Reliability-as-a-Service Usage of Electric Vehicles: Suitability Analysis for Different Types of Buildings
<p>Flowchart for parameter extraction and suitability analysis of RaaS usage of EVs.</p> "> Figure 2
<p>Daily mileage and per-trip travelling-duration histograms for vehicles.</p> "> Figure 3
<p>Probability densities of arrival and departure times: (<b>a</b>) home; (<b>b</b>) workplace.</p> "> Figure 4
<p>Cumulative densities for arrival/departure time for home/workplace.</p> "> Figure 5
<p>Percent of vehicles staying at home/workplace during different hours of day.</p> "> Figure 6
<p>Energy consumption and useable battery size of commercially available EVs.</p> "> Figure 7
<p>Daily energy consumption of EVs during weekdays and holidays.</p> "> Figure 8
<p>Useable energy for RaaS in EVs: (<b>a</b>) home; (<b>b</b>) workplace.</p> "> Figure 9
<p>Results of useable energy in residential buildings for RaaS during weekdays.</p> "> Figure 10
<p>Results of useable energy in residential buildings for RaaS during holidays.</p> "> Figure 11
<p>Useable energy in resintial buildings for 50% participation case: (<b>a</b>) weekdays; (<b>b</b>) holidays.</p> "> Figure 12
<p>Results of useable energy in commercial/industrail buildings for RaaS during weekdays.</p> "> Figure 13
<p>Results of useable energy in commercial/industrial buildings for RaaS during holidays.</p> "> Figure 14
<p>Useable energy in commercial/industrial buildings for 50% participation case: (<b>a</b>) weekdays; (<b>b</b>) holidays.</p> "> Figure 15
<p>Results of useable energy in mixed buildings for RaaS during weekdays.</p> "> Figure 16
<p>Results of useable energy in mixed buildings for RaaS during holidays.</p> "> Figure 17
<p>Useable energy in mixed buildings for 50% participation with 35% home-based EVs case: (<b>a</b>) weekdays; (<b>b</b>) holidays.</p> "> Figure 18
<p>Variation index for different EV fleet sizes.</p> "> Figure 19
<p>Available energy for RaaS under different EV fleet sizes.</p> ">
Abstract
:1. Introduction
- The presence of EVs in a particular location during different hours of the day varies, leading to uncertainty.
- The amount of energy available from each EV to provide RaaS during any event is uncertain.
- The probability of the presence of EVs in a particular location could be different for weekdays and holidays, especially for workplaces.
- The percentage of EV owners willing to participate in the RaaS program could also vary from location to location.
2. Parameter Extraction from NHTS Data
2.1. Data Preprocessing
- Remove trips with unreported trip mileage and/or unreported trip duration.
- Select only trips with driving mode as car, SUV, minivan, and pickup truck (remove trips on motorcycle, bicycle, school bus, public or commute bus, etc.).
- Remove trips with unidentified vehicle ID.
- Remove trips with an unspecified origin/destination and day of the trip.
- Calculate vehicle speed using reported mileage and trip duration, then remove trips with unrealistic speeds.
2.2. Daily Mileage of Vehicles
Algorithm 1 Daily mileage computation for vehicles. |
|
2.3. Daily Arrival and Departure Times
Algorithm 2 Arrival and departure times tracking of vehicles. |
|
2.4. Vehicle Stay Time
3. EV Energy Estimation for RaaS
3.1. Allocation of EVs to Different Mileage Ranges
3.2. Daily Energy Consumption of EVs
3.3. Energy for RaaS Usage in EVs
4. Suitability Analysis for Different Types of Buildings
4.1. Residential Buildings
4.2. Commercial/Industrial Buildings
4.3. Mixed Buildings
5. Discussion and Analysis
5.1. Variation Index for Mixed Buildings
5.2. EV Fleet Size and Available Energy for RaaS
5.3. Future Research Directions
- Estimation of optimum EV fleet sizes for different building types, considering the load characteristics, such as load nature (critical vs. non-critical) and magnitude of loads (required duration of backup power).
- Determination of the potential of using EVs for a particular type of commercial/industrial building, considering the correlation between the presence of EVs and the amount of critical load.
- Analysis of the potential of using EVs to provide RaaS to industrial buildings located in the suburbs with poor public transportation systems. This will change the ratio of vehicles used for commuting and, consequently, the amount of energy available for RaaS.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Abbreviations | |
BESS | Battery energy storage system |
EV | Electric vehicle |
NHTS | National Household Travel Survey |
RaaS | Reliability-as-a-service |
SoC | State-of-charge |
Identifiers | |
i | Identifier for unique vehicles, running from 1 to I. |
k | Identifier for unique trips, running from 1 to K. |
t | Identifier for interval of a day, running from 1 to T (24). |
l | Identifier for number of bin edges (used in daily mileage), running from 1 to L. |
n | Identifier for EVs, running from 1 to N. |
Parameters and variables | |
Daily mileage of vehicle, i, for weekdays and holidays, respectively. | |
Identity of vehicle used for trip k. | |
Identity of the household from where trip k was carried out. | |
Distance traveled in miles during trip k. | |
Home arrival and departure time, respectively, x ∈ {w: weekdays, h: holidays}. | |
Workplace arrival and departure time, respectively, x ∈ {w: weekdays, h: holidays}. | |
Arrival and departure time, respectively for trip k. | |
The ratio of vehicles staying at home and workplace, respectively, during time t. | |
The ratio of vehicles returning home and used for commute, respectively. | |
Home arrival and departure probability density, respectively, for time t. | |
Workplace arrival and departure probability density, respectively, for time t. | |
Histogram value of the daily mileage for bin edge l. | |
Number of EVs in mileage range corresponding to bin edge l. | |
Daily mileage for EVs in bin edge l. | |
Bin width in km, used for daily mileage of vehicles. | |
Daily energy consumption of EV n. | |
Energy consumption per km of EV n in Wh. | |
Excess amount of energy available in EV n at home and workplace, respectively. | |
Amount of energy available in EV n at home and workplace at arrival time, respectively. | |
Preferred minimum level of SoC for EV n. | |
Useable battery size (capacity) of EV n. | |
Used to define the desired amount of reserve energy for EVs in the workplace for upcoming tasks. | |
Amount of energy available to be used for RaaS during any interval t. | |
Used to determine the ratio of home-based EVs and workplace-based EVs for mixed buildings. | |
The ratio of EVs willing to participate in RaaS. | |
The proposed variation index. | |
The maximum and minimum amount of energy available for RaaS, respectively. | |
Mean and standard deviation of energy available for RaaS, respectively. |
References
- Campbell, R.J. CRS Report for Congress: Weather-Related Power Outages and Electric System Resiliency. 2012. Available online: https://sgp.fas.org/crs/misc/R42696.pdf (accessed on 3 December 2021).
- Wang, Y.; Chen, C.; Wang, J.; Baldick, R. Research on Resilience of Power Systems under Natural Disasters—A Review. IEEE Trans. Power Syst. 2016, 31, 1604–1613. [Google Scholar] [CrossRef]
- Faraji, J.; Babaei, M.; Bayati, N.; Hejazi, M.A. A comparative study between traditional backup generator systems and renewable energy-based microgrids for power resilience enhancement of a local clinic. Electronics 2019, 8, 1485. [Google Scholar] [CrossRef] [Green Version]
- A Case Study on Emergency Backup Power with Renewable Energy. Available online: https://sustain.ubc.ca/about/resources/case-study-emergency-backup-power-renewable-energy (accessed on 3 December 2021).
- Alberta Utilities Commission. Waterton Battery Energy Storage System. 2021. Available online: https://efiling-webapi.auc.ab.ca/Document/Get/683303 (accessed on 3 December 2021).
- Li, J.; Niu, D.; Wu, M.; Wang, Y.; Li, F.; Dong, H. Research on Battery Energy Storage as Backup Power in the Operation Optimization of a Regional Integrated Energy System. Energies 2018, 11, 2990. [Google Scholar] [CrossRef] [Green Version]
- HSB. Maintaining Emergency and Standby Engine-Generator Sets. 2020. Available online: https://www.munichre.com/content/dam/munichre/global/content-pieces/documents/447-Recommended-Practice-for-Maintaining-Emergency-and-Standby-Engine-Generator-Sets.pdf/_jcr_content/renditions/original.media_file.download_attachment.file/447-Recommended-Practice-for-Maintainig-Emergency-and-Standby-Engine-Generator-Sets.pdf (accessed on 13 January 2022).
- International Energy Agency. Global EV Outlook 2021 Overview; International Energy Agency: Paris, France, 2021. [Google Scholar]
- Electric Vehicle Database Useable battery capacity of fully electric vehicles cheatsheet—EV Database. Available online: https://ev-database.org/cheatsheet/useable-battery-capacity-electric-car (accessed on 3 December 2021).
- Li, X.; Tan, Y.; Liu, X.; Liao, Q.; Sun, B.; Cao, G.; Li, C.; Yang, X.; Wang, Z. A cost-benefit analysis of V2G electric vehicles supporting peak shaving in Shanghai. Electr. Power Syst. Res. 2020, 179, 106058. [Google Scholar] [CrossRef]
- Yoo, Y.; Al-Shawesh, Y.; Tchagang, A. Coordinated control strategy and validation of vehicle-to-grid for frequency control. Energies 2021, 14, 2530. [Google Scholar] [CrossRef]
- Haghi, H.V.; Qu, Z. A Kernel-Based Predictive Model of EV Capacity for Distributed Voltage Control and Demand Response. IEEE Trans. Smart Grid 2018, 9, 3180–3190. [Google Scholar] [CrossRef]
- Agarwal, L.; Peng, W.; Goel, L. Using EV battery packs for vehicle-to-grid applications: An economic analysis. In Proceedings of the 2014 IEEE Innovative Smart Grid Technologies—Asia (ISGT ASIA), Kuala Lumpur, Malaysia, 20–23 May 2014; IEEE Computer Society: Piscataway Township, NJ, USA, 2014; pp. 663–668. [Google Scholar]
- Kataoka, R.; Ogimoto, K.; Iwafune, Y. Marginal Value of Vehicle-to-Grid Ancillary Service in a Power System with Variable Renewable Energy Penetration and Grid Side Flexibility. Energies 2021, 14, 7577. [Google Scholar] [CrossRef]
- Van Heuveln, K.; Ghotge, R.; Annema, J.A.; van Bergen, E.; van Wee, B.; Pesch, U. Factors influencing consumer acceptance of vehicle-to-grid by electric vehicle drivers in the Netherlands. Travel Behav. Soc. 2021, 24, 34–45. [Google Scholar] [CrossRef]
- Hussain, A.; Musilek, P. A Reward Mechanism for Reliability-as-a-Service Usage of Electric Vehicles. In Proceedings of the 13th IEEE Conference on Innovative Smart Grid Technologies (ISGT 2022), Washington, DC, USA, 21–24 February 2022; Available online: https://era.library.ualberta.ca/items/383d89bb-c29b-4fbc-b683-769c6483fb91 (accessed on 3 December 2021).
- National Household Travel Survey. Available online: https://nhts.ornl.gov/downloads (accessed on 3 December 2021).
- Energy Consumption of Full Electric Vehicles Cheatsheet—EV Database. Available online: https://ev-database.org/cheatsheet/energy-consumption-electric-car (accessed on 3 December 2021).
- Yang, W.; Xiang, Y.; Liu, J.; Gu, C. Agent-Based Modeling for Scale Evolution of Plug-in Electric Vehicles and Charging Demand. IEEE Trans. Power Syst. 2018, 33, 1915–1925. [Google Scholar] [CrossRef]
- Nie, Y.; Chung, C.Y.; Xu, N.Z. System State Estimation Considering EV Penetration with Unknown Behavior Using Quasi-Newton Method. IEEE Trans. Power Syst. 2016, 31, 4605–4615. [Google Scholar] [CrossRef]
- Gong, H.; Ionel, D.M. Optimization of aggregated EV power in residential communities with smart homes. In Proceedings of the 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020, Chicago, IL, USA, 23–26 June 2020; Institute of Electrical and Electronics Engineers Inc.: Piscataway Township, NJ, USA, 2020; pp. 779–782. [Google Scholar]
- U.S. Department of Transportation. 2017 NHTS Data User Guide; Federal Highway Administration Office of Policy Information: Washington, DC, USA, 2018; Volume 2018.
- Zhang, P.; Qian, K.; Zhou, C.; Stewart, B.G.; Hepburn, D.M. A methodology for optimization of power systems demand due to electric vehicle charging load. IEEE Trans. Power Syst. 2012, 27, 1628–1636. [Google Scholar] [CrossRef]
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Hussain, A.; Musilek, P. Reliability-as-a-Service Usage of Electric Vehicles: Suitability Analysis for Different Types of Buildings. Energies 2022, 15, 665. https://doi.org/10.3390/en15020665
Hussain A, Musilek P. Reliability-as-a-Service Usage of Electric Vehicles: Suitability Analysis for Different Types of Buildings. Energies. 2022; 15(2):665. https://doi.org/10.3390/en15020665
Chicago/Turabian StyleHussain, Akhtar, and Petr Musilek. 2022. "Reliability-as-a-Service Usage of Electric Vehicles: Suitability Analysis for Different Types of Buildings" Energies 15, no. 2: 665. https://doi.org/10.3390/en15020665