to a single-column and column correlation index, and considering that the quality of the generated travel data is over-closed when the quality score of the weighted evaluation index is more than 85 percent, and the training model is qualified.
Step 9, judging the power change requirement after each stroke is finished; at the end of each trip, in the current state of charge
Selecting an observation index for a power change, the lowest psychological threshold value of the driver +.>
Selecting a reference index for the power change, < >>
For season index, the judgment rule is as follows: tracking time- >
Travel reaches grid cell TAZ j Generating a power change demand at the place; otherwise, the power-exchanging requirement is not generated, and the next travel of the travel chain is continued; here, winter +.>
The value is 0.3, and the spring is +.>
And autumn->
The value is 0.25, summer +.>
The value was 0.27.
Step 10, calculating the power-changing bypass loss; the power conversion behavior is an energy supplementing process outside the normal travel behavior, and the power conversion vehicle returns to the original travel chain after finishing the power conversion process; assuming a battery-change travel distance
Obeying 0 ~ 4km of uniform distribution, the commutation detour loss time is calculated as +.>
，/>
For the average speed of the last travel, the battery after power change is +.>
Obeying a normal distribution N (0.9,0.05) with an upper limit of 1.
Step 11, based on travel mode simulation, forecasting space-time distribution of the power conversion requirement by considering the influence of air temperature change;
F1. according to step 7D1, determining the number of travel chains of each grid cell
；
F2. Given season k, extracting characteristic day-hour air temperature set of most sensitive electric automobile energy consumption to air temperature under the season according to historical air temperature data of national weather science data center
；
F3. Simulation implementation of TAZ from grid cells j Start of the first step
The travel chains are extracted according to the step 4A5, and the travel chain travel number is +.>
And extracting the first trip time according to the step 7 >
And go out for the first time->
Confirm->
Belonging to the period->
；/>
F4. Obtaining spatial transition probability matrices of different time periods according to step 5B3
Sampling confirms the destination of the first journey>
；
F5. In the determined and belonged to
，/>
and />
Next, the characteristic quantity ++of the first-stage stroke remainder generated by the condition generator G according to step 8>
，/>
，/>
And calculates the first-stage stroke average speed +.>
Arrival time->
；
F6. Combining the determined characteristic quantities according to the energy consumption model of the step 6
，/>
，/>
，/>
And the average temperature of the stroke->
Calculating +.>
；
F7. Judging according to the power changing requirement in the step 9, if
Less than or equal to the minimum psychological threshold->
When the power is required to be changed, the power is smoothly transferred to F8; otherwise, no electricity changing requirement is generated, a travel chain of travel is continued normally, and the process jumps to F10;
F8. if it is
Less than the minimum psychological threshold->
In the case of a journey, then at the arrival point +.>
Generating a power change demand according to the arrival time +.>
Distribution of the demand for a change of electricity accumulated to a time period taking into account the temperature>
；
F9. According to step 10, the power conversion loss time is calculated
The method comprises the steps of carrying out a first treatment on the surface of the Further calculating the parking time of the power change behavior>
The method comprises the steps of carrying out a first treatment on the surface of the Here, the first-stage stroke end-corrected parking time is a superposition of the power-change loss time and the parking time;
F10. determining the characteristic quantity of the departure place of the next travel according to the recurrence relation of the characteristic variables of adjacent travel in the same travel chain obtained in the step 5B6 
、/>
，/>
；
F11. Repeating F4-F10 until all the travel chains are completed
A simulation process of the secondary journey;
F12. repeating F3-F11 until the grid cell TAZ is completed j Number n of battery-changing vehicles j Traversing all grid cells;
F13. F2-F12 is repeated, and traveling and power conversion processes of all vehicles are simulated for each seasonal scene.
Example 2
Fig. 2 shows a schematic overall structure according to an embodiment of the present invention.
The embodiment provides a two-stage power exchange station site selection and operation strategy collaborative optimization method based on seasonal space-time distribution of power exchange requirements, which specifically comprises the following steps:
step 12, dividing the area into S grid cells, wherein each grid cell is a power change requirement set and a power change station candidate site set, and the step 1 is performed. Wherein the power change requirement is consistent with the space-time distribution result of the predicted power change requirement obtained in the step 11, and the distribution is that
Characterizing seasonal scenario->
Down->
Time period is in grid->
The number of electricity changing demands generated on the battery;
step 13, determining a collaborative optimization target:
(1) The construction cost and expected operation cost of the power exchange station are reduced as much as possible;
(2) The impact of the electric automobile power exchange station on the power grid load is reduced as much as possible, and the electric automobile power exchange station is converted into economic indexes, namely the charging cost of the power battery is reduced as much as possible;
(3) The power conversion requirements from each time period to the power conversion station under different seasonal scenes are met;
step 14, model assumption:
(1) Dividing a natural year into continuous K period season scenes, and having different operation strategies under different season scenes, including an inventory plan and an ordered charging plan;
(2) The time of day is uniformly divided into T time periods, and the process that the undercharged battery is fully charged once can be assumed to be completed in a single time period T;
(3) Compared with the time period length, the power change process is short, the battery supply is sufficient, and the power change time length and the queuing are negligible;
(4) The electricity prices in one time period are the same, the peak-to-valley electricity prices exist in commercial electricity in one day, and the ordered charging plan is to determine the number of batteries to be charged in each time period;
(5) The battery is full, and all the remaining undercharged batteries are fully charged in the last period of one day;
step 15, two-stage optimization model:
g1 Symbol description
I: candidate site (grid) set to
Is an index;
j: a set of power change requirements (grids) to
Is an index;
k: seasonal scenario classification set in natural years to 
Is an index;
t: a set of time periods within a day to
Is an index;
g2 The first stage determines the site selection and grade decision x of the power exchange station which does not change along with the seasonal situation;
the objective function is:
i.e. minimizing the sum of investment construction costs (including land costs and fixed facility construction costs) and the operating costs of the power exchange station, wherein the chase function
For scene->
The lower operation cost is influenced by the decision of the second stage;
the constraint conditions are as follows:
g3 The second stage, optimizing the operation strategy of the inventory plan B and the charging plan B based on the seasonal scenario on the basis of the site selection and the level decision x determined in the first stage;
the objective function is:
wherein ,
cost of maintenance of facilities
；
Stock and transfer costs of backup batteries
；
Battery charging cost
；
Power conversion income of power conversion station
；
The constraint conditions are as follows:
Step 16, designing a solving algorithm of an optimization model;
h1 Observing the two-stage model in the step 15, wherein the first-stage model belongs to a 0-1 plan Binary Programming, and the second-stage model belongs to an integer plan Integer Programming, and the improved integer L-shaped algorithm is suitable for solving;
h2 The two-stage model of step 15 is expressed as a general form of stochastic programming:
wherein ,
，/>
decision variables representing the first phase, +.>
Representing a solution space of a first stage;
introduction of H3 into a New variable
The main problem (first stage problem) is converted into:
s.t. 
；
wherein ,
；
the integer L-shaped algorithm is an improvement of the stochastic programming model based on a branching and cutting algorithm, and the center thinking is: according to the binary characteristic of the main problem decision variable x, searching a solution space by adopting a branch-and-bound tree, and solving the main problem by considering fixed values on the nodes; relaxation of
Adding optimal cuts in sequence does notBreaking the search branch-and-bound tree, iteratively solving the main problem to better approximate +.>
Until a meeting +.>
S.t is an abbreviation for subject to, representing the meaning obeyed;
if the second phase is assumed to be linear programming, constraint is to be imposed
Relaxation to->
Then the linear optimality cut can be constructed directly according to the dual problem, namely
wherein ,
is the probability of a scene occurrence, < >>
Is->
During iteration, the second-stage sub-problem is optimally decorrelated as a dual variable;
if the second stage in the two-stage model is an integer programming problem, an integer optimality cut defined by the first stage for 0-1 programming is adopted, namely
wherein ,
is->
Solution vector of the first stage 0-1 variable at multiple iterations,>
the definition is as follows: />
Index ∈1>
Set of->
Is->
Lower bound of (2);
in addition, as the two-stage model has no upper cost limit constraint, any feasible solution of the first stage is feasible for the problem of the second stage, no sub-problem is unbounded, and a feasibility cut needs to be added;
h4 The integer L-shaped algorithm comprises the following specific steps:
(1) Order the
For the upper bound of the objective function, initialize +.>
，/>
Or appropriately (I)>
The initial value is set to +.>
Or a suitable lower bound, ignored in solution; taking an initial main problem without adding an optimality cut as a root node, and creating a node list of a branch-and-bound tree;
(2) Order the
Selecting one from the listThe node is used as the current main problem, if the node does not exist, the node is terminated;
(3) Solving the current main problem, if no feasible solution exists, the node ascertains, and jumps to (2); otherwise, order
Turning to (4) for the optimal solution;
(4) Judging if
The current node ascertains and jumps to (2); otherwise, go to (5);
(5) Checking integer constraint (whether the value is 0 or 1), if the constraint is not satisfied, creating two new branches according to the normal branching process, adding new nodes into the child node list, jumping to (2), otherwise, jumping to (6);
(6) Calculating the desired compensation function value of the second stage problem of linear relaxation
If so, the most non-optimal solution can be cut off through the linear optimality generated by the linear relaxation problem of the second stage, and the linear optimality cut in H3 is added to the main problem corresponding to the current node and is jumped (3); otherwise go to (7);
(7) For the following
Calculate +.>
And->
If->
Update the upper bound->
Judging whether or not +.>
If yes, the current node ascertains and jumps to (2); otherwise, adding integer optimality cuts in H3 to the main problem corresponding to the current node and jumping to (3).
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The method for acquiring the space-time distribution of the power conversion requirement by considering the temperature factor is characterized by comprising the following steps of:
step 1, dividing the whole area into S regular hexagonal grids with side length of 500m as basic grid units TAZ according to the geographic information of the research area j ；
Step 2, acquiring GPS track data of historical travel of the electric automobile, wherein the resolution is less than one minute, and the information of each time stamp comprises a vehicle ID, longitude, latitude, speed, time and current charge state;
step 3, acquiring historical meteorological data of the target city, wherein the historical meteorological data comprise hour-level historical temperature data of the day with the lowest average temperature in each season in a corresponding track data time period;
step 4, screening historical effective travel from the acquired track data and matching the historical effective travel with temperature data to acquire travel chain length probability;
step 5, constructing a travel chain model based on the actual travel behavior of the electric automobile;
step 6, constructing a journey energy consumption model;
step 7, determining the characteristic quantity of the travel chains and the probability distribution function thereof, and determining the quantity of the travel chains on each grid
Departure time of first-stage journey ∈ ->
And initial battery state of charge +.>
；
Step 8, training a journey feature variable association distribution model and sampling by an application condition generator, adopting a CTGAN model in a Python open source synthesis database SDV to construct a journey feature variable association distribution model, and applying a CTGAN condition generator to sample journey feature variables under given journey departure time period and origin-destination grid conditions;
Step 9, judging the power change requirement after each stroke is finished; at the end of each trip, in the current state of charge
Selecting an observation index for a power change, the lowest psychological threshold value of the driver +.>
Selecting a reference index for the power change, < >>
For season index, the judgment rule is as follows: tracking time->
Travel reaches grid cell TAZ j Generating a power change demand at the place; otherwise, the power-exchanging requirement is not generated, and the next travel of the travel chain is continued; here, winter +.>
The value is 0.3, and the spring is +.>
And autumn->
The value is 0.25, summer +.>
The value is 0.27;
step 10, calculating the power-changing bypass loss; the power conversion behavior is an energy supplementing process outside the normal travel behavior, and the power conversion vehicle returns to the original travel chain after finishing the power conversion process; assuming a battery-change travel distance
Obeying 0 ~ 4km of uniform distribution, the commutation detour loss time is calculated as +.>
，/>
For the average speed of the last travel, the battery after power change is +.>
Normal distribution N (0.9,0.05) obeying the upper limit of the value of 1;
and 11, based on travel mode simulation, forecasting space-time distribution of the power conversion requirement by considering the influence of air temperature change.
2. The method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 4:
Preprocessing GPS track data, namely eliminating invalid data according to whether longitude and latitude are in the range of a research area, whether abnormal values exist in information such as speed, time and the like;
A2. judging the travel, wherein the travel comprises a travel process and a stopping process of the travel ending, and if the speed is 0 and the duration exceeds 15min, the travel ending enters the stopping process; the starting time of the next speed which is not 0 is recorded as the beginning of the next stroke;
A3. calculating historical travel characteristic variables, and counting the track data frames of each travel to obtain travel characteristic variables, wherein the travel characteristic variables comprise departure grid units and departure timeStarting from
Travel distance, travel time, average speed, arrival at grid cell, arrival time, parking time, arrival +.>
；
A4. Acquiring the stroke average temperature information, and expanding the average temperature of the stroke into the history effective stroke information according to matching of the departure time and arrival time of the stroke with the history temperature data;
A5. and counting the length of the travel chain and probability distribution thereof, namely connecting all travel routes within one day of a single vehicle to form a travel chain, wherein the length of the travel chain is the number of travel routes in the travel chain, ignoring samples with the length of the travel chain being more than 10 and with extremely small occupied ratio, and obtaining the discrete probability distribution condition of the length of the travel chain.
3. The method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 5:
B1. the travel chain represents the time and space coordinate change of the traveler within one day, and the travel chain with index is a set and comprises travel characteristic variables
；
B2. Space chain consists of variables
、/>
and />
Indicating (I)>
and />
Is->
First->
Grid cell TAZ corresponding to departure and destination of individual journey->
Is->
First->
Travel distance of each travel;
B3. the travel chain of the space dimension is regarded as a Markov chain, and a space transition probability matrix is used
Expressed as:
wherein ,
for the period to which any travel departure time belongs, 2h is taken as resolution, < >>
Is recorded as period->
Lower slave grid cell TAZ i The departure and destination are located in the grid cell TAZ j According to the history of journey->
Data set, obtaining a spatial transition probability matrix +.>
；
B4. The time chain variable includes
, wherein ,/>
and />
Is->
First->
Departure time and arrival time of the respective journey, +.>
Is->
First->
After the journey reaches the destination +.>
Arrival time to the next, i.e., +.>
The parking time between departure times of the strokes;
B5. For travel distance
，/>
For the current state of charge of the battery at the start, < >>
Current state of charge of the battery at the time of arrival at the destination;
B6. adjacent travel characteristic variable recurrence relation in the same travel chain:
namely the first
The departure time of the secondary journey is equal to +.>
The arrival time of the secondary journey plus the parking time +.>
The departure point of the secondary journey is +.>
The arrival point of the secondary trip while ignoring the energy consumption during the stop.
4. The method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 6:
C1. according to travel data
Distance of travel->
And battery capacity->
Calculating the energy consumption per unit mileage in the journey>
：
The energy consumption of unit mileage when the electric automobile runs mainly comprises two aspects of power system energy consumption and temperature control system energy consumption:
C2. 
the specific expression is as follows:
wherein ,
for average speed of journey>
Is the average temperature of the travel;
C3.
for the purpose of energy consumption of the temperature control system,mainly, an air conditioning system can be expressed as:
wherein ,
for the mean temperature of the journey, +.>
Travel time, air conditioner on probability and +.>
The energy consumption value is related to +.>
Proportional to the ratio;
C4. travel time 
For the journey arrival time +.>
And departure time->
Difference, travel speed->
For distance of travel->
Travel time->
The ratio of the travel characteristic variables is determined or calculated from the historical travel data +.>
；
C5. Integrating the C1-C4, constructing a multi-element nonlinear model describing the relation between the stroke energy consumption and the stroke characteristic variable:
according to the historical travel data, performing parameter fitting on the model to obtain coefficients
~/>
、/>
~/>
。
5. The method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 7:
D1. grid cell TAZ j Number of travel chains going up
Regarding the expected scale of the battery-powered electric vehicle in the grid unit, the calculation formula is as follows:
wherein ,
for grid cells->
Population number of->
Keep the quantity for the motor vehicle of people and +.>
In grid cell for the intended battery-change electric vehicle +.>
Is a permeability of (b);
D2. departure time of first-stage travel in travel chain
According to the historical trip chain data, fitting to obtain normal distribution is as follows:
D3. first-stage travel departure time
Is +.>
According to the historical trip chain data, the exponential distribution is obtained by fitting:
6. the method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 8:
E1. Data preparation and single table data definition, extracting characteristic variables of all R strokes from historical track data
, wherein ,/>
The resolution is 2h for the time period of the travel departure time; />
Departure grid cell and arrival grid cell for journey, +.>
For distance of travel>
For journey time +.>
For the stopping time after the end of the journey, here, < > is given>
Is a discrete variable, +.>
Is a continuous variable, considered as interrelated single table data,/->
A data primary key, each row in the table representing a new entity;
E2. the CTGAN model is created based on a single table metadata structure, in which,
the data type of (a) is set to "category", ">
The data type of (2) is set to "numeric";
e3.ctgan model parameters and constraint settings thereof,
"performance_min_max_values" =false so that the synthesized data may contain values smaller or larger than the actual data, "performance_rotation" =true so that the synthesized data has the same decimal number as the actual data, "epochs" =500 training iterations of the GAN model 500 times, "cuda" =true allows the use of the GPU to accelerate modeling time, adding scalar inequality constraints to all continuous variable single columns so that the values are within a reasonable range;
E4. Training a CTGAN model by using real historical travel data according to E1-E3, and extracting and storing a condition generator G;
E5. calling a condition generator G to perform condition sampling, creating a condition object containing three types of discrete feature variables
Selecting the 10 largest and the 10 smallest discrete characteristic variable combinations in the historical travel data, and checking the training effect of the unbalanced data set, namely calling a condition generator G (z, condition) under a given discrete characteristic variable combination condition to generate 500 lines of data as the travel data;
E6. and (3) generating travel data evaluation, namely commonly introducing the generated travel data and the historical travel data under different conditions into an evaluation_quality function, evaluating the quality of the generated travel data according to a single-column and column correlation index, and considering that the quality of the generated travel data is over-closed when the quality score of the weighted evaluation index is more than 85 percent, and the training model is qualified.
7. The method for obtaining the space-time distribution of the power change demand taking into consideration the temperature factor according to claim 1, wherein the method further comprises the following steps in step 11:
F1. according to step 7D1, determining the number of travel chains of each grid cell
；
F2. Given season 
Extracting the season according to the historical air temperature data of the national weather science data centerCharacteristic day-hour air temperature set for most sensitive energy consumption of lower electric automobile>
；
F3. Simulation implementation of TAZ from grid cells j Start of the first step
The travel chains are extracted according to the step 4A5, and the travel chain travel number is +.>
And extracting the first trip time according to the step 7>
And go out for the first time->
Confirm->
Belonging to the period->
；
F4. Obtaining spatial transition probability matrices of different time periods according to step 5B3
Sampling confirms the destination of the first journey>
；
F5. In the determined and belonged to
，/>
and />
Next, the characteristic quantity ++of the first-stage stroke remainder generated by the condition generator G according to step 8>
，/>
，/>
And calculates the first-stage stroke average speed +.>
Arrival time->
；
F6. Combining the determined characteristic quantities according to the energy consumption model of the step 6
，/>
，/>
，/>
And the average temperature of the stroke->
Calculating +.>
；
F7. Judging according to the power changing requirement in the step 9, if
Less than or equal to the minimum psychological threshold->
When the power is required to be changed, the power is smoothly transferred to F8; otherwise, no electricity changing requirement is generated, a travel chain of travel is continued normally, and the process jumps to F10;
F8. if it is
Less than the minimum psychological threshold->
In the case of a journey, then at the arrival point +. >
Generating a power change demand according to the arrival time +.>
Distribution of the demand for a change of electricity accumulated to a time period taking into account the temperature>
；
F9. According to step 10, the power conversion loss time is calculated
The method comprises the steps of carrying out a first treatment on the surface of the Further calculating the parking time of the power change behavior>
The method comprises the steps of carrying out a first treatment on the surface of the Here, the first-stage stroke end-corrected parking time is a superposition of the power-change loss time and the parking time;
F10. determining the characteristic quantity of the departure place of the next travel according to the recurrence relation of the characteristic variables of adjacent travel in the same travel chain obtained in the step 5B6
、/>
，/>
；
F11. Repeating F4-F10 until all the travel chains are completed
A simulation process of the secondary journey;
F12. repeating F3-F11 until the grid cell TAZ is completed j Number n of battery-changing vehicles j Traversing all grid cells;
F13. F2-F12 is repeated, and traveling and power conversion processes of all vehicles are simulated for each seasonal scene.
8. A two-stage power exchange station site selection and operation strategy collaborative optimization method based on seasonal space-time distribution of power exchange requirements is characterized by comprising the following steps:
step 12, dividing the area into S grid cells, wherein each grid cell is a power conversion requirement set and a power conversion station candidate site set, the power conversion requirement is consistent with the acquired space-time distribution result of the predicted power conversion requirement, and the distribution is that 
Characterizing seasonal scenario->
Down->
Time period is in grid->
The number of electricity changing demands generated on the battery;
step 13, determining a collaborative optimization target:
(1) The construction cost and expected operation cost of the power exchange station are reduced as much as possible;
(2) The impact of the electric automobile power exchange station on the power grid load is reduced as much as possible, and the electric automobile power exchange station is converted into economic indexes, namely the charging cost of the power battery is reduced as much as possible;
(3) The power conversion requirements from each time period to the power conversion station under different seasonal scenes are met;
step 14, model assumption:
(1) Dividing a natural year into continuous K period season scenes, and having different operation strategies under different season scenes, including an inventory plan and an ordered charging plan;
(2) The time of day is uniformly divided into T time periods, and the process that the undercharged battery is fully charged once can be assumed to be completed in a single time period T;
(3) Compared with the time period length, the power change process is short, the battery supply is sufficient, and the power change time length and the queuing are negligible;
(4) The electricity prices in one time period are the same, the peak-to-valley electricity prices exist in commercial electricity in one day, and the ordered charging plan is to determine the number of batteries to be charged in each time period;
(5) The battery is full, and all the remaining undercharged batteries are fully charged in the last period of one day;
Step 15, two-stage optimization model:
g1 Symbol description
I: candidate site sets to
Is an index;
j: a set of power change demands to
Is an index;
k: seasonal scenario classification set in natural years to
Is an index;
t: time of dayTo be assembled to
Is an index;
g2 The first stage determines the site selection and grade decision x of the power exchange station which does not change along with the seasonal situation;
the objective function is:
namely the investment construction cost and the operation of the power exchange stationThe sum of this expectation is minimized, wherein the chase function
For scene->
The lower operation cost is influenced by the decision of the second stage;
the constraint conditions are as follows:
g3 The second stage, optimizing the operation strategy of the inventory plan B and the charging plan B based on the seasonal scenario on the basis of the site selection and the level decision x determined in the first stage;
the objective function is:
wherein ,
cost of maintenance of facilities
；
Standby for useInventory and transfer costs of batteries
；
Battery charging cost
；
Power conversion income of power conversion station
；
The constraint conditions are as follows:
step 16, designing a solving algorithm of an optimization model;
h1 Observing the two-stage model in the step 15, wherein the first-stage model belongs to a 0-1 plan Binary Programming, and the second-stage model belongs to an integer plan Integer Programming, and the improved integer L-shaped algorithm is suitable for solving;
h2 The two-stage model of step 15 is expressed as a general form of stochastic programming:
wherein ,
，/>
decision variables representing the first phase, +.>
Representing a solution space of a first stage;
introduction of H3 into a New variable
The main problem is converted into：
s.t. 
；
wherein ,
；
the integer L-shaped algorithm is an improvement of the stochastic programming model based on a branching and cutting algorithm, and the center thinking is: according to the binary characteristic of the main problem decision variable x, searching a solution space by adopting a branch-and-bound tree, and solving the main problem by considering fixed values on the nodes; relaxation of
Adding the optimality cuts in turn and searching the branch-and-bound tree continuously, and iteratively solving the main problem to better approximate +.>
Until a meeting +.>
Is the optimal solution of (a);
if the second phase is assumed to be linear programming, constraint is to be imposed
Relaxation to->
Then the linear optimality cut can be constructed directly according to the dual problem, namely
wherein ,
is the probability of a scene occurrence, < >>
Is->
During iteration, the second-stage sub-problem is optimally decorrelated as a dual variable;
If the second stage in the two-stage model is an integer programming problem, an integer optimality cut defined by the first stage for 0-1 programming is adopted, namely
wherein ,
is->
Solution vector of the first stage 0-1 variable at multiple iterations,>
the definition is as follows: />
Index ∈1>
Set of->
Is->
Lower bound of (2);
in addition, as the two-stage model has no upper cost limit constraint, any feasible solution of the first stage is feasible for the problem of the second stage, no sub-problem is unbounded, and a feasibility cut needs to be added;
h4 The integer L-shaped algorithm comprises the following specific steps:
(1) Order the
For the upper bound of the objective function, initialize +.>
，/>
Or appropriately (I)>
The initial value is set to +.>
Or a suitable lower bound, ignored in solution; taking an initial main problem without adding an optimality cut as a root node, and creating a node list of a branch-and-bound tree;
(2) Order the
Selecting a node from the list as the current main problem, and terminating if the node does not exist;
(3) Solving the current main problem, if no feasible solution exists, the node ascertains, and jumps to (2); otherwise, order
Turning to (4) for the optimal solution;
(4) Judging if
Then the current node detectsBright and jump to (2); otherwise, go to (5);
(5) Checking integer constraint, if the constraint is not satisfied, creating two new branches according to a normal branching process, adding new nodes into a child node list, jumping to (2), otherwise, jumping to (6);
(6) Calculating the desired compensation function value of the second stage problem of linear relaxation
If so, the most non-optimal solution can be cut off through the linear optimality generated by the linear relaxation problem of the second stage, and the linear optimality cut in H3 is added to the main problem corresponding to the current node and is jumped (3); otherwise go to (7);
(7) For the following
Calculate +.>
And->
If->
Update the upper bound->
Judging whether or not +.>
If yes, the current node ascertains and jumps to (2); otherwise, adding integer optimality cuts in H3 to the main problem corresponding to the current node and jumping to (3).
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