- Serious Games
Archive for category Urban Industrial Development
SEMS Research Highlights 2015: Enabling the Adoption of Alternative Fuel Vehicles – An Approach to Refueling Station Spatial Placements
Refueling station accessibility for more cleaner and greener energy is one of the most important factors in the adoption of alternative fuel vehicles.
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
SEMS in collaboration with PT Makara Mas (Holding Company of Universitas Indonesia) conducted an introductory system dynamics workshop on modeling sustainable development for Fiscal Policy Agency – Ministry of Finance, Government of Indonesia. The workshop was part of Low Carbon Support, provided by the United Kingdom (UK) for the Ministry of Finance, especially the Centre for Climate Change Financing and Multilateral Policy (PKPPIM) in the Fiscal Policy Agency. PKPPIM are tasked to recommend a low carbon fiscal policies especially starting from the national budget 2015. This is why they needed a more integrated modeling tool to be able to evaluate green fiscal policy impacts.
FPA has already a strong group of economic models that are based on IO Models, SAM, and CGE, however since the questions of green policy is multi-dimensions with multi-sectoral approach, they feel that they need to have a more adaptive model to answer these questions.
The workshop was conducted for 5 days in the 2nd week of February, ranging from the basics of systems thinking and system dynamics, group dynamics, simple model building and closed by discussion on future models development of a new “green fiscal policy” model.
SBM ITB with UKP4 (Presidential Working Unit for Supervision and Management of Development) organized International Workshop on Agent Based Modeling for Policy Development following last year successful event. This year Pak Akhmad Hidayatno was invited to present one SEMS Lab research study conducted in Agent Based Modeling regarding Pedestrian Movement. The research case study was conducted with the support of PT Mass Rapid Transit Jakarta (MRT Jakarta) to evaluate the feasibility of their current underground station design based on pedestrian movement flow. Underground stations are much more expensive the their on-land or elevated stations. Therefore, any design changes during construction could be costly.
Read the rest of this entry »
This year, as part of the modeling project major assignment of system modeling class, the class decided to raise the topic that are close to their daily activities, the Greater Jakarta Commuter Line. Universitas Indonesia has a dedicated intercity rail station of a train line that spans from Central Jakarta to the Bogor City in the south area , nickname “KA Jabotabek”, KA is an acronym for “Kereta Api” or Trains. It is targeted that in 2014, there will be 1.2 million passengers carried by this line
The model are complex and challenging with so many obstacles since they started building the model 2 months ago. However, they have successfully completed their model, and today they will be presenting their models in the SEMS Lab’s Simulation Gaming Area. Each groups will be presenting a specific problems that they want to solve based on the generic model they vuild, from maintenance scheduling, passenger queue, resource scheduling, train schedule, electrical supply and many other.
Therefore, congratulations to Class of 2009 on they achievement on developing a very complex discrete event model.