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The default LDV and LDT trip data come from the +2017 National Household Travel Survey (NHTS), with an example of the data included in +:numref:`example_daily_trips`. + +.. _example_daily_trips: + +.. figure:: demand/transportation_electrification/img/methodology/daily_trips.png + :align: center + + Example daily trips of vehicle data from NHTS + +These are also divided into Census Divisions, which are then mapped to the +corresponding regions within the grid model. The MDV and HDV trip data are anonymized +from the Texas Commercial Vehicle Survey and calibrated to align with medium- and heavy- +duty vehicle trip statistics. More details about each data set are included in +:numref:`vehicle_travel_patterns` and can be updated to reflect different vehicle +scenarios or incorporate more recent data. In all cases, an average weekday and average +weekend daily pattern of vehicle trips is calculated from the vehicle trip data. Then, +these trips are filtered by vehicle type (e.g. LDV, LDT, MDV, and HDV) and by range for +LDVs and LDTs (e.g. less than 100 miles, less than 200 miles, less than 300 miles) to +capture what is capable for typical battery capacities. This creates a weekday and +weekend daily pattern of BEV-capable trips that will be used in the subsequent steps. +The LDV and LDT vehicle miles traveled (VMT) by hour is presented in +:numref:`vmt_by_hour`. + +.. _vmt_by_hour: + +.. figure:: demand/transportation_electrification/img/methodology/vmt_by_hour.png + :align: center + + Light-duty vehicle/truck miles traveled by hour of the day for weekends and weekdays  + +Similarly, Figure :numref:`dwelling_over_24h` illustrates the percentage of vehicles +parked and able to charge at each hour over a 24-hour period. + +.. _dwelling_over_24h: + +.. figure:: demand/transportation_electrification/img/methodology/dwelling_over_24h.png + :align: center + + Status of dwelling vehicles over 24-hour period + +The flowchart in :numref:`trip_patterns_flowchart` illustrates the entire procedure. + +.. _trip_patterns_flowchart: + +.. mermaid:: + :caption: Daily BEV-capable trip patterns + + flowchart TD + subgraph main[ ] + subgraph input[ ] + A(Input: NHTS trip data for LDV/LDT) + B(Input: Anonymized trip data for MDV/HDV) + end + A==>C(Divide into census region) + C==>D(Create weekday and weekend pattern of typical vehicle trips) + B==>D + D==>E("Filter trips by range [0,100, 0,200, 0,300] miles") + E==>F(Create weekday and weekend pattern of BEV-capable trips) + end + + class input white_no_border + class main,A,B,C,D,E,F white + classDef white fill:#ffffff,stroke:#333,stroke-width:3px + classDef white_no_border fill:#ffffff,stroke:#333,stroke-width:0px + +These daily BEV-capable trip patterns are then further distributed temporally and +spatially, as discussed in more detail in :numref:`annual_vehicle_miles_traveled`. +State-level and urban area (UA) VMT per capita were taken from the Department of +Transportation’s transportation health tool and create the distribution across rural +and urban areas, as defined by the U.S. Census Bureau +:cite:p:`DoT_transportation_health_tool`, :cite:p:`CB_urban_rural_classification`. +Weight factors from the U.S. EPA model, MOVES, create the time-of-year scaling of +weekday versus weekend and month of the year :cite:p:`EPA_moves`. +  +Projections from NREL’s Electrification Futures Study (EFS) provide the adoption rate +scaling of BEV demand to create the base-year and simulation-year profiles of BEV- +capable trip patterns :cite:p:`NREL_electric_technology_adoption`. Total charging +demand by area (urban and rural) is scaled based on state-level BEV VMT projections +from NREL’s EFS :cite:p:`NREL_efs`. As more granular BEV projections become available, +scaling projections could be targeted to specific urban and rural areas given the +model’s structure. The procedure is shown in :numref:`dynamics_flowchart`. + +.. _dynamics_flowchart: + +.. mermaid:: + :caption: Simulating base-year and simulation-year dynamics + + flowchart LR + subgraph main[ ] + direction TB + subgraph start[ ] + A(Weekday and weekend pattern
of BEV-capable trips) + end + subgraph input[ ] + direction LR + B(Input: census population data
for urban area and states) + C(Input: DoT Transportation and Health Tool's
VMT per capita for urban areas and states) + B------C + end + A====input + D(Convert state-level VMT per capita into one
year's total of urban and rural driving for each state) + input==>D + subgraph middle[ ] + direction LR + E(Annual amount of VMT is distributed
across weeks and months based on
driving data from EPA's MOVES) + F(Input: EPA's MOVES for weekday/weekend
and monthly VMT distributions) + F----E + end + D==>middle + subgraph last[ ] + direction LR + G(Create a base-year and simulation-year profile
of BEV-capable trips by scaling annual VMT
to match NREL's EFS projections) + H(Input: NREL's EFS projections of
future BEV adoption rates) + H----G + end + middle==>last + end + + class main,A,input,B,C,D,middle,E,F,last,G,H white + class start white_no_border + classDef white fill:#ffffff,stroke:#333,stroke-width:3px + classDef white_no_border fill:#ffffff,stroke:#333,stroke-width:0px + +Algorithmically, these projections are modeled by making multiple copies of individual +trips, as illustrated in :numref:`scaling_process_vehicle_trip`, which are used in the +smart charging algorithm.  + +.. _scaling_process_vehicle_trip: + +.. figure:: demand/transportation_electrification/img/methodology/scaling_process_vehicle_trip.png + :align: center + + Scaling process of vehicle trip + +With the projected BEV vehicle trips in place, NREL’s EFS is again used to set the fuel +efficiency for the simulated year to determine the amount of electricity needed to +charge after each BEV trip. Then, the charging model uses one of two charging +algorithm strategies: immediate (uncoordinated) charging and smart (optimal) charging, +with example illustrations shown in :numref:`immediate_charging_result` and +:numref:`smart_charging_result`. Both algorithms are deterministic and directly +utilize the input vehicle trip data to calculate the charging demand based on vehicle +travel distances, dwell locations, and user defined infrastructure parameters. The +Smart Charging algorithm currently uses an optimization function to minimize wholesale +prices via flattening the net load curve. Incorporating additional optimization goals +that will change the cost function, such as minimizing individual vehicle costs in +response to time-varying utility rate structures, will be explored in future work. For +the Smart Charging algorithm, each representative vehicle sequentially sets its +charging pattern in response to the optimization function as well as an aggregate load +profile. That vehicle’s additional charging load is then added to the aggregate load +profile, which is then sent to the next vehicle as an input to its smart charging +decision. The aggregate profile of electricity demand from all smart-charging BEVs is +then simply the sum across all vehicles (see :numref:`demand_calculation_flowchart`).  + +.. _demand_calculation_flowchart: + +.. mermaid:: + :caption: Calculating simulation-year electricity demand + + flowchart TB + subgraph main[ ] + direction TB + subgraph input[ ] + direction RL + A(Base-year and simulation-year
profile of BEV trips) + B(Input: NREL's EFS for fuel efficiency
projections by vehicle type) + B---->A + end + C(Fuel efficiency projections determine
charging needed after each vehicle trip) + input==>C + D(Charging algorithm: immediate and smart) + C==>D + E(Create a simulation-year profile
of electricity demand from
transportation electrification) + D==>E + end + + class main,input,A,B,C,D,E white + classDef white fill:#ffffff,stroke:#333,stroke-width:3px + classDef white_no_border fill:#ffffff,stroke:#333,stroke-width:0px + + +Immediate and Smart Charging Example Outputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Immediate Charging refers to full power charging at time of plug-in until full capacity +reached or car unplugged, whichever comes first. + +.. _immediate_charging_result: + +.. figure:: demand/transportation_electrification/img/methodology/immediate_charging_result.png + :align: center + + Notional results for immediate charging algorithm, with charging hours within the + bracket + +Smart charging refers to coordinated charging, where drivers provide information on their travel schedule and charging demand to the electric grid operator. + +.. _smart_charging_result: + +.. figure:: demand/transportation_electrification/img/methodology/smart_charging_result.png + :align: center + + Notional results for smart charging algorithm, with charging hours within the + bracket  + + +**Example Output -- Immediate Charging**. Immediate Charging refers to full power +charging at time of plug-in until the battery is full or until the vehicle departs on +the next driving trip. :numref:`ldv_immediate_charging_output` and +:numref:`ldt_immediate_charging_output` present normalized, unscaled LDV and LDT +charging demand, respectively. These normalized profiles are then scaled based on the +parameters for the desired simulation year, with an example output shown in +:numref:`example_ldv_immediate_load`. These include the projected VMT for the +simulated year, the fuel efficiency projection (e.g. number of kWh used per mile +traveled), and the efficiency of the charging process. + +.. _ldv_immediate_charging_output: + +.. figure:: demand/transportation_electrification/img/methodology/ldv_immediate_charging_output.png + :align: center + + Normalized LDV immediate charging output for 168 hours (1 week) + +.. _ldt_immediate_charging_output: + +.. figure:: demand/transportation_electrification/img/methodology/ldt_immediate_charging_output.png + :align: center + + Normalized LDT immediate charging output for 168 hours (1 week) + + +**Example Output -- Smart Charging**. Smart charging refers to coordinated charging, +where drivers provide information on their travel schedule and charging demand to the +electric grid operator. Vehicle charging is optimized based on cost (e.g., time-of-use +rates), grid support needs, travel considerations, and vehicle constraints. +:numref:`ldv_smart_charging_output` and :numref:`ldt_smart_charging_output` present +normalized, unscaled LDV and LDT smart charging demand, respectively. +:numref:`example_ldv_smart_load` shows an example of the results from LDV smart +charging at scale that was optimized for grid support needs by flattening net demand +(e.g. “filling in the valleys”).  + +.. _ldv_smart_charging_output: + +.. figure:: demand/transportation_electrification/img/methodology/ldv_smart_charging_output.png + :align: center + + Normalized LDV smart charging output for 168 hours (1 week) + +.. _ldt_smart_charging_output: + +.. figure:: demand/transportation_electrification/img/methodology/ldt_smart_charging_output.png + :align: center + + Normalized LDT smart charging output for 168 hours (1 week) + + +.. _vehicle_travel_patterns: + +Vehicle Travel Patterns +^^^^^^^^^^^^^^^^^^^^^^^ +Light-duty Travel patterns +~~~~~~~~~~~~~~~~~~~~~~~~~~ +The 2017 National Household Travel Survey (NHTS) documents the light-duty vehicle and +light-duty truck travel patterns (https://nhts.ornl.gov/). Data from the NHTS 2017 +``trippub.csv`` dataset were filtered to identify all vehicle trips. Relevant data were +then divided into nine datasets, one for each Census Division, as defined within the +dataset, see :numref:`census_divisions_table`. + +.. _census_divisions_table: + +.. table:: Census divisions + + +----------------+--------------------+--------------------------------------+ + | Division Number| Name | States Included | + +================+====================+======================================+ + | 01 | New England | CT, MA, ME, NH, RI, VT  | + +----------------+--------------------+--------------------------------------+ + | 02 | Middle Atlantic | PA, NJ, NY  | + +----------------+--------------------+--------------------------------------+ + | 03 | East North Central | IL, IN, MI, OH, WI  | + +----------------+--------------------+--------------------------------------+ + | 04 | West North Central | IA, KS, MN, MO, ND, NE, SD  | + +----------------+--------------------+--------------------------------------+ + | 05 | South Atlantic | DE, FL, GA, MD, NC, SC, VA, WV, (DC) | + +----------------+--------------------+--------------------------------------+ + | 06 | East South Central | AL, KY, MS, TN   | + +----------------+--------------------+--------------------------------------+ + | 07 | West South Central | AR, LA, OK, TX    | + +----------------+--------------------+--------------------------------------+ + | 08 | Mountain | AZ, CO, ID, MT, NM, NV, UT, WY   | + +----------------+--------------------+--------------------------------------+ + | 09 | Pacific | AK, CA, HI, OR, WA    | + +----------------+--------------------+--------------------------------------+ + +The definition for each column in the final datasets is in +:numref:`nhts_trip_dataset_table`. Columns 1-20 are taken directly from the NHTS +dataset, and columns 21-28 are calculated values based on the preceding columns.  + +.. _nhts_trip_dataset_table: + +.. table:: Columns in modified NHTS trip Dataset + + +--------+--------------------------------+ + | Column | Variable  | + +========+================================+ + | 1 | Household  | + +--------+--------------------------------+ + | 2 | Vehicle ID | + +--------+--------------------------------+ + | 3 | Person ID | + +--------+--------------------------------+ + | 4 | Scaling Factor Applied  | + +--------+--------------------------------+ + | 5 | Trip Number | + +--------+--------------------------------+ + | 6 | Date (YYYYMM) | + +--------+--------------------------------+ + | 7 | Day of Week (1 - 7) | + +--------+--------------------------------+ + | 8 | If Weekend | + +--------+--------------------------------+ + | 9 | Trip Start Time (HHMM) | + +--------+--------------------------------+ + | 10 | Trip End Time (HHMM) | + +--------+--------------------------------+ + | 11 | Travel Minutes  | + +--------+--------------------------------+ + | 12 | Dwell Time | + +--------+--------------------------------+ + | 13 | Miles Traveled | + +--------+--------------------------------+ + | 14 | Vehicle Miles Traveled  | + +--------+--------------------------------+ + | 15 | Why From | + +--------+--------------------------------+ + | 16 | Why To | + +--------+--------------------------------+ + | 17 | Vehicle Type (1-4 LDV, 5+ LDT) | + +--------+--------------------------------+ + | 18 | Household Vehicle Count | + +--------+--------------------------------+ + | 19 | Household Size | + +--------+--------------------------------+ + | 20 | Trip Type | + +--------+--------------------------------+ + | 21 | Start Time (hour decimal)  | + +--------+--------------------------------+ + | 22 | End Time (hour decimal) | + +--------+--------------------------------+ + | 23 | Dwell Time (hour decimal) | + +--------+--------------------------------+ + | 24 | Travel Time (hour decimal)  | + +--------+--------------------------------+ + | 25 | Vehicle Speed (mi/hour) | + +--------+--------------------------------+ + | 26 | Sample Vehicle Number | + +--------+--------------------------------+ + | 27 | Total Vehicle Trips | + +--------+--------------------------------+ + | 28 | Total Vehicle Miles Traveled | + +--------+--------------------------------+ + +Total vehicle trips variable refers to the total number of trips a single vehicle takes +in the sample window (24 hours). Trips are divided into weekday and weekend trips. The +resulting charging profile for each day type is replicated across the year, i.e., each +weekday and each weekend are the same set of trips across the year. +:numref:`trip_count_table` presents the total number of trips in the trip datasets for +each vehicle category. The weekday and weekend trips are weighted based on the MOVES +weight factors. The charging demand is scaled up and down further based on the MOVES +monthly weight factors depending on the month of the year. + +.. _trip_count_table: + +.. table:: Trip count by vehicle category, census division, and day of week + + +------------------+-----------------+-------------------+ + | Vehicle Category | Census Division | Trip Count | + + + +---------+---------+ + | | | Weekday | Weekend | + +==================+=================+=========+=========+ + | LDV | 01  | 3979 | 1235 | + + +-----------------+---------+---------+ + | | 02  | 32831 | 10664 | + + +-----------------+---------+---------+ + | | 03  | 30815 | 5116 | + + +-----------------+---------+---------+ + | | 04  | 8962 | 2885 | + + +-----------------+---------+---------+ + | | 05  | 58173 | 9620 | + + +-----------------+---------+---------+ + | | 06  | 2294 | 611 | + + +-----------------+---------+---------+ + | | 07  | 52982 | 7818 | + + +-----------------+---------+---------+ + | | 08  | 9127 | 2033 | + + +-----------------+---------+---------+ + | | 09  | 53554 | 16386 | + +------------------+-----------------+---------+---------+ + +------------------+-----------------+---------+---------+ + | LDT | 01  | 2881 | 935 | + + +-----------------+---------+---------+ + | | 02  | 29513 | 9347 | + + +-----------------+---------+---------+ + | | 03  | 31788 | 5478 | + + +-----------------+---------+---------+ + | | 04  | 10157 | 2960 | + + +-----------------+---------+---------+ + | | 05  | 58900 | 9465 | + + +-----------------+---------+---------+ + | | 06  | 2416 | 678 | + + +-----------------+---------+---------+ + | | 07  | 60929 | 8530 | + + +-----------------+---------+---------+ + | | 08  | 10006 | 2084 | + + +-----------------+---------+---------+ + | | 09  | 40161 | 12116 | + +------------------+-----------------+---------+---------+ + +------------------+-----------------+---------+---------+ + | MDV | All | 8302 | same | + +------------------+-----------------+---------+---------+ + +------------------+-----------------+---------+---------+ + | HDV | All | 8407 | same | + +------------------+-----------------+---------+---------+ + +Medium- and Heavy-duty Vehicles +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The construction of the original representative datasets is described in +:cite:p:`2020:forest`.   + +The following is a description of the data that are rendered from the initial, +representative HDV dataset.   + +1. Trip times, trip count, and miles traveled – data on trips was taken from the + original trip datasets and scaled to align with known statistics for the target + region. Only trip count, times, and miles traveled were included. Information on + locations, vehicle identity, vehicle class, and vocation are excluded.  +2. Dwell location – dwell locations are simplified to being either a “home base + location” or not. Home base locations are defined as depots that are owned, managed, + and/or under contract with the same entity as the fleet vehicle.   +3. Trip start and stop times – travel and dwell times by time of day.  + +The final data table structure is presented in :numref:`mdv_and_hdv_trip_dataset`. Each +row of the data table is a unique trip taken by the specified vehicle.  + +.. _mdv_and_hdv_trip_dataset: + +.. table:: Columns in modified MDV and HDV trip datasets  + + +--------+--------------------------------+---------------------------------------+ + | Column | Variable  | Description | + +========+================================+=======================================+ + | 1 | Vehicle Number | Unique vehicle number | + +--------+--------------------------------+---------------------------------------+ + | 2 | Trip Number | Current trip number of vehicle | + +--------+--------------------------------+---------------------------------------+ + | 3 | Destination (home base or not) | Where the trip ends, 1 = home base, | + | | | 2 = not home base | + +--------+--------------------------------+---------------------------------------+ + | 4 | Trip Distance | Miles traveled in the trip | + +--------+--------------------------------+---------------------------------------+ + | 5 | Trip Start | Time of trip start | + +--------+--------------------------------+---------------------------------------+ + | 6 | Trip End | Time of trip end | + +--------+--------------------------------+---------------------------------------+ + | 7 | Dwell Time | Length of time vehicle parked between | + | | | trips  | + +--------+--------------------------------+---------------------------------------+ + | 8 | Trip Time | Length of travel time | + +--------+--------------------------------+---------------------------------------+ + | 9 | Total Vehicle Trips | Total count of trips taken by the | + | | | identified vehicle in the time window | + +--------+--------------------------------+---------------------------------------+ + | 10 | Total Vehicle Miles | Total vehicle miles traveled by | + | | | vehicle in time window | + +--------+--------------------------------+---------------------------------------+ + + + +.. _annual_vehicle_miles_traveled: + +Annual Vehicle Miles Traveled +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The model is structured for a single base year and three future years: 2017, 2030, +2040, and 2050. Each year is unique based on vehicle miles traveled (VMT) and fuel +economy (miles per gallon of gasoline equivalent, mi/GGE), which together determine +annual vehicle electricity demand. The base year and future projections of battery +electric vehicle miles traveled are taken from NREL’s Electrification Futures Study +:cite:p:`NREL_electric_technology_adoption`. BEV VMT is divided into VMT occurring in +Urban Areas (UA) and Rural Areas (RA). UA is a term assigned by the U.S. Census Bureau +and is described as areas with a population of 50,000 people or more +:cite:p:`CB_urban_rural_classification`. Other years are available from NREL, if +desired. + + +Electric Vehicle Miles Traveled Projections by Urban and Rural Area  +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +VMT per capita for each state and Urban Area (UA) was taken from the Department of +Transportation’s transportation health tool :cite:p:`DoT_transportation_health_tool`. +To determine total state VMT, state population was taken from Census data and was +multiplied by the above state VMT per capita data +:cite:p:`DoT_transportation_health_tool` :cite:p:`CB_urban_rural_classification`. Then, +to calculate the fraction of total state VMT that is allocated to each urban area (UA) +and rural area (RA), the UA population was also pulled from Census data +:cite:p:`CB_urban_rural_classification`. From there, the UA population was multiplied +by UA VMT per capita to get total UA VMT. Lastly, UA VMT is subtracted from state VMT +to determine the RA VMT for the state. These calculations are summarized below:  + +.. math:: + + V_{\rm state} = A_{\rm state} \times P_{\rm state} + +| where: +| :math:`V_{\rm state}` is the state VMT, +| :math:`A_{\rm state}` is the VMT per capita, +| :math:`P_{\rm state}` is the state population. + +.. math:: + + V_{\rm UA} = A_{\rm state} \times P_{\rm UA} + +| where: +| :math:`V_{\rm UA}` is the urban area VMT, +| :math:`P_{\rm UA}` is the urban area population. + +.. math:: + + V_{\rm RA} = A_{\rm state} - \sum V_{\rm UA} + +| where: +| :math:`V_{\rm RA}` is the rural area VMT. + + +Monthly and Daily Weight Factors  +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Along with the rural/urban distribution, the default scenarios also use weekday/weekend +and monthly weight factors to distribute annual VMT. These weight factors come directly +from the U.S. Environmental Protection Agency’s MOtor Vehicle Emission Simulator +(MOVES) model :cite:p:`EPA_moves`. The weight factor values are listed in +:numref:`weekday_vs_weekend_weight_table` and :numref:`month_weight_table`.  + +.. _weekday_vs_weekend_weight_table: + +.. table:: Weekday versus weekend weight factors + + +-------------------------------+---------+----------+ + | Day Type | Rural | Urban | + +===============================+=========+==========+ + | Weekday (divided over 5 days) | 0.72118 | 0.762365 | + +-------------------------------+---------+----------+ + | Weekend (divided over 2 days) | 0.27882 | 0.237635 | + +-------------------------------+---------+----------+ + +.. _month_weight_table: + +.. table:: Month weight factors + + +-----------+---------------+ + | Month | Weight Factor | + +===========+===============+ + | January | 0.0731 | + +-----------+---------------+ + | February | 0.0697 | + +-----------+---------------+ + | March | 0.0817 | + +-----------+---------------+ + | April | 0.0823 | + +-----------+---------------+ + | May | 0.0875 | + +-----------+---------------+ + | June | 0.0883 | + +-----------+---------------+ + | July | 0.0923 | + +-----------+---------------+ + | August | 0.0934 | + +-----------+---------------+ + | September | 0.0847 | + +-----------+---------------+ + | October | 0.0865 | + +-----------+---------------+ + | November | 0.0802 | + +-----------+---------------+ + | December | 0.0802 | + +-----------+---------------+ + +BEV VMT Projections  +~~~~~~~~~~~~~~~~~~~ +To calculate the BEV VMT by vehicle class for each UA, state-level BEV VMT projections +were based on the NREL Electrification Futures Study for 9 vehicle types +:cite:p:`NREL_electric_technology_adoption`:  + +1. LDV BEV Cars: 100 mi, 200 mi, 300 mi  +2. LDV BEV Trucks: 100 mi, 200 mi, 300 mi  +3. BEV Transit Buses  +4. MDV Trucks  +5. HDV Trucks  + +Projections were used for 2030, 2040, and 2050. The 2017 base year assumptions were +calibrated based on historical data. For all years, BEV VMT at the state level was +translated to BEV VMT at the UA level by multiplying the state-level projections by the +fraction of state VMT allocated to each UA. It is assumed that the proportion of VMT +occurring in urban areas relative to total state VMT will be constant moving into the +future. There are some UAs that did not have VMT data. Out of 481 UAs, 56 did not have +VMT per capita data from the DOT, so those entries are zeros. + +Once each UA and the state’s RA have their projected annual VMT for a simulation year, +the annual VMT is distributed to each day of the year based on weight factors from U.S. +EPA MOVES model, see :numref:`scaling_table`. Each daily weight factor represents the +fraction of annual VMT that is traveled in that specific day. The daily weight factors +vary by month, whether the VMT is in a UA or a RA, and whether the day is a weekday or +a weekend day. For example, within a given urban area all weekdays in January have the +same weight factor and therefore the same allocated VMT. + +.. _scaling_table: + +.. table:: Urban and rural scaling factors by month and weekday vs weekend + + +-----------+-------------------+-------------------+ + | | Urban  | Rural | + +-----------+---------+---------+---------+---------+ + | Month | Weekday | Weekend | Weekday | Weekend | + +===========+=========+=========+=========+=========+ + | January | 0.00252 | 0.00196 | 0.00238 | 0.00230 | + +-----------+---------+---------+---------+---------+ + | February | 0.00266 | 0.00207 | 0.00251 | 0.00243 | + +-----------+---------+---------+---------+---------+ + | March | 0.00279 | 0.00218 | 0.00266 | 0.00257 | + +-----------+---------+---------+---------+---------+ + | April | 0.00296 | 0.00231 | 0.00268 | 0.00277 | + +-----------+---------+---------+---------+---------+ + | May | 0.00299 | 0.00233 | 0.00285 | 0.00275 | + +-----------+---------+---------+---------+---------+ + | June | 0.00313 | 0.00244 | 0.00297 | 0.00287 | + +-----------+---------+---------+---------+---------+ + | July | 0.00321 | 0.00250 | 0.00301 | 0.00291 | + +-----------+---------+---------+---------+---------+ + | August | 0.00319 | 0.00249 | 0.00304 | 0.00294 | + +-----------+---------+---------+---------+---------+ + | September | 0.00302 | 0.00236 | 0.00285 | 0.00276 | + +-----------+---------+---------+---------+---------+ + | October | 0.00298 | 0.00232 | 0.00282 | 0.00272 | + +-----------+---------+---------+---------+---------+ + | November | 0.00284 | 0.00221 | 0.00270 | 0.00261 | + +-----------+---------+---------+---------+---------+ + | December | 0.00279 | 0.00217 | 0.00262 | 0.00253 | + +-----------+---------+---------+---------+---------+ + +The trip data are scaled based on the allocated daily VMT. The daily patterns are +adjusted by scaling the VMT of each trip within the daily patterns so that the total +across the simulation matches the Annual VMT projection for each state.  + + +Fuel Efficiency Projections  +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +NREL’s Electrification Futures Study also projects average BEV fuel economy over time, based on assumptions regarding technology improvements and vehicle range +:cite:p:`NREL_efs`. NREL provides a range of possible BEV fuel economies (“Slow +Advancement”, “Moderate Advancement”, and “Rapid Advancement”). The charging model uses +mid-range values as the default fuel economy for each vehicle category and year, as +shown in :numref:`bev_fuel_economy_table`.  + +.. _bev_fuel_economy_table: + +.. table:: Default BEV fuel economy by vehicle category and year + + +-------------------------------------+---------------------------+ + | | Fuel Economy (mile/GGE) | + + +------+------+------+------+ + | Vehicle Type | 2017 | 2030 | 2040 | 2050 | + +=====================================+======+======+======+======+ + | LDV BEV cars, 100 mile range | 138 | 156 | 158 | 160 | + +-------------------------------------+------+------+------+------+ + | LDV BEV cars, 200 mile range | 129 | 152 | 155 | 156 | + +-------------------------------------+------+------+------+------+ + | LDV BEV cars, 300 mile range | 122 | 148 | 152 | 154 | + +-------------------------------------+------+------+------+------+ + | LDT BEV trucks, 100 mile range | 103 | 105 | 106 | 107 | + +-------------------------------------+------+------+------+------+ + | LDT BEV trucks, 200 mile range | 97 | 103 | 104 | 104 | + +-------------------------------------+------+------+------+------+ + | LDT BEV trucks, 300 mile range | 94 | 100 | 101 | 102 | + +-------------------------------------+------+------+------+------+ + | Transit buses | 13 | 16 | 18 | 19 | + +-------------------------------------+------+------+------+------+ + | MDV trucks | 16 | 21 | 23 | 24 | + +-------------------------------------+------+------+------+------+ + | HDV trucks | 12 | 16 | 17 | 18 | + +-------------------------------------+------+------+------+------+ + +Smart Charging Optimization Algorithm  +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The smart charging algorithm was developed by :cite:p:`2014:zhang`. The algorithm is a +least cost optimization problem, with the cost function as follows: + +.. math:: + + min \sum_{i=1}^n \sum_{j=1}^{seg(i)} f_{ij} x_{ij} + +| where: +| :math:`f` is the cost of electricity in [$/kWh], +| :math:`x` is the increase in vehicle state of charge in [kWh], +| :math:`i` is the dwell period per trip count, +| :math:`seg(i)` is the number of dwell segments for dwell period :math:`i`, +| :math:`j` is the dwell segment in [h], +| :math:`n` is the total number of dwell per trip periods + +The total number of dwell segments :math:`seg(i)` will depend on the how long the +vehicle is parked. + +**Equality** constraint: + +.. math:: + + \sum_{i=1}^n \sum_{j=1}^{seg(i)} x_{ij} + \sum_{i=1}^n y_i = 0 + +| where: +| :math:`j` is the discharged energy from driving + +**Inequality** constraints: + +.. math:: + + y_1 & > -c + + y_1 & + \sum_{j=1}^{seg(1)} x_{1j} + y_2 > -c + + y_1 & + \sum_{j=1}^{seg(1)} x_{1j} + y_2 + \dotsc + \sum_{j=1}^{seg(n-1)} x_{n-1j} + y_n > -c + +| where: +| :math:`c` is the battery energy capacity in [kWh] + +**Bounds**: + +.. math:: + + 0 \leq x_{ij} \leq p_{ij} \times \Delta t_{ij} \times \eta + +| where: +| :math:`p` is the rated power of charger in [kW] +| :math:`\Delta t` is the dwell time in [h] +| :math:`\eta` is the charging efficiency + +The optimization is structured to minimize the cost to charge each battery electric +vehicle within defined battery constraints. It is conducted at hourly timescale, +meaning that cost and electricity demand are provided in hourly segments. Charging +efficiency is dependent on the type of electric vehicle supply equipment (EVSE). +Efficiency tends to increase with higher charging rates. + +If a dwell time (:math:`\Delta t`) falls below one hour, the charge (x) available for +that segment will be reduced proportional to the amount of time spent parked (i.e., if +a vehicle is parked for 30 minutes, the charge will be reduced to :math:`1/2`). The +default minimum dwell time to consider a charging event is 0.2 hours, or 12 minutes. +This value can be modified depending on the user’s scenario. The minimum dwell time is +set in order to avoid impractical charging events where, in the real world, a vehicle +operator would not plug in their vehicle due to the shortness of the stop. If the +charging rate available is high (e.g., DC Fast Charging), a shorter minimum dwell time +may be warranted. diff --git a/docs/index.rst b/docs/index.rst index fb2e5f5a7..3e8393727 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -14,6 +14,8 @@ folder adjacent to your clone of PreREISE. generate any input data if you use the scenario framework to carry out power flow study. +.. contents:: :local: + General Comments ---------------- @@ -61,6 +63,10 @@ Transportation Electrification .. include:: demand/transportation_electrification/manual.rst +.. include:: + demand/transportation_electrification/methodology.rst + + NREL Electrification Futures Study Demand and Flexibility Data ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. include:: @@ -107,3 +113,7 @@ Resources .. include:: renewables/notebook.rst + +Bibliography +++++++++++++ +.. bibliography::