Model-Based Analysis of Singapore’s Energy System
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Sustainable energy production and usage will become important key factors in future energy systems. In particular, Singapore as one of the world’s most densely populated countries with a high standard of living, needs a long-term concept for its energy use. Besides the high oil consumption of the industrial sector, power generation, which is nearly fully based on gas and oil, causes a high demand of fossil fuels. This leads not only to high CO2 emissions but also to a significant import dependency on neighboring countries. Renewable energy sources and a more efficient conversion of energy are necessary for sustainable economic growth.
The analysis of Singapore’s energy system is carried out by using the mathematical optimization model TIMES (The Integrated MARKAL (Market Allocation) EFOM (Energy Flow Optimization Model) System). This optimization model acts as an economic model generator for local, national or multi-regional energy systems. After modeling Singapore’s energy system as a network consisting of processes (power plants, electric vehicles etc.) and commodities (energy carriers, passenger kilometers etc.), it is cost-optimized regarding to a defined objective function. Furthermore, various boundary conditions (energy prices, policies etc.) are taken into consideration.
Based on the TIMES calculations, different scenarios for energy generation, conversion and consumption are analyzed and assessed on the basis of primary energy demand, final energy demand and CO2 emissions. The special focus is on Singapore’s passenger transportation and the integration of different kinds of electric vehicles (battery electric vehicles, fuel cell electric vehicles). In order to show the effects on final energy supply, the existing stock of power plants in Singapore is enhanced by alternative technologies of power generation (solar, coal and nuclear energy).
By this, the necessity of a holistic approach for the development of a sustainable energy system is shown.
KeywordsUrban energy modeling TIMES Renewable energies Electromobility CO2 emissions
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