CN109492791B - Inter-city expressway network light storage charging station constant volume planning method based on charging guidance - Google Patents
Inter-city expressway network light storage charging station constant volume planning method based on charging guidance Download PDFInfo
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Abstract
A constant volume planning method for an inter-city highway network optical storage charging station based on charging guidance comprises the following steps: the method comprises the steps of constructing an intercity highway network charging guide system framework, based on statistical distribution data of traffic flow of the intercity highway network, combining a charging decision of an electric vehicle after the electric vehicle utilizes the charging guide system, considering benefits of both user queuing waiting time and equipment utilization rate of an optical storage charging station, optimizing the number of charging motors in the optical storage charging station, regulating and controlling output time of energy storage equipment in the station according to illumination conditions and load levels of each station by combining time-of-use electricity prices, optimizing and configuring capacity of the optical storage equipment, promoting photovoltaic high-efficiency consumption, and further saving equipment investment cost of the optical storage charging station. The method reduces the daily average life comprehensive cost of the optical storage charging station, and effectively gives consideration to the trip experience of the user.
Description
Technical Field
The invention relates to a planning method for an electric vehicle charging station, in particular to a constant volume planning method for an electric vehicle light storage charging station on an intercity highway network.
Background
An Electric Vehicle light storage charging station is a basic guarantee for Electric Vehicles (EV) to go out between cities and provinces, and is an important measure for consuming local photovoltaic resources and solving the problems of traffic, environment and energy[1-2]. The EV for intercity trip has the characteristics of long single driving distance, limited endurance mileage and obvious psychological 'mileage anxiety' of the user[3]. In addition, because the optical storage charging stations on the intercity highway network cannot be comprehensively considered and optimizedThe charging infrastructure in the station is configured, so that the trip experience of a user is influenced, the cost is additionally increased while the resource waste is caused, and the reliable and economic operation of the optical storage charging station is endangered. Therefore, after the EV travels on a large scale between cities, how to effectively relieve the anxiety psychology of the EV user in traveling, the charging facilities in the light storage charging station are planned in a coordinated manner, the benefits of the user and the light storage charging station are taken into consideration, and the method has important theoretical significance and application value.
Currently, there are many scholars studying the volume planning of EV charging stations[4-11]The planning problem of the EV charging station is mainly divided into two types by integrating domestic and foreign documents:
planning and constructing an EV charging station under disordered charging: document [4]]In order to realize the benefit balance of independent developers of the centralized charging station and a power distribution company, the position, the capacity and the dispatching of the charging station are optimized by establishing a multi-target two-layer planning model of the centralized charging station reflecting different benefit subjects. Document [5]]And (3) unified consideration is given to the joint planning of the expansion of the energy storage system, the EV charging station and the power distribution network, and a multi-stage joint planning model with the minimum investment cost, the minimum running cost and the minimum load loss cost as the target is constructed. The EV charging station is not only a component of a power distribution network, but also an important transportation facility[6]Therefore, in planning and construction, the influence of the power system and the traffic system needs to be comprehensively considered, and the method is based on the document [7]]A traffic satisfaction model of a traffic network is provided by researching a path selection and evaluation model, and an EV charging station and distributed power constant volume location model of multiple targets with the minimum total cost, the minimum network loss and the highest traffic satisfaction are constructed, however, when the traffic satisfaction model is constructed, only the running time of an EV user is considered, and the influence of queuing waiting time in the station on the planning constant volume is ignored. Document [8]Although the planning model of the EV charging station in the urban area is provided by considering the queuing waiting time of the EV users and considering the constraints of a road network structure, traffic information, a power distribution network structure and the like, the time-varying property of the poisson distribution parameters of the users reaching the charging station is not analyzed, the queuing condition in the station cannot be accurately obtained, and the constant volume planning result of the charging station is further influenced.
Therefore, to construct more reasonable charging station capacity configuration optimizationModel, it is also necessary to accurately know EV travel data and information in charging station[9]Under the background, the constant volume planning method in the charging mode is guided by establishing a charging guide system by taking real-time data interaction as guidance:
document [10] realizes information interaction between the EV and the charging station by constructing a charging navigation framework, and constructs the charging station by using a hierarchical game strategy plan, so as to improve reliability of the power distribution network and economic benefits of the charging station. Document [11] proposes a charging guidance model and a solving algorithm based on a self-adaptive variation particle swarm algorithm, electric taxis after charging guidance are uniformly distributed to corresponding charging stations according to the scale of charging motors in the charging stations, and the balanced distribution of the utilization rate of charging facilities is effectively realized.
However, the existing literature only takes a small number of EVs in urban areas as research targets, and no research on the structure of the charging guidance system after large-scale travel of the EVs on the intercity highway network is available; in addition, for the optical storage charging station, the existing literature mainly develops work with fixed photovoltaic capacity and energy storage device capacity, and no research for optimally configuring the capacity of the optical storage device in the corresponding station by regulating and controlling the output time of the energy storage device is available.
Disclosure of Invention
The invention aims to provide a constant volume planning method for an inter-city expressway network optical storage charging station based on charging guidance, aiming at optimizing and configuring the capacity of charging infrastructure in the station according to the charging decision of an EV (electric vehicle) and the comprehensive information of the optical storage charging station by establishing an effective inter-city expressway network charging guidance system and combining time-of-use electricity price, and considering the collaborative planning of the charging station and optical storage equipment and the benefits of a user and both the charging station and the optical storage equipment.
The purpose of the invention is realized as follows: and modeling the optical storage charging station based on the charging guide. Due to the fact that charging times of the EV when the EV travels between cities are frequent, a user wants to be aware of the distribution of the sites of the optical storage charging stations in the journey, and can reduce waiting time of in-station queuing and finish charging as soon as possible. In order to take account of the convenience of users in traveling and the benefits of the optical storage charging stations, modeling of the optical storage charging stations after traveling of the EV based on the charging guidance mode is researched from the aspects of EV user queuing waiting time, power balance relation in the optical storage charging stations, equipment utilization rate and the like.
2.1 model description and assumptions
Because the charging infrastructure in the planning and configuration optical storage charging station is influenced by a lot of factors, in order to describe the corresponding constraint factors more comprehensively, on the premise of not influencing the calculation of the objective function, the following assumptions are made:
1) since service areas are arranged on the intercity highway network at intervals of 50-60 km, catering, parking rest and other services are provided for users, and in order to reduce the occupied land construction cost of the optical storage charging stations, the optical storage charging stations are assumed to be constructed in the service areas;
2) based on the characteristic that vehicles run on the highway in one direction, traffic vehicles in 2 directions do not influence each other, so that the research objects are only single-side vehicles running on the highway and single-side light storage and charging stations;
3) due to the versatility and convenience of the charging guidance system, EV users generally accept the charging guidance decisions they make;
4) the electric quantity loss when the EV starts and stops is not considered temporarily, and according to data information uploaded by the sensor, the average speed in the last 2 time periods is taken as the speed of the corresponding EV to travel to the optical storage charging station;
5) after the EV is reserved for charging, a corresponding optical storage charging station reserves a charging station for the EV, and the EV is charged at constant power.
2.2 electric vehicle queuing waiting time in optical storage charging station
Based on the charging guide system of the intercity highway network, travel information such as the charge state, the accumulated travel and the destination is uploaded by the EV in real time, when the charge state of the EV reaches a threshold value at the time T, the guide system avoids deep discharge of the EV battery and prolongs the service life of the EV battery, a charging decision is made for the EV according to the number of chargers and a charging plan of the light storage charging station in the travel, the light storage charging station with less queuing waiting time is selected to reserve a charging station, and the corresponding light storage charging station waits for the entering station to be charged.
The ith vehicle EV at an initial time Ti 0When traveling, the maximum driving mileage can be represented by the optimal driving mileage and the endurance mileage, and the calculation formulas are respectivelyAs shown in formula (1), formula (2) and formula (3).
Si＝BE_Si+MA_Si (1)
Wherein: siThe maximum driving mileage of the ith vehicle during initial travel is obtained; BE _ Si、
Respectively the optimal driving mileage and the charge state when the ith vehicle initially travels; alpha is the threshold value (0) of the state of charge of the ith vehicle<α<1)；PiThe power consumption of the ith vehicle is hundred kilometers; b isiBattery capacity of the ith vehicle; MA _ SiThe driving mileage of the ith vehicle during initial travel is taken as the driving mileage.
The cumulative residence time of the ith vehicle in the optical storage charging station j' at the moment T
Cumulative stroke SiAre respectively shown as a formula (4) and a formula (5).
Wherein: j is the serial number of the optical storage charging station in the intercity highway network; k is the charging frequency of the guide system accumulated service vehicle i; x is the number ofij'For charging flag variable, when the ith vehicle is charging to the jth light storage charging station in front of the vehicle, xij'Is 1, otherwise is 0.
The departure time of the vehicle i after the k-th completion of the charging action,
the arrival time of the corresponding EV; viThe traveling speed of the i-th vehicle.
The charging guide system obtains the light storage charging station j and the running time delta t which can be selected by the ith vehicle under the cruising mileage through the layer cloud computing of the analysis processing platformi,jAre respectively shown as a formula (6) and a formula (7).
Si≤Lj≤Si+MA_Si (6)
Wherein: l isjThe distance between the light storage charging station and the starting point of the vehicle i.
Δti,jDuring the time, the charging plan in the optical storage charging station j consists of two parts: an EV which issues a charging demand before time T and reaches the light storage charging station j and has not finished the charging action, and T + Deltati,jSending a charging demand before the moment and arriving at the EV of the optical storage charging station j based on the queuing theory[14]The waiting time WA _ T of the ith vehicle can be obtainedi,jAs shown in formula (8).
Wherein: mjThe number of vehicles reserved to be charged in the optical storage charging station j; m isjThe number of the electric motors is charged in the light storage charging station j; t is ti'j,FirLeaThe departure time of the vehicle i' leaving first in the station when the ith vehicle arrives at the optical storage charging station j; t is ti'j,CalLeaWhen the ith vehicle arrives at the light storage charging station j, the M in the stationj-mj+1 departure times of the departure vehicles i'.
2.3 Power balance relationship in optical storage charging station
The electric energy of the light storage charging station is supplied by photovoltaic power generation and a power grid, and the electric energy generated by photovoltaic is mainly used for auxiliary power generation. The photovoltaic output has certain regularity and uncertainty[15]The power balance relationship in the optical storage and charging station is as follows:
when photovoltaic output in the light storage charging station can not meet the EV power demand in the station, the electric energy is supplemented by the power grid and the energy storage equipment, and at the moment:
wherein: m is the number of sections of the charging station in the whole day, delta tmIs the duration of the m-th period, PPV,mIs the average power of the photovoltaic output in the m-th period, PBI,mAverage power discharged for energy storage device in m-th period, PG,mAverage power, P, for supplying the grid during the mth periodEV,mIs the average power of the EV load in the station during the mth period.
When photovoltaic output is greater than power demand in the station in the light stores up the charging station, surplus electricity is gone on the net, this moment:
wherein: p'BI,mAverage power, P 'for charging energy storage devices during the m-th period'G,mAnd the average power of the photovoltaic grid-connected in the mth period.
2.3 Equipment utilization in optical storage charging station
By utilizing the charging guide system, the ith vehicle selects the optical storage charging station j to charge, and the charging state of the ith vehicle when the ith vehicle arrives at the station j
As shown in equation (11).
Wherein: t + Δ Ti,jThe time when the vehicle i arrives at the optical storage charging station j,
the state of charge of vehicle i at time T.
When the optical storage charging stations of the EV on the intercity highway network are charged, the charge electric quantity can finish the residual travel or go to next charging stations in the travel by the vehicle owner in consideration of saving travel time, so that the charge electric quantity delta B of the vehicle ii,jAs shown in equation (12).
Wherein: beta is the state of charge after EV charging,
in order to uniformly compare the usage of the charging facilities in the optical storage charging stations on the inter-city highway network and measure the service intensity of the charging facilities, as shown in document [11], the device utilization rate of the charging facilities in the optical storage charging stations is defined as the ratio of the service time of the corresponding device to the time of the whole day, as shown in formula (13).
Wherein: n is the number of EV's, P, served by charging station j all dayEVThe charging power of the charger.
3 light stores up charging station modeling based on guide of charging
3.1 Objective function for optical storage charging station constant volume planning
The travel convenience requirement of a user is met, and in order to ensure the economic operation of the optical storage charging station, the operation mode of the optical storage charging station is compared with that of the traditional optical storage charging station[16]Based on the principle of preferentially consuming photovoltaic power and reducing the electricity purchasing cost from the peak time period to the power grid, the following operation strategies are proposed:
during the off-hour electricity price period (generally, no photovoltaic output exists), the power grid provides electric energy for the EV and the energy storage equipment; in the usual electricity price period, when the photovoltaic output is greater than the EV load demand, the residual electric energy is used for storing energy, otherwise, the required electric energy is supplemented by the power grid; and in the peak-hour electricity price period, when the photovoltaic output is greater than the EV load demand, the residual electric energy is used for storing energy, otherwise, the energy storage equipment supplements the required electric energy, and if the capacity of the energy storage equipment is insufficient, the electric energy is supplied by the distribution network.
Based on the optimization objective and the operation strategy, an objective function of a constant volume scheme is formulated with the minimum daily average life cycle cost of the charging infrastructure in each optical storage charging station as follows:
wherein: cj、NjThe purchase cost and the service life of the charging motor in the optical storage charging station j are respectively set; cPV,j、CBI,j、CG,j、CRES,jRespectively the purchasing cost, the electricity purchasing cost and the daily average cost after the conversion of the equipment operation and maintenance cost of the photovoltaic equipment and the energy storage equipment in the photovoltaic and energy storage charging station j in the life cycle thereof, CPV’,jThe cost is compensated for the daily average of the photovoltaic grid-connected electricity quantity.
3.2 constraints on optical storage charging station Capacity planning
3.2.1 number constraints of chargers
On one hand, because the light storage charging station has limited floor area and investment cost, the number of the charging motors in the station j has upper and lower limits:
meanwhile, in order to avoid the phenomenon that charging resources are idle or charging facilities are insufficient to cause congestion in the station due to excessive configuration of the charging motor in the optical storage charging station, the equipment utilization rate of the charging motor in the optical storage charging station has a lower limit:
on the other hand, as can be seen from equation (8), the number of the charging motors in the optical storage charging station affects the in-station queuing waiting time of the EV user, and in order to relieve the anxiety psychology of the user and facilitate the user to go out, there is an upper limit to the user queuing waiting time:
3.2.2 optical storage device Power constraints
Because photovoltaic output is influenced by factors such as typical daily illumination intensity and photovoltaic capacity in the optical storage charging station, the photovoltaic output has an upper limit:
in addition, there are upper and lower limits by the influence of energy storage equipment capacity in the light stores up charging station energy storage equipment charging and discharging power:
λBI,m·λ′BI,m＝0 (21)
λBI,m,λ′BI,m∈{0,1} (22)
wherein: lambda [ alpha ]BI,m、λ′BI,mThe variable is a 0 and 1 variable, and represents that the charging and discharging states of the energy storage device are unique in any optimization time period.
3.3 constant volume planning strategy solution
The minimum life cycle cost of the light storage charging station is taken as a target, the queuing waiting time of users is considered, and the number of the charging motors, the photovoltaic and the energy storage devices in the station are optimizedThe capacity of the device has the characteristics of multivariable, multi-constraint and nonlinearity, and the traditional optimization method is difficult to solve the complex optimization problem[17]Since the adaptive variation particle swarm optimization algorithm has high convergence speed and strong global search capability and is not easy to fall into local optimum, the problem is solved by adopting the adaptive variation particle swarm optimization algorithm. The algorithm determines the variation factor of the current best particle according to the overall evolution degree and the individual evolution degree in the particle evolution process, adjusts the inertia weight of each particle, thereby realizing the self-adaptive adjustment of the evolution speed and the position of each particle, and continuously searches after the particles enter the adjacent region to determine a new individual extreme value and a new global extreme value.
When the optical storage charging station is configured based on the adaptive variation particle swarm optimization algorithm, the formula (14) is used as an objective function, and the quantity particles of the charger, the photovoltaic capacity particles and the energy storage device capacity particles which meet constraint conditions are solved, wherein the specific steps and the flow are as follows.
1) And inputting inter-city highway network information, light storage charging station serial numbers, geographic position information and the number of EVs.
2) Initializing particle swarm, simulating and generating travel parameters of the EV, adopting a guiding charging mode, and making a charging decision by an intercity highway network charging guiding system when the charge state of the EV reaches a threshold value to guide the corresponding EV to enter a station for charging.
3) Let the maximum number of iterations be S, and the current number of iterations S be 0. And calculating the individual optimal solution and the global optimal solution of the initialized particle swarm.
4) Let the maximum particle number be Z, and the current particle number be 0, update the velocity and position of the particles.
5) Checking whether the particles meet constraint conditions, and if so, calculating a fitness function value of the current particles; if not, the fitness value of the particle is made infinite.
6) And checking whether the current fitness function value is superior to the current individual extreme value and the group extreme value, and if so, updating the individual optimal solution and the global optimal solution. Otherwise, go to step (7).
7) Let Z be Z +1, check whether the number of particles Z is equal to the maximum number of particles Z, if yes, continue to step (8); otherwise, returning to the step (5).
8) If yes, the global optimal solution is the optimal constant volume scheme of the optical storage charging station, and the solution is finished; otherwise, go to step (9).
9) And (4) calculating the group fitness variance of the current particle swarm and the variation probability of the global optimal solution, determining whether variation exists according to the random number, performing variation operation on the global optimal solution by adopting a method of increasing random disturbance, and then turning to the step (4).
Compared with the prior art, the invention has the beneficial effects that:
in order to solve the problem of optimized constant volume of charging infrastructure after large-scale travel of an EV between cities, convenience of user travel, utilization rate of equipment in a light storage charging station and daily average life comprehensive cost are considered, an intercity highway network charging guide system is constructed, a constant volume planning method of an intercity highway network light storage charging station based on charging guide is provided, and based on a simulation result, the following conclusion can be obtained:
1) by constructing the inter-city expressway network charging guidance system, the inter-city expressway network informatization level is improved, EV resources and charging facility resources in the optical storage charging station are effectively integrated, and the charging behavior of the EV is served for constant volume planning of the charging facility in the optical storage charging station. The maximum queuing waiting time in the user station is not more than 45min, the user traveling experience is improved, and the utilization rate of the charging infrastructure in the optical storage charging station is effectively improved.
2) By means of time-of-use electricity price, photovoltaic output and in-station load conditions, important support is provided for regulating and controlling output time of the energy storage equipment, after capacity of the in-station light storage equipment is optimally configured, compared with the output time without light storage equipment and without optimized energy storage equipment, 2614 yuan and 2005 yuan are saved respectively in daily life comprehensive cost, efficient utilization of photovoltaic energy is promoted, and daily life comprehensive cost of the light storage charging station is reduced.
3) With the future large-scale travel of the EV, the improvement of the capacity and the efficiency of the optical storage equipment and the reduction of corresponding cost, the optical storage charging station volume planning method provided by the text effectively takes the benefits of a user and the optical storage charging station into consideration, and provides a feasible solution for the volume planning problem of the optical storage charging station.
Drawings
Fig. 1 shows an inter-city highway network EV charging guidance system.
FIG. 2 is a flow chart of an optimized volumetric solution.
Fig. 3 is an intercity highway network.
Fig. 4 is a schematic view of the distribution of the light storage and charging station area.
Fig. 5 is a typical daily traffic flow characteristic of an expressway.
Fig. 6 is a graph of maximum queue wait time before and after optimization of the pre-fix EV.
Fig. 7 shows the utilization rate of the devices in the optical storage charging station before and after the optimization of the constant volume.
FIG. 8 shows the device utilization at different travel permeabilities.
Fig. 9 is the maximum queue wait time for different travel permeabilities.
FIG. 10 is a typical daily EV power demand.
Fig. 11 is a power balance relationship in an optical storage charging station.
Detailed Description
FIG. 1 shows an inter-city highway network charging guidance system architecture
The existing navigation system effectively avoids negative effects such as congestion and the like caused by large-scale vehicle traveling due to functions such as positioning, path guidance, destination selection and the like, so that the navigation system is widely applied to a traveling scene of a vehicle owner[12]Especially, the method is more generally applied to long-distance travel between cities. The 5G network has the characteristics of real-time performance, high communication speed and stability[13]Based on the existing research, an inter-city highway network EV charging guidance system architecture is proposed as shown in fig. 1. As can be seen from the figure, the guidance system is an information sharing system having interconnection and interworking characteristics, which uses technologies such as a cloud platform and big data on the basis of the conventional navigation technology, mainly uses an EV, uses an intercity highway network as a carrier, and is tightly coupled with a communication network. The guidance system is mainly divided into four layers of a terminal layer, a network layer, a platform layer and an application layer: application of 5G network to cityThe EV and the charging station which are in interstation travel become data terminals, the EV uploads travel time, a destination and corresponding data of the sensor, and the charging station uploads static and dynamic information such as the number of motors and charging plans in the station. The network layer solves the communication problem between the EV and the charging station, and the platform layer realizes data interaction in the guidance system through the cloud server by utilizing the technologies of cloud computing, data analysis processing and the like. The application layer is a comprehensive information platform integrating a data platform, a support platform and an operation platform, and is mainly applied to charging decisions after charging demands are sent by the EV.
Compared with the traditional navigation technology, the charging guidance system can instantly perform data interaction with the charging station while collecting and processing massive and real-time information of the EV which is going out in large scale between cities, effectively solves the problem of information interaction delay, improves the navigation precision, and efficiently makes charging decisions for EV users.
The simulation analysis is as follows:
the inter-city expressway network shown in fig. 3 is adopted in the calculation example, the network has 5 cities, 10 expressway sections and 30 optical storage charging stations, the distribution of parameters of each road section and the distribution of the optical storage charging stations in the inter-city expressway network are shown in table 1, A, B, C, D, E in the figure are 5 cities of network nodes, the network nodes are all provided with the optical storage charging stations, 1-30 are optical storage charging station serial numbers, and the typical sunshine intensity in the region range is collected and researched.
TABLE 1 intercity highway network information
The triangular area of the intercity highway network formed by adjacent city nodes is automatically divided by utilizing the Thiessen polygon, and the region distribution corresponding to each light storage charging station is shown in figure 4. The sunshine intensity of charging stations in the same region is the same, and the photovoltaic output in the station is distributed normally.
Typical daily traffic flow characteristics of highway ODs given in document [19] and a traffic flow meter are used as traffic flow data. Based on practical considerations, an EV always selects the shortest path between a starting point and a destination when traveling on an intercity highway, and typical daily traffic flow characteristics and traffic matrices are shown in fig. 4 and table 2, respectively.
TABLE 2 expressway typical day traffic OD matrix
Setting the travel permeability of the EV in the intercity expressway network to be 20%, wherein the travel parameter range of the EV on each road section is shown in table 3, and the first-time travel time of the EV follows normal distribution[1]Travel parameters of each EV are generated by monte carlo simulation.
TABLE 3 expressway network EV travel parameters
Parameters of photovoltaic and energy storage components in the document [20] are adopted, and the EV is charged in a constant-power charging mode in a photovoltaic and energy storage charging station, wherein the charging power is 120 kW. As shown in table 4 below, taking the time-of-use electricity price of beijing as an example, the optical storage charging station purchases electricity from the power grid by using the time-of-use electricity price.
TABLE 4 Peak-valley time-of-use electricity price in Beijing City
Setting alpha to be 0.3, setting M to be 1440, setting the upper limit and the lower limit of the number of chargers to be 35 and 5 respectively, setting the maximum queuing waiting time of the EV to be 45 minutes, and setting the maximum charging and discharging power of the energy storage equipment to be 200 kW. In order to ensure that nearly half of the chargers in each day are used and avoid that the queuing waiting time of users in the optical storage charging station is far beyond the upper limit value when vehicles travel in holidays due to the fact that charging facilities in the optical storage charging station are insufficient, the utilization rate of equipment in the optical storage charging station is set to be 40% -80%.
Example results and analysis
Number configuration of chargers
In this section, by taking the above example scenario as an example, the inter-city light storage charging station volume planning method based on charging guidance provided herein is simulated, and the number of optimally configured chargers in each light storage charging station and the number of vehicles in typical daily accumulated service are obtained as shown in table 5.
TABLE 5 simulation results
According to the constant volume optimization result of the charging motors in each optical storage charging station shown in table 5, 450 charging machines are configured in the road network, and the number of the charging machines configured in the optical storage charging stations on different high-speed road sections is uniformly distributed according to the number of vehicles entering the station on a typical day; compared with the optical storage charging stations located between the traffic nodes, the optical storage charging stations located near the traffic nodes on the same highway section need to supplement electric quantity when a part of EVs initially travel after a large-scale EV travels between cities, so that a larger number of chargers need to be configured.
As can be seen from fig. 6 and 7, in the optical storage charging station before the volume fixing is optimized, a phenomenon that the utilization rate of the charging equipment and the queuing waiting time of the user develop in an uncoordinated manner generally occurs: when the utilization rate of equipment in the optical storage charging station is low, the phenomenon of resource idling can be caused, and the investment cost of a corresponding station is increased; on the contrary, when the utilization rate of the equipment is high, the queuing waiting time in the user station is too long, and the user traveling experience is seriously influenced. After the number of the charging motors in the optical storage charging stations is optimized, as shown in stations 4, 6, 7, 15 and the like, when the maximum queuing waiting time of EV users does not exceed a preset value, the number of the chargers is reduced so as to improve the equipment utilization rate of the corresponding optical storage charging stations; as shown in the stations 5, 10, 18, 26 and the like, when the utilization rate of the charging equipment meets the constraint condition, the number of the chargers is increased, so that the queuing waiting time of EV users can be effectively reduced, and the congestion phenomenon in the optical storage charging station is avoided.
Under different EV travel permeabilities, the optimal configuration results of the number of the charging motors in the optical storage charging station are shown in table 6, and the calculation results of the device usage rate and the maximum queuing waiting time are respectively shown in fig. 8 and fig. 9.
Table 6 simulation results for different trip permeabilities
From the above results, when the EV travel permeability is 35% and 50%, after the EV travels based on the charging guidance system, the number of the charging motors in each optical storage charging station is optimally configured, the ranges of the device utilization rates in the optical storage charging stations are 0.45-0.76 and 0.41-0.67, the maximum queuing waiting time of the EV user is 44min and 43min, and both the device utilization rate in each optical storage charging station and the maximum queuing waiting time of the user meet constraint values.
Light storage capacity configuration in light storage charging station
And obtaining the number of chargers arranged in the optical storage charging station and the number of the EV vehicles in accumulated service according to the analysis, thereby obtaining the load condition in the optical storage charging station. Under the condition that the EV travel permeability is 20%, taking an example of an optimized configuration of the light storage capacity in the light storage charging station 9, the station 9 is located in the region 4, 6 charging machines are configured in the station, a typical daily cumulative service is 204 EVs, and the intra-station EV power demand is as shown in fig. 10 below.
According to typical sunshine intensity of the region where the station 9 is located, the photovoltaic laying area, the photovoltaic output condition and the EV power demand in the light storage charging station, by combining a target function and constraint conditions, the photovoltaic capacity in the light storage charging station after optimal configuration is 1000kW, the capacity of energy storage equipment is 1200kW, the daily average life comprehensive cost is 4729 yuan, and the power balance relationship in the station is shown in figure 11.
As can be seen from fig. 11, the optical storage charging station with optimized photovoltaic and energy storage capacity operates according to the above-mentioned strategy: 24: 00-day 07: 00, the power grid executes off-peak electricity price and has no photovoltaic output, and because the electricity purchasing cost in the time period is lowest, the light storage charging station supplements energy storage equipment to the power grid electricity purchasing and meets the EV power requirement in the station, and the energy storage equipment is arranged in a range of 07: when 00, the state of full electric quantity is reached; 07: 00-18: when the photovoltaic output is larger than the load power demand in the station, the surplus power is on line, and when the photovoltaic output is smaller than the load power demand at 16: 00-18: 00, the electricity purchasing cost is relatively low due to the fact that the current power grid executes the usual electricity price, and the corresponding power shortage is supplemented by the power grid; 18: 00-21: when the power grid is at 00 hours, the peak-time electricity price is executed, and when the photovoltaic output cannot meet the load power demand, the corresponding power shortage is preferably supplemented by the energy storage equipment so as to reduce the peak-time electricity purchasing cost; 21: 00-24: when the electricity is 00 hours, the power grid executes the electricity price at ordinary times and in valley time, the light storage charging station sequentially provides electric energy for the EV at the ordinary times, and the electricity price supplements the electric quantity of the energy storage equipment at the valley time. According to the formula (12), when no light storage device exists in the light storage charging station 9, the light storage charging station purchases electricity to the power grid at the time-of-use electricity price in combination with the time-of-use electricity price, and the daily average life comprehensive cost is 7343 yuan; after the optical storage equipment is arranged, the peak-valley electricity price combined with a power grid is not considered, the output time of the energy storage equipment in the optical storage charging station is regulated and controlled, and the daily average life comprehensive cost is calculated to be 6734 yuan when the capacity of the optical storage equipment is optimally configured; the system regulates and controls the output time of the energy storage equipment and optimizes the capacity of the light storage equipment by combining the photovoltaic output and the load level in the station, thereby reducing the acquisition cost of the light storage equipment and the electricity acquisition cost of a power grid, and compared with the former, the daily saving cost is 2614 yuan and 2005 yuan respectively.
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Claims (1)
1. A constant volume planning method for an inter-city highway network optical storage charging station based on charging guidance is characterized by comprising the following steps:
model A description and assumptions
Because the charging infrastructure in the planning and configuration optical storage charging station is influenced by a lot of factors, in order to describe the corresponding constraint factors more comprehensively, on the premise of not influencing the calculation of the objective function, the following assumptions are made:
a1, service areas are arranged on an intercity highway network at intervals of 50-60 km, catering and parking rest services are provided for users, and in order to reduce the occupied land construction cost of the optical storage charging stations, the optical storage charging stations are assumed to be constructed in the service areas;
a2 is based on the characteristic that vehicles run on the highway in one direction, and the vehicles in 2 directions do not influence each other, so the research objects are only one-side vehicles running on the highway and one-side light storage charging stations;
a3 EV users accept the charging guidance decision they make due to the versatility and convenience of the charging guidance system;
a4, taking the average speed in the last 2 time intervals as the speed of the corresponding EV to travel to the optical storage charging station according to the data information uploaded by the sensor without considering the electric quantity loss when the EV starts and stops;
after the A5 EV reserved charging, a corresponding optical storage charging station reserves a charging station for the EV, and the EV is charged at constant power;
waiting time for electric automobile queuing in B light storage charging station
Based on an intercity highway network charging guide system, uploading charge state, accumulated travel and destination trip information in real time by an EV (electric vehicle), and when the charge state of the EV reaches a threshold value at the time T, making a charging decision for avoiding deep discharge of the EV battery by the guide system and prolonging the service life of the EV, selecting an optical storage charging station with less queuing waiting time to reserve a charging station according to the number of chargers and a charging plan of the optical storage charging station in the travel and waiting for the corresponding optical storage charging station to enter the station for charging;
the ith vehicle EV at an initial time Ti 0When the vehicle travels, the maximum travel mileage is represented by the optimal travel mileage and the endurance mileage, and the calculation formulas are respectively shown as a formula (1), a formula (2) and a formula (3);
Si＝BE_Si+MA_Si (1)
wherein: siThe maximum driving mileage is the accumulated travel when the ith vehicle initially travels; BE _ Si、
Respectively the optimal driving mileage and the charge state when the ith vehicle initially travels; alpha is the threshold value of the state of charge of the ith vehicle, 0<α<1；PiThe power consumption of the ith vehicle is hundred kilometers; b isiBattery capacity of the ith vehicle; MA _ SiThe driving mileage of the ith vehicle during initial trip is taken as the driving mileage;
the cumulative residence time of the ith vehicle in the optical storage charging station j' at the moment T
Cumulative stroke SiRespectively shown in formula (4) and formula (5):
wherein: j is the serial number of the optical storage charging station in the intercity highway network; k is the charging frequency of the guide system accumulated service vehicle i; x is the number ofij'For charging flag variable, when the ith vehicle is charging to the jth light storage charging station in front of the vehicle, xij'Is 1, otherwise is 0;
the departure time of the vehicle i after the k-th completion of the charging action,
the arrival time of the corresponding EV;Vithe traveling speed of the ith vehicle;
the charging guide system obtains the light storage charging station j and the running time delta t which can be selected by the ith vehicle under the cruising mileage through the layer cloud computing of the analysis processing platformi,jRespectively shown as a formula (6) and a formula (7);
Si≤Lj≤Si+MA_Si (6)
wherein: l isjThe distance between the light storage charging station and the starting point of the vehicle i trip is shown;
Δti,jduring the time, the charging plan in the optical storage charging station j consists of two parts: an EV which issues a charging demand before time T and reaches the light storage charging station j and has not finished the charging action, and T + Deltati,jThe charging demand is sent before the moment, the EV which reaches the optical storage charging station j can obtain the waiting time WA _ T of the ith vehicle based on the queuing theoryi,jAs shown in formula (8);
wherein: mjThe number of vehicles reserved to be charged in the optical storage charging station j; m isjThe number of the electric motors is charged in the light storage charging station j; t is ti'j,FirLeaThe departure time of the vehicle i' leaving first in the station when the ith vehicle arrives at the optical storage charging station j; t is ti'j,CalLeaWhen the ith vehicle arrives at the light storage charging station j, the M in the stationj-mj+1 departure times of the departure vehicles i';
power balance relation in C optical storage charging station
The electric energy of the light storage charging station is derived from photovoltaic power generation and power grid power supply, and the electric energy generated by photovoltaic is used for auxiliary power generation; due to the regularity and uncertainty of photovoltaic output, the power balance relationship in the optical storage charging station is as follows:
when photovoltaic output in the light storage charging station can not meet the EV power demand in the station, the electric energy is supplemented by the power grid and the energy storage equipment, and at the moment:
wherein: m is the number of sections of the charging station in the whole day, delta tmIs the duration of the m-th period, PPV,mIs the average power of the photovoltaic output in the m-th period, PBI,mAverage power discharged for energy storage device in m-th period, PG,mAverage power, P, for supplying the grid during the mth periodEV,mThe average power of the EV load in the station in the mth period;
when photovoltaic output is greater than power demand in the station in the light stores up the charging station, surplus electricity is gone on the net, this moment:
wherein: p'BI,mAverage power, P 'for charging energy storage devices during the m-th period'G,mThe average power of the photovoltaic grid-connected in the mth time period;
d light storage charging station equipment utilization rate
By utilizing the charging guide system, the ith vehicle selects the optical storage charging station j to charge, and the charging state of the ith vehicle when the ith vehicle arrives at the station j
As shown in formula (11);
wherein: t + Δ Ti,jThe time when the vehicle i arrives at the optical storage charging station j,
the state of charge of vehicle i at time T;
when the optical storage charging stations of the EV on the intercity highway network are charged, the charge electric quantity can finish the residual travel or go to next charging stations in the travel by the vehicle owner in consideration of saving travel time, so that the charge electric quantity delta B of the vehicle ii,jAs shown in formula (12);
wherein: beta is the state of charge after EV charging,
in order to uniformly compare the use conditions of the charging facilities in the optical storage charging stations on the inter-city expressway network, measure the service intensity of the charging facilities, and define the equipment utilization rate of the charging facilities in the optical storage charging stations as the ratio of the service time of corresponding equipment to the time of the whole day, as shown in formula (13);
wherein: n is the number of EV's, P, served by charging station j all dayEVThe charging power of a charger;
e, modeling of optical storage charging station based on charging guide
E1 objective function for optical storage charging station volume planning
Satisfying user's trip convenience demand, for guaranteeing light storage charging station economic operation, originally the principle of preferred consumption photovoltaic, reduction peak period purchase electric charge to the electric wire netting, propose the following operation strategy:
during the off-hour electricity price period, no photovoltaic output exists, and the power grid provides electric energy for the EV and the energy storage equipment; in the usual electricity price period, when the photovoltaic output is greater than the EV load demand, the residual electric energy is used for storing energy, otherwise, the required electric energy is supplemented by the power grid; in the peak electricity price period, when the photovoltaic output is greater than the EV load demand, the residual electric energy is used for storing energy, otherwise, the required electric energy is supplemented by the energy storage equipment, and if the capacity of the energy storage equipment is insufficient, the electric energy is supplied by the distribution network;
based on the optimization objective and the operation strategy, an objective function of a constant volume scheme is established with the minimum daily average life cycle cost of the charging infrastructure in each optical storage charging station as follows:
wherein: cj、NjThe purchase cost and the service life of the charging motor in the optical storage charging station j are respectively set; cPV,j、CBI,j、CG,j、CRES,jRespectively the purchasing cost, the electricity purchasing cost and the daily average cost after the conversion of the equipment operation and maintenance cost of the photovoltaic equipment and the energy storage equipment in the photovoltaic and energy storage charging station j in the life cycle thereof, CPV’,jThe cost is compensated for the daily average of the photovoltaic grid-connected electricity quantity;
constraint for volume planning of E2 optical storage charging station
Quantity constraint of E2.1 charger
On one hand, because the light storage charging station has limited floor area and investment cost, the number of the charging motors in the station j has upper and lower limits:
meanwhile, in order to avoid the phenomenon that charging resources are idle or charging facilities are insufficient to cause congestion in the station due to excessive configuration of the charging motor in the optical storage charging station, the equipment utilization rate of the charging motor in the optical storage charging station has a lower limit:
on the other hand, as can be seen from equation (8), the number of the charging motors in the optical storage charging station affects the in-station queuing waiting time of the EV user, and in order to relieve the anxiety psychology of the user and facilitate the user to go out, there is an upper limit to the user queuing waiting time:
e2.2 optical storage device power constraints
Because photovoltaic output is influenced by typical daily illumination intensity and photovoltaic capacity factors in the optical storage charging station, the photovoltaic output has an upper limit:
in addition, there are upper and lower limits by the influence of energy storage equipment capacity in the light stores up charging station energy storage equipment charging and discharging power:
λBI,m·λ′BI,m＝0 (21)
λBI,m,λ′BI,m∈{0,1} (22)
wherein: lambda [ alpha ]BI,m、λ′BI,mIs a variable of 0, 1, lambdaBI,mThe charging state of the energy storage device in any optimization time period is unique; lambda'BI,mThe discharge state of the energy storage device in any optimization period is unique;
e3 constant volume planning strategy solution
Solving by adopting a self-adaptive variation particle swarm optimization algorithm; the algorithm determines the variation factor of the current best particle according to the overall evolution degree and the individual evolution degree in the particle evolution process, adjusts the inertia weight of each particle, thereby realizing the self-adaptive adjustment of the evolution speed and the position of each particle, and the particles continue to search after entering the adjacent region to determine a new individual extreme value and a new global extreme value;
when the optical storage charging station is configured based on the adaptive variation particle swarm optimization algorithm, the formula (14) is used as an objective function, and the quantity particles of the charger, the photovoltaic capacity particles and the energy storage device capacity particles which meet constraint conditions are solved, wherein the specific steps and the flow are as follows;
(1) inputting inter-city expressway network information, light storage charging station serial numbers, geographic position information and the number of EVs;
(2) initializing a particle swarm, simulating and generating travel parameters of the EV, adopting a guiding charging mode, and making a charging decision by an intercity highway network charging guiding system when the charge state of the EV reaches a threshold value to guide the corresponding EV to enter a station for charging;
(3) setting the maximum iteration number as S and the current iteration number as 0; calculating an individual optimal solution and a global optimal solution of the initialized particle swarm;
(4) enabling the maximum particle number to be Z, enabling the current particle number to be 0, and updating the speed and the position of the particles;
(5) checking whether the particles meet constraint conditions, and if so, calculating a fitness function value of the current particles; if not, making the fitness value of the particle infinite;
(6) whether the current fitness function value is superior to the current individual extreme value and the group extreme value is checked, and if the current fitness function value is superior to the current individual extreme value and the group extreme value, the individual optimal solution and the global optimal solution are updated; otherwise, turning to the step (7);
(7) let Z be Z +1, check whether the number of particles Z is equal to the maximum number of particles Z, if yes, continue to step (8); otherwise, returning to the step (5);
(8) if yes, the global optimal solution is the optimal constant volume scheme of the optical storage charging station, and the solution is finished; otherwise, turning to the step (9);
(9) and (4) calculating the group fitness variance of the current particle swarm and the variation probability of the global optimal solution, determining whether variation exists according to the random number, performing variation operation on the global optimal solution by adopting a method of increasing random disturbance, and then turning to the step (4).
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