Routing EV users towards an optimal charging plan

Routing EV users towards an optimal charging plan
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   EVS26  Los Angeles, California, May 6 - 9, 2012 Routing EV Users Towards an Optimal Charging Plan Sandford Bessler 1 , Jesper Grønbæk  1 1 FTW Telecommunication Research Center, Donau-City 1, 1220 Vienna, Austria,  Abstract In this work we address the efficient operation of public charging stations. Matching energy supply anddemand requires an interdisciplinary understanding of both the mobility of electric vehicle (EV) usersand the load balancing mechanisms.As a result of existing mobility studies, we propose in this work a routing service for searching andreserving public charging spots in the neighborhood of a given destination. When comparing the searchresults for direct drive with those for a multimodal route (using driving, walking and public transport)in an urban environment, we obtain for the latter significantly more charging options in particular at lowe-mobility penetration levels, at a cost of slightly longer trip duration.Further contributions address the schedule optimization, that, due to the proposed distributed architec-ture, can be performed independently at each public charging station. We formulate an integer programfor the controlled charging and compare results obtained both with the exact and with a greedy heuristicmethod. Keywords: e-mobility, EV routing service, multimodal route optimization, charging station, controlled charging, power flow calculation 1 Introduction A well studied scenario for charging electric ve-hicles (EV) is charging overnight at home. Thisscenario alone, however, fails to address certainsignificant user groups such as residents of ur-ban areas without own garage, vehicle fleets, ve-hicles with higher mileage, etc. These users areall dependent of the existence of public charg-ing stations (PCS). Using a PCS poses howevertwo problems for the user: a) to find a PCS thatmatches the mobility needs and b) the found PCSmust be available in terms of energy and parkingspace in the desired charging period. We addressboth interdependent problems in this work, thathas been conducted within the KOFLA project[1]As the vehicular mobility can shift energy charg-ing energy very quickly from place to place, themain idea we follow in this work is to plan re-sources in advance that contribute both to usersatisfaction and to the service performance of thegrid operator.Recent mobility studies [2],[3] reveal that EVcharging must be subordinated to the mobilitygoal or activity and not viceversa. As a conse-quence, users should plug-in their EVs in walk-ing distance of their destination. Partial chargingis acceptable, if the stay duration is limited.Based on these assumptions, we have defined aquery and reservation protocol between an EVand a routing service that enables the user to:a) query availability of charging stations anytimein advance, b) reserve a time slot for chargingat an available charging station, and c) be noti-fied, when a charging point becomes available.The protocol runs over a wireless channel avail-able in a cellular network GSM/UMTS/LTE, butin the future it could be integrated in the ITS ser-vice ecosystem, as proposed currently in ETSIEV notification draft [18].The routing service is designed to serve a geo-graphical region and represents a broker betweenEV users and energy providers. The broker en-tails the best PCS match in terms of availabil-ity of resources required for charging. It fur-ther considers user preferences such as locationconvenience, price importance, preferred energyprovider, etc.There are two parameters in the user provided in-formation (see Table 1) that help to route the userand reserve resources at a selected PCS: the time EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, 2012  1  window, i.e. estimated arrival and leave time,and demand, i.e. the maximum amount of en-ergy needed. Whereas the arrival time and en-ergy can be estimated by the vehicle navigationand the battery management system, the stay du-ration has to be provided by the user (but it couldalso be infered from the activity type). Table 1: Parameters of the query messageParameter valuetimestampdestination (next stop) coordinatesexpected arrival timeexpected departure timeexpected State of charge (SoC)may use public transport yes/nocharging rates supported slow/normal/quick price importance high, lowwaiting importance high, lowwalking distance importance high, lowrenewable importance high, lowpayment means card name, null For this scenario, we introduce a decentralizedsystem architecture, where each PCS is able toschedule and control the charging of individualEVs allocatd to it. In this way the architecturesupports a new stakeholder: the charging stationowner. This provider has the freedom and incen-tive to setup charging stations and sell the park &charge service in conjunction with added valueservices like loyalty programs, public transporta-tion (see discussion in [4]).Figure1showsschematicallytheproposedarchi-tecture: the routing server dispatches the reser-vations on the basis of the rough availability of resources at certain selected public charging sta-tions. EachPCSsendsloadstatusupdatesbothtothe routing service and to a LV-grid agent, whichhas the task to calculate periodically the feasiblechargingload(availablepower)foreachPCS,us-ing a power flow model. In the example in Figure1, PCS A and B are in the same LV-grid, whereasPCS C is in a different LV-grid. PCS ARoutingServerPCS CPCS BLowvoltagegridLowvoltageegridReservationProtocolLoadUpdateReserveAvailablePowerLoadUpdate Figure 1: System architecture: charging load balanc-ing is performed through routing the requests to dif-ferent PCSs and through optimized activity schedul-ing in the PCS. In the rest of the paper, we focus on two mech-anisms that would drastically improve the avail-ability of charging resources at a PCS. In section2 we evaluate the park, charge & ride option andin Section 3 we study the optimal allocation of resources at a specific PCS. 2 Multimodal Routing Multimodal routing covers the aspect of includ-ing different means of transportation in a route.Multimodality is relevant for park-and-ride sce-narios where drivers park in the perimeter of thecity and continue to their destination using pub-lic transport. Multimodality with public trans-port can also be combined with e-mobility charg-ing to increase the access to more charging sta-tions while still ensuring end-to-end mobility. In-creased charging station availability is importantin early low penetration scenarios. Further, iden-tifying several charging station options can bebeneficial to increase the possibility of identify-ing a charging station with free resources.In this section, we will shortly revisit our previ-ous work from [19], which introduces a multi-modal routing scheme and a preliminary analysison gains in charging station availability. In thiswork, the preliminary analysis is extended to as-sess the travel time impact of multimodal routesunder different road traffic conditions. 2.1 Multimodal Routing Heuristic The multimodal routing heuristic proposed in ourprevious work [19] involves the modalities of EV-driving, public transport and walking. Theobjective of the heuristic is to identify a set of charging stations which are adjacent to publictransport stops that can provide user-mobility tothe final destination. The heuristic is based on aset of presumptions: 1) An EV-user can acceptto walk maximally  T  walk  seconds between twopoints on a route i.e. from charging station topublic transport stop and from a public transportstop to the destination, 2) The user can accept tospend maximally  T   pt  seconds in public transporttransit, 3)ThedesiredEVdrivingrangeislimitedto  EV   range  which enables for instance to satisfylow battery EV range constraints. The heuristicis realized as follows: Off-line initialization:  All charging stationswithin  T  walk  seconds of a public transport stopare identified. Online processing (at route request): A. Identify the public transport stop closest tothe destination P  ds  (destination stop), whichis within  T  walk  walking distance. If noneare found, return an empty set.B. Identify public transport stops  P  icss ,  i  =0 ...N   (charging station stop).  N   corre-sponds to the amount of stops that canbe reached within  T   pt  seconds from  P  ds (assuming symmetric of public transportroutes). The travel time to each station canbe calculated from a shortest-path-tree [14]. EVS26 Electric Vehicle Symposium, 2012  2  Based on P  icss , look-up all charging stations CS   jall ,  j  = 0 ...M   where  M   are the amountof charging stations within walking distanceof the stops in  P  icss .C. Of the charging stations  CS   jall  filterout irrelevant charging stations based on EV   range . In the following evaluations,charging stations are considered in a radiusdefined by the distance from the EV to thedestination stop + 2000 meter to avoid ex-cessively long routes.D. For each remaining charging station, calcu-late two routes: 1) driving from the currentlocation of the EV to the charging stationand 2) public transport from the chargingstation to the destination. These routes aremerged to a joint multimodal route.E. The routes calculated for each chargingstation in D. are returned in a final set CS  koptions , k  = 0 ...P   to the EV-user for a fi-nalselection.  P   isthetotalamountofreach-able charging stations returned.To evaluate the proposed mechanism, it hasbeen realized using open source route calcula-tion tools. Graphserver [20] is applied to derivethe public transport and walking routes whereasGosmore [21] provides vehicle routes. An evalu-ation scenario has been constructed from the Vi-enna region which represents a large city with astrong public transport network. For the evalu-ation, only the subway system is considered. Itcovers the major parts of the city region and alsoa majority of all public transport trips conductedin the region ( 64%  [24]). Geographical data areobtained from [12] and pub-lic transport data from “Wiener Linien”.Results on charging station reachability and im-pact on total travelling time have been obtainedby generating trips in a 16 km by 16 km area de-picted in Figure 2. Note, the term  reachability is used instead of availability as the scope of thiswork only is to consider if a charging station canbe reached and not if it is free or occupied. Atrip consists of a starting point and a destination.For each trip three routing schemes are calcu-lated: 1) a  direct   route which defines the drivingtime directly to the destination (without charg-ing), 2) a  charging in destination vicinity  route,where a charging station is within  T  walk  secondsfrom the destination, and 3) a  multimodal  routevia a charging station and public transport. Amultimodal and a direct route for an example tripis provided in Figure 2. To generate and com-pare different trips, independent start and des-tination locations are generated. Start locationsare randomly generated (uniform) all over the re-gion to simulate trips starting in the city as wellas the surroundings. The destinations are ran-domly chosen from a database with points-of-interest(,restaurants, hotels, sport centers, etc.) to ensurecommonly relevant destinations. 16.2816.316.3216.3416.3616.3816.416.4216.4416.4648.1448.1648.1848.248.2248.2448.26   Parking with or without chargingDriving directlyDriving partiallyWalkingPublic transporttart!harging tationDestination Figure 2: Example trip from start to destination by direct   driving without charging, driving to a chargingstation in  vicinity  of the destination and travelling via  public transport  . To assess the impact of multimodality in differ-ent degrees of electrified public parking places,three different EV penetration levels have beencompared:  10% ,  50%  and  100% . The pen-etration levels are controlled by the parameter  p CS  , whichdescribestheprobabilitythatapublicparking place has been electrified. For the eval-uation scenario, public parking places have beenobtained from the OpenStreetMaps data leadingto a total of 568 parking places. These are alsodepicted in 2. Table 2: Main parameters used in the simulation of routing schemes. Parameter Value Trips evaluated 484Walking speed 5 km/hTrip start time 09:00:00 (weekday) T   pt  unlimited  T  walk  300 s From the analysis presented in [19], significantgains have been shown in terms of charging sta-tion reachability. The previously obtained resultsare summarized in Table 3 using the values of Ta-ble 2 comparing the  destination vicinity charging scheme to the  multimodel  routing scheme. Theresults show the fraction of trips where at leastone ( P >  0 ) or at least 3 ( P   ≥  3 ) charging sta-tions are reachable. Identifying several reachablecharging stations is clearly necessary when con-sidering that for a given charging station no freecapacity may be available.For both results sets, significant benefits of themultimodal scheme can be demonstrated; espe-cially in low penetration scenarios. For moredetails on this analysis interested readers are re-ferred to [19]. EVS26 Electric Vehicle Symposium, 2012  3  Table 3: Results of reachability of one or more charg-ing stations on a trip. CS set   p CS   = 0 . 1  p CS   = 0 . 5  p CS   = 1 P >  0  8  . 7  %  39  . 3  %  62  . 6  % 40 . 5 %  61 . 0 %  74 . 2 % P >  3  0  . 0  %  1 . 9  %  11 . 0  % 32 . 9 %  35 . 7 %  41 . 11 %  Destination vicinity charging ,  Multimodal 2.2 Impact of Traffic Conditions In the following, a quantification of the impactof the  multimodal  routes is made compared to destination vicinity charging  routes in the Vi-enna city scenario. The quantification focuseson the change in average trip travel time betweenthetwoschemesconsideringdifferentroadtrafficconditions. This enables to clarify the potentialadvantages of the subway based transport, whichis clearly not affected by varying road traffic con-ditions. For the study, two extremes are consid-ered:  perfect   low traffic conditions and during rush hour  .To model the road traffic conditions a simplisticmodel is applied. The model separates the roadsegments of the studied region into the classesof   city roads  and  motorways . Each class isparametrized by an average speed. This sim-plistic model is in correspondence with existingtraffic information systems for the Vienna regionallowing for a direct mapping of existing statis-tics [23]. The traffic conditions are modelled asaverage speed conditions for all vehicles in theroad network. As such the analysis does not takeinto consideration potential congestion scenariosin individual trips. Average speed parameters forcity roads have been obtained from the work in[22]. The authors describe an ITS system for on-line collection of traffic data based on taxis withGPS. The system has been deployed in Vienna toenablerealisticaveragespeedestimatesunderthedifferent conditions. Also based on GPS data, theworkof[23]similarlypresentsaveragevaluesformotorways in the Vienna region. A summary of the utilized parameters is provided in Table 4. Table 4: Average speed according to traffic conditionsand road type. TrafficconditionsCity roads Motorway Ideal 30 km/h 85 km/hRush hour 20 km/h 58 km/h The classification of road segments into respec-tively  city roads  and  motorways  in the regionof Figure 4 has been made from maxspeed val-ues available in the OpenStreetMap data. Roadsegments with a maximum speed of 80 km/h orabove are classified as motorway.Utilizing the evaluation approach introduced inSection 2.1 and the values from Table 4 com-plete route traveling times have been calculated. 051015    M  e  a  n   I  n  c  r  e  a  s  e   [  m   i  n   ] City Roads=30 km/h, Motorway=85 km/h p =0.1 (53)p =0.5 (283)p =1 (568)051015 Charging Station Penetration (Number of charging stations)    M  e  a  n   I  n  c  r  e  a  s  e   [  m   i  n   ] City Roads=20 km/h, Motorway=58 km/h  Destination vicinity chargingMulti−modal only (>0)Multi−modal only ( ≥ 3)CSCSCS Figure 3: Comparison of the overall trip time increasecompared to driving directly to the destination. Note, that for this evaluation  T   pt  is set to  unlim-ited   to not sort out any routes where driving pri-marily with the subway could be the fastest ap-proach. Figure 3 presents the mean increase intravel time for the two routing approaches un-der different charging station penetration levels.The increase is calculated in relation to the directroute to the destination (without charging). Theresults suggest under ideal road conditions thatthe difference in increase between the  destina-tion vicinity charging  and the fastest  multimodal route (grey bar) are between 4 and 7 minutes de-pending on the penetration level. Also, it can beobserved that the mean travel time does not in-crease significantly when considering using oneof the three fastest multimodal routes (the whitebar). Under rush hour traffic conditions, it can beobserved that this difference is decreased to be-tween 1 minute (for  P  CS   = 1 ) and 5 minutes forlow penetration scenarios.Which increase in travel time EV owners subjec-tively are willing to accept is up to future usabil-ity studies to clarify. It is, however, clear that themultimodal scheme offers a significant improve-mentinthereachabilityofchargingstations. Fur-ther, our results suggest that in medium to highpenetration scenarios and rush hour conditionsthe mean travel time increase is in the order of afew minutes. Overall, the results are encouragingand future work must establish how the increasedreachabilityofchargingstationscanbeappliedtoimprove overall charging availability and poten-tially geographical balancing of grid load. 3 Controlled Charging at theCharging Station With the increase of EV penetration, controlledcharging, i.e. the coordination of charging timeslots will become indispensable both in residen- EVS26 Electric Vehicle Symposium, 2012  4  tial areas such as apartment blocks with elec-trified parking lots, and public charging facili-ties. A controlled charging strategy schedulesthe charging jobs in such a way that it reducesload peaks caused by users that plug-in their carsapproximately in the same time. In contrast toother works on recharge scheduling [8],[9],[10],our model uses as key parameter the  time window corresponding to user’s estimated parking startand end time. Further required are: the amountof energy needed to fill up the battery and thecharging rates (in kW) supported by both the ve-hicle and the charging station. 3.1 Problem Formulation For the problem we want to solve there is givena time horizon of T discrete periods, a numberof M charging points and a number of A ac-tivities, so that each needs one of the M park-ing places for a duration time window (earli-est,latest), and 2) a power resource for a certainduration which depends on the selected charg-ing speed  sp k  (from a discrete set). The solutionhas to determine the start charging time for eachactivity, the allocation to a charging point, suchthat the total power consumed in each time pe-riod does not exceed the planned value, and suchthat a certain profit objective function to be ex-plained below is maximized.Note that we have to deal with two types of re-sources: parking place during the whole timewindow, and power limitation during the charg-ing period. If we make the assumption that thecharging points are identical (machines), we cansplit the problem into two sub-problems:1. since the machines are identical, we allo-cate first the activity time windows to thecharging points. This problem is similar toscheduling classes to classrooms, and canbe solved optimally by a greedy heuristic(increasing starting time rule). The intervalsare the activity time windows and cannot beshifted. Theresultedallocationofactivitytocharging point allows us to address the sec-ond subproblem as a one machine problem.2. this is a bandwidth or resource allocationproblem over time intervals, or RAP) whichis NP-hard, since it can be reduced to aknapsack problem if the time windows areset to the intervaal [0,1]. The specificproblem which we denote EVRSTW (elec-tric vehicle recharge scheduling with timewindows) requires that the charging takesplace within the time window of the activ-ity, has different speeds (supported by bothcar and charging point), and is limited by to-tal power available in each of the T periods.In case the machines are not identical, i.e. slowand fast charging, the machine allocation can bestill made under the following assumption: fastcharging spot is an expensive resource, thereforeis will not be used for slow charging, which leadsto two disjoint activity groups. In the most gen-eral case, instances have to be created for eachDescription T   set of time periods  t  ∈  T A  Set of charging activities M   set of charging points I   instances generated from activities A e  j ,  l  j  time window (earliest, latest)  j  ∈  As i ,f  i  start and finish time of charging ,  i  ∈  I d  j  energy demand of activity j P  t total available power during  t  ∈  T w i  charging speed  i  ∈  I v ti  parking period of instance  i  ∈  I,t  ∈  T c i  completion degree of instance, one of C  p i  profit generated by instance i x i  variable:  x i  ∈ { 0 , 1 } ,i  ∈  I  . k [ sp i ]  cost for charging at speed  sp i Table 5: Summary of notation. individual machine, making the problem verylarge.Intherestofthissectionweassumethemachinesare identical, so that the allocation activity-charging spot has been done and focus only onthe second subproblem, the RAP. The RAP prob-lem is highly combinatorial, since the charginginterval can be selected anywhere within the timewindow, can have different durations determinedby the charging speed and the completeness cri-teria, leading to different profits for the same ac-tivity. In order to simplify the integer linear pro-gram (ILP) formulation, we generate from eachparameter combination an instance  i  and use onebinary variable  x i  to denote that that instance isselected in the solution, see Bar-Noy et al [15] :for  j  ∈  A for  m  ∈  M   (not identical m)for  n  ∈  C  for  k  ∈  S  for  l  ∈  [ e  j ,l  j  − dur ] ,  dur  =  nd i /k generate instance  inst ( i,m,n,k,l ) endendendendendThe profit  p i  is defined as a weighted sum of two objectives: the completion degree  c i  and thecost factor  k [ sp i ]  of the charging speed (quick charging requires more expensive equipment andshortens the battery life). We set  α  = 0 . 5  p i  =  αc i  + (1  −  α ) /k [ sp i ]  (1)Problem EVRSTW: Z   = max  i ∈ I   p i x i  (2) EVS26 Electric Vehicle Symposium, 2012  5
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