Refueling station accessibility for more cleaner and greener energy is one of the most important factors in the adoption of alternative fuel vehicles.

Image Courtesy of Radar Pena

If the refueling station is not strategically located, people will be hesitant to adopt the new alternative fuel (such as gas, hydrogen or other type of alternative fuel).
Due to the importance of locations,  researcher in System Engineering, Modeling, and Simulation lab of Universitas Indonesia develop an operations research-based spatial-model to determine ideal refueling station locations. Early results has delivered a whopping gain up to 96% demand coverage, while at same time maintained profitability in each individual location.

The model was develop in three stages: demand mapping, spatial simulation and financial screening.

First stage is determining how many refueling stations are needed to cover potential demand within the scope. This is done through a series of calculations: total vehicles converted are multiplied by each vehicle’s fuel demand, subtracted by the amount of existing alternative fuel supply (if some refueling stations already exist), and then divided by an individual station’s capacity. Through these calculations, a number of stations needed to be built can be obtained.

In the second stage, multiple variables are used as input to reflect real-world conditions in a geographic information system-based spatial model. These variables include spatial data, such as the locations of distributed potential demand, already-existing alternative refueling stations, and candidate locations to build the new refueling stations, as well as non-spatial data, like the daily capacity of each refueling station and the maximum distance car owners are willing to travel to reach a station. The model then uses a location-allocation technique—the ‘maximize capacitated coverage’ approach—to determine the ideal locations for every refueling stations. These locations cover the most demand possible while subjecting to the capacity of individual stations.

The final stage is financial screening of the chosen locations. Three economic metrics are used to determine profitability: the NPV, IRR, and payback period. The demand in each location chosen, obtained through the spatial model, is entered into a simple financial model of a refueling station’s operations to reveal the three economic metrics. Afterwards, a final analysis is conducted to determine other alternatives to reach demand points not yet covered, or to replace unprofitable locations.

In this study, the researchers focused on the adoption of natural gas vehicles by public transportation fleets in DKI Jakarta, as the case study. There were four scenarios used, based primarily on the types of candidate locations (to simulate ease of implementation) and the simulated traffic conditions. The resulting locations show a range of 79-96% coverage, with the lower numbers found in traffic jam scenarios. To boost coverage and replace unprofitable locations, there were 2 possible alternatives: constructing stand-alone refueling stations (not constrained by candidate locations) and deploying mobile refueling units (MRUs).

This work has implications for various types of alternative fuel vehicles, not just limited to natural gas. Refueling stations are capital-heavy infrastructures regardless of fuel type, especially for new, still growing vehicle types. The approach used is replicate-able and adjustable for other situations to improve the adoption process.

This research was conducted by Aziiz Sutrisno, Akhmad Hidayatno, Dio Aufa Handoyo, Eka Nugraha Putra