Cloud computing provides unique opportunities for the various sectors of the economy such as the automotive industry, education, finance, and governmental entities. In-house IT infrastructures vary in quality and can often be outdated and ineffective. As an alternative, cloud computing is a popular IT model which allows various businesses to “outsource” the IT infrastructure to a more efficient model. Businesses are opting to use cloud computing in order to eliminate the capital expenses of traditional IT systems and reduce the around-the-clock expert manpower required to operate on-site IT infrastructures. Relying on cloud services gives businesses the flexibility to access a large amount of computing resources easily with minimal hardware and labor. Data centers as backbone of the cloud computing consist of different physical components such as servers, routers, switches, as well as electricity and cooling infrastructure. Data centers are the brick and mortar face of the cloud computing industry and their performance ensures the prosperity of the underlying business. Electricity is an essential resource to operate data centers and it is often the costliest expenditure. Furthermore, the power system’s reliability and security of energy supply directly translates to data center’s operation cost and quality of cloud services. Ironically, the data centers’ remarkable demand for electricity influences the power system’s reliability and security and on larger scale may affect the local prices of electricity where data centers are installed. This thesis highlights the interdependence between the cloud computing and the power system in planning and operation platforms and addresses the challenges and potential opportunities may infer for both parties in case of cooperation between cloud providers and power system operators. To address the planning platform, first a coordinated expansion planning for data centers presented which is focused on the economic aspects of the expansion planning of data centers in the data and electricity networks while ensuring the energy supply security. The presented coordination between the electricity and data networks ensures the adequacy and security of energy supply for the data centers. In the planning platform, a framework for expansion planning of battery energy storages in the distribution network proposed that captures the interaction among the data center operators and distribution system operators. The proposed framework minimizes the installation and operation cost of the distribution network while ensuring the security and reliability of the distribution network as well as the quality of service for the end users in the data center. As a result of coordination among data center operators and the distribution network operator, the expansion planning costs of the battery energy storage facilities would minimize in the distribution network. In the operation platform on broad scale, the interaction among the cloud providers in the could computing market as well as the interactions among the cloud providers as demand entities in the wholesale market addressed. As a result, the strategic behavior of the market participants (i.e., cloud providers) in the cloud computing market; as well as the market participants (i.e., cloud providers and generation companies) in the wholesale electricity market to maximize their payoffs presented. In operation platform on distribution level, the operation of distribution networks with volatile supply and controllable demand of data centers investigated. As a result an operation framework proposed for distribution system operator to determine the demand of distribution network in the day ahead market based on the foretasted day-ahead hourly and intra-hourly electricity prices. The proposed framework incorporates risk aversion by constraining the volatility of the expected operation cost through conditional value at risk (CVaR) assessment.
Electrical and Computer Engineering
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Vafamehr, Ali, "Operation and Planning of Data Centers in Electricity Networks" (2019). Electrical Engineering Theses and Dissertations. 27.