The International Energy Agency estimates that the number of people who do not have access to electricity is nearly 1.3 billion and a billion more have only unreliable and intermittent supply. Moreover, current supply for electricity generation mostly relies on fossil fuels, which are finite and one of the greatest threats to the environment. Rising population growth rates, depleting fuel sources, environmental issues and economic developments have increased the need for mathematical optimization to provide a formal framework that enables systematic and clear decision-making in energy operations. This thesis through its methodologies and algorithms enable tools for energy generation, transmission and distribution system design and help policy makers make cost assessments in energy infrastructure planning rapidly and accurately.
In Chapter 2, we focus on local-level power distribution systems planning for rural electrification using techniques from combinatorial optimization. We describe a heuristic algorithm that provides a quick solution for the partial electrification problem where the distribution network can only connect a pre-specified number of households with low voltage lines. The algorithm demonstrates the effect of household settlement patterns on the electrification cost. We also describe the first heuristic algorithm that selects the locations and service areas of transformers without requiring candidate solutions and simultaneously builds a two-level grid network in a green-field setting. The algorithms are applied to real world rural settings in Africa, where household locations digitized from satellite imagery are prescribed.
In Chapter 3 and 4, we focus on power generation and transmission using clean energy sources. Here, we imagine a country in the future where hydro and solar are the dominant sources and fossil fuels are only available in minimal form. We discuss the problem of modeling hydro and solar energy production and allocation, including long-term investments and storage, capturing the stochastic nature of hourly supply and demand data. We mathematically model two hybrid energy generation and allocation systems where time variability of energy sources and demand is balanced using the water stored in the reservoirs. In Chapter 3, we use conventional hydro power stations (incoming stream flows are stored in large dams and water release is deferred until it is needed) and in Chapter 4, we use pumped hydro stations (water is pumped from lower reservoir to upper reservoir during periods of low demand to be released for generation when demand is high). Aim of the models is to determine optimal sizing of infrastructure needed to match demand and supply in a most reliable and cost effective way.
An innovative contribution of this work is the establishment of a new perspective to energy modeling by including fine-grained sources of uncertainty such as stream flow and solar radiations in hourly level as well as spatial location of supply and demand and transmission network in national level. In addition, we compare the conventional and the pumped hydro power systems in terms of reliability and cost efficiency and quantitatively show the improvement provided by including pumped hydro storage. The model will be presented with a case study of India and helps to answer whether solar energy in addition to hydro power potential in Himalaya Mountains would be enough to meet growing electricity demand if fossil fuels could be almost completely phased out from electricity generation.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8JW8C2F |
| Date | January 2014 |
| Creators | Kocaman, Ayse |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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