Modelling the Feasibility of Alternative Fuel Infrastructure: CNG in the GTHA Case Study

Transcription

1 Modelling the Feasibility of Alternative Fuel Infrastructure: CNG in the GTHA Case Study ESRI Canada GIS Scholarship 2017 Amal Ghamrawi University of Windsor Masters of Applied Science in Civil Engineering

2 Table of Contents Introduction and Objective... 3 Methodology... 3 Demand Estimation...3 Identify Possible Locations...4 Service Areas...4 Allocating Facilities...4 Results... 4 Demand and Proposed Sites...4 Service Areas...5 Location-Allocation...6 Conclusions... 7 References... 8 List of Figures Figure 1: Study Area... 3 Figure 2: Methodology... 3 Figure 3: Potential Sites for CNG Storage Facility... 5 Figure 4: Service Areas of Potential Sites in Low Population Areas... 6 Figure 5: Travel Time Statistics... 7 Figure 6: Selected Sites for GTHA Coverage... 7 List of Tables Table 1: Heavy Truck Count Model... 4 Table 2: Total Heavy-Truck Trips and Truck Counts and Correlation Matrix

3 Introduction and Objective Natural Gas Vehicles (NGVs) have the potential to reduce the environmental and societal impacts associated with diesel powered heavy-duty trucks used for urban freight movement. To enable the adoption of CNG, mobile on-site refueling at truck yards is proposed. CNG can be housed at central stations with locations optimized based on demand. To facilitate the adoption of NGVs while maintaining the efficiency of goods movement, the overall objective of this research is to examine the feasibility of alternative fuel infrastructure. The specific objectives of this study are to estimate the potential CNG demand and to identify potential sites for facilities that can store CNG and CNG-carrying fleets. The Greater Toronto and Hamilton Area (GTHA) is the proposed study area, it can be delineated using Census Metropolitan Areas (Figure 1). The GTHA hosts the largest market in Canada and commercial vehicle movement is prevalent in this area due to the high presence of warehouses and distributions centers and accessibility and proximity to major Ontario highways such as the Highway 401. Methodology The overall methodology of the project is summarized in the Figure 2. Figure 1: Study Area Estimate Demand: Heavy Duty Truck Count Identify Possible CNG Storage Locations Estimate Service Areas for these Potential Sites Allocate Facility Locations Figure 2: Methodology Demand Estimation The demand for CNG is equated to the number of heavy-duty trucks located in the GTHA. Census tract truck counts were obtained from R. L. Polk and Co for the Windsor-Essex Region. A linear regression model was developed to determine the relationship between the number of jobs and heavy trucks per census tract and later applied to the GTHA. Zonal truck counts were then used to determine the demand for CNG fueling per zone. The centroid of each census tract will be used to spatially distribute the demand points. Two models are used to predict heavy-duty truck trips to verify the truck count model. Quick Response Freight Manual (QRFM) model and the model proposed in Roorda et al. (2010). 3

4 Identify Possible Locations Possible sites for CNG refueling and storage stations must be selected to determine the optimal locations with respect to CNG demand. The proposed location must meet four requirements: (i) access to an existing natural gas pipeline, (ii) available land, (iii) proximity to the road network, and (iv) low population. The existing DMTI Natural Gas Pipeline, Ontario Road Network and Land Use Data were obtained from the Scholars Geoportal. The proximity to a major road enhances accessibility to the market and reduces vehicular emissions. Finally, locating the stations in census tracts with lower population will minimize any risks and concerns associated with the natural gas storage. Service Areas Location is often considered the most important factor leading to the success of a private- or public-sector organization (5). Therefore, this step is essential to ensure the optimal selection in facility locations. Using the selected potential locations, the ArcGIS 10.2 Network Analyst is used to create service areas to determine the coverage of the proposed facilities. The travel time is based on free-flow driving time and is suitable since the refueling fleet will travel at night. Allocating Facilities The Network Analyst s Location-Allocation solver is used to select the ideal locations from the potential sites. A p-median problem with free-flow travel time as an impedance will aim to minimize fixed and transportation costs while refueling of the maximum number of trucks with the optimal number and location of facilities. Results Demand and Proposed Sites The estimated truck-count model and associated statistics based on Windsor Heavy-Truck zonal counts are presented in Table 1. It is evident that the model has strong explanatory power based on the t-statistics and the coefficient of determination. Table 1: Heavy Truck Count Model Parameter Coefficients Standard Error t-stat P-value Jobs 11 & < Jobs 53, 54, 55 & R Adjusted R NAICS Industry Sector 11 and 23 2 NAICS Industry Sectors 53, 54, 55 and 81 The estimated total trucks and trips and the correlations between zonal totals are presented in Table 2. The number of heavy truck trips and trucks can be compared since they should be similar in magnitude as each truck represents the potential of generating a commercial trip. It is logical that the number of trucks in the GTHA is lower than the number of trips because the number of trucks likely represents the trucks that operate from the GTHA and will return to the zone on a nightly basis. The overall zonal distribution of heavy trucks and trips is consistent and the three have a strong correlation. 4

5 Table 2: Total Heavy-Truck Trips and Truck Counts and Correlation Matrix Roorda et al. (24 hr) QRFM Trucks Totals Roorda et al. (24 hr) QRFM Truck Counts , ,781 38,972 Based on the criteria, seventeen potential sites were selected in the GTHA as shown in Figure 3, only sites with a population lower or equal to 6,750 were further considered in the analysis. Figure 3: Potential Sites for CNG Storage Facility Service Areas Service areas were obtained for the ten potential sites serving the GTHA. Figure 4 proves that four major areas are not served. Area A is the City of Toronto, and is not serviced due to the high population density and the lack of natural gas pipelines and available land in the area. Regions B and C have sparse CNG demand and in general are not reached within a twenty-minute driving range due to their distance from potential sites. With the existing proposed facilities, it likely is not economical to convert fleets in regions A, B and C to NGV. Region D is a small section of un-serviced area that may be better suited to be serviced from outside of the GTHA if adjacent areas have lower populations and land prices may be cheaper. 5

6 Figure 4: Service Areas of Potential Sites in Low Population Areas Location-Allocation Results suggest ten facilities should be established. However, this scenario does not exploit the potential capacity of each facility as several of these proposed facilities would have low capacities. Also, from an economic perspective it does not make sense to establish ten facilities for CNG is in its introductory phase. Investing in a few facilities at first and establishing more facilities in the future is more strategic as these facilities require a large capital investment. However, minimizing the number of facilities will increase the travel time of the refueling fleet and therefore incur additional costs and safety risks associated with travel. Restricting the analysis to select only a specified number of facilities will allow for the comparison of travel times associated with the varying number of facilities. Figure 5 is a graph of the minimum, maximum and average travel time from established facilities to the demand point for a varying number of facilities. The travel time values tend to be consistent for six to ten established facilities. When the number of established facilities is five or lower, both the minimum and average travel time slowly increase and the maximum travel time jumps from 44 minutes to 66 minutes (three to two required facilities). The total travel time increases after when there are less than three facilities. 6

7 Travel Time Statistics Travel Time to Demand (minutes) 70 Minimim Maximum Mean Number of Facilities Established Figure 5: Travel Time Statistics It is not economical to establish more than six facilities to service the GTHA area since there is not much variation in the service times past six facilities. Establishing three facilities will allow for similar average service times as six facilities, Figure 6 provides the locations of the proposed sites for three and four established facilities respectively, serving the entire estimated GTHA demand. Three facilities are recommended if the objective is to reduce fixed costs associated with the CNG stations. Conclusions In the GTHA context, three CNG storage stations are recommended. Further research is required on the costbenefit advantages of CNG over Diesel. Figure 6: Selected Sites for GTHA Coverage Examining the environmental impact associated with the entire life-cycle of both CNG and Diesel powered trucks is essential before implementing this conversion. To determine the feasibility of this project it is essential to obtain more detailed costs associated with: the conversion of vehicles to natural gas, refueling vehicles and the capital cost of establishing a natural gas station in any of the proposed census tracts. Information regarding local regulations and the actual capacity of CNG-carrying tractor-trailers will provide more accurate demandallocation results. 7

8 References 1. CHASS, CHASS Data Centre 2011 National Household Survey. Faculty of Arts & Science, University of Toronto. Last Accessed 18 April Clarke, S. and DeBruyn, J., Vehicle Conversion to Natural Gas or Biogas Factsheet. Ontario Ministry of Agriculture, Food and Rural Affairs. Queens Printer for Ontario. Last Accessed 17 April ESRI, ArcGIS Resource Center Desktop 10: Location-allocation analysis /. Accessed 15 April Faiz, A., Weaver, C.S., Walsh, M.P., Air Pollution from Motor Vehicles: Standards and Technologies for Controlling Emissions. The World Bank, Washington, DC, pp Holguín-Veras, J., Sánchez-Díaz, I., Lawson, C., Jaller, M., Campbell, S., Levinson, H., Shin, H.S., Transferability of Freight Trip Generation Models. Transportation Research Record: Journal of the Transportation Research Board, 2379, Hunt, J., Stefan, K. and Brownlee, A., Establishment-based survey of urban commercial vehicle movements in Alberta, Canada: survey design, implementation, and results. Transportation Research Record: Journal of the Transportation Research Board, 1957, pp J.B. Hunt, Natural Gas in Transportation: J.B. Hunt Perspective White Paper. Accessed 5 April Metrolinx (2016). GTHA Urban Freight Study & Status Update. Last Accessed 27 May Muñuzuri, J., Cortés, P., Onieva, L., Gaudix, J., Estimation of Daily Vehicles Flows for Urban Freight Deliveries. Journal of Urban Planning and Development, 138(1), Omnitek Engineering, Project Management: Implementing a Successful Natural Gas Policy. Last Accessed 10 April Ris, C., U.S. EPA health assessment for diesel engine exhaust: a review. Inhalation Toxicology: International Forum for Respiratory Research, 19, Roorda, M., Hain, M., Amirjamshidi, G., Cavalcante, R., Abdulhai, B. and Woudsma, C., Exclusive truck facilities in Toronto, Ontario, Canada: analysis of truck and automobile demand. Transportation Research Record: Journal of the Transportation Research Board, 2168, pp Udaeta, M.E.M., de Oliveira Bernal, J.L., Grimoni, J.A.B. and Galvão, L.C.R., Natural Gas Virtual-Pipeline for Alternative Energy Distribution. INTECH Open Access Publisher. 14. Yeh, S, An empirical analysis on the adoption of alternative fuel vehicles: The case of natural gas vehicles. Energy Policy 35,