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- All Subjects: Market Analysis
- Creators: Bacon, David
Description
Electric power systems are facing great challenges from environmental regulations, changes in demand due to new technologies like electric vehicle, as well as the integration of various renewable energy sources. These factors taken together require the development of new tools to help make policy and investment decisions for the future power grid. The requirements of a network equivalent to be used in such planning tools are very different from those assumed in the development of traditional equivalencing procedures. This dissertation is focused on the development, implementation and verification of two network equivalencing approaches on large power systems, such as the Eastern Interconnection. Traditional Ward-type equivalences are a class of equivalencing approaches but this class has some significant drawbacks. It is well known that Ward-type equivalents "smear" the injections of external generators over a large number of boundary buses. For newer long-term investment applications that take into account such things as greenhouse gas (GHG) regulations and generator availability, it is computationally impractical to model fractions of generators located at many buses. A modified-Ward equivalent is proposed to address this limitation such that the external generators are moved wholesale to some internal buses based on electrical distance. This proposed equivalencing procedure is designed so that the retained-line power flows in the equivalent match those in the unreduced (full) model exactly. During the reduction process, accommodations for special system elements are addressed, including static VAr compensators (SVCs), high voltage dc (HVDC) transmission lines, and phase angle regulators. Another network equivalencing approach based on the dc power flow assumptions and the power transfer distribution factors (PTDFs) is proposed. This method, rather than eliminate buses via Gauss-reduction, aggregates buses on a zonal basis. The bus aggregation approach proposed here is superior to the existing bus aggregation methods in that a) under the base case, the equivalent-system inter-zonal power flows exactly match those calculated using the full-network-model b) as the operating conditions change, errors in line flows are reduced using the proposed bus clustering algorithm c) this method is computationally more efficient than other bus aggregation methods proposed heretofore. A critical step in achieving accuracy with a bus aggregation approach is selecting which buses to cluster together and how many clusters are needed. Clustering in this context refers to the process of partitioning a network into subsets of buses. An efficient network clustering method is proposed based on the PTDFs and the data mining techniques. This method is applied to the EI topology using the "Saguaro" supercomputer at ASU, a resource with sufficient memory and computational capability for handling this 60,000-bus and 80,000-branch system. The network equivalents generated by the proposed approaches are verified and tested for different operating conditions and promising results have been observed.
ContributorsShi, Di (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
For this thesis, the authors would like to create a hypothetical Private Equity Real Estate Investment firm that focuses on creating value for partners by taking an opportunistic approach to acquiring under-performing urban multi-family properties with large upside potential for investing. The project will focus on both the market analysis and financial modeling associated with investment strategy and transactions. There is a substantial amount of complexity within commercial real estate and this thesis seeks to offer an accurate and comprehensive documentary of the process, while simplifying it for everyday readers. Additionally, there are a significant amount of risk factors associated with investment decisions, so the best practices from the industry documented in this manuscript are valuable tools for successful investing in the future. To gain the most profound and reliable industry knowledge, the authors leveraged the experience of dozens of industry professionals through research and personal interviews. Through careful analysis, the authors were able to ascertain the current economic position in the real estate cycle and to create a plan for future investing. Additionally, they were able to identify and evaluate a specific asset for purchase. As a result, the authors found that multifamily properties are a sound investment for the next two years and that the company should slowly start to shift directions to office and retail in 2018.
ContributorsBacon, David (Co-author) / Soto, Justin (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05