Penetration of advanced sensor systems such as advanced metering infrastructure (AMI), high-frequency overhead and underground current and voltage sensors have been increasing significantly in power distribution systems over the past few years. According to U.S. energy information administration (EIA), the aggregated AMI installation experienced a 17 times increase from 2007 to 2012. The AMI usually collects electricity usage data every 15 minute, instead of once a month. This is a 3,000 fold increase in the amount of data utilities would have processed in the past. It is estimated that the electricity usage data collected through AMI in the U.S. amount to well above 100 terabytes in 2012. To unleash full value of the complex data sets, innovative big data algorithms need to be developed to transform the way we operate and plan for the distribution system. This paper not only proposes promising applications but also provides an in-depth discussion of technical and regulatory challenges and risks of big data analytics in power distribution systems. In addition, a flexible system architecture design is proposed to handle heterogeneous big data analysis workloads.
AcknowledgementsFirst and foremost, I would like to thank my adviser Prof. Mani Chandy. He took a chance on me even though I was by no means qualified. In addition, there were moments along the way when things looked quite stark and Mani's support was invaluable in me getting through it. For this, I will always be in his debt. He is one of a handful of researchers I have met that manages to conduct research that has both academic merit and yet a large societal impact. Still, the most valuable thing I have learned from Mani is how to be a good person.
AbstractThe rapid rise in the residential photo voltaic (PV) adoptions in the past half decade has created a need in the electricity industry for a widely-accessible model that estimates PV adoption based on a combination of different business and policy decisions. This work analyzes historical adoption patterns and finds fiscal savings to be the single most important factor in PV adoption, with significantly greater predictive power compared to all other socioeconomic factors including income and education. We can create an application available on Google App Engine (GAE) based on our findings that allows all stakeholders including policymakers, power system researchers and regulators to study the complex and coupled relationship between PV adoption, utility economics and grid sustainability. The application allows users to experiment with different customer demographics, tier structures and subsidies, hence allowing them to tailor the application to the geographic region they are studying. This study then demonstrates the different type of analyses possible with the application by studying the relative impact of different policies regarding tier structures, fixed charges and PV prices on PV adoption. vi
D Distribution-system, interconnecteD, utilityscale photovoltaic (PV) systems (those 500 kW to 5 mW in size) have become commonplace in many utilities' service areas since the first few systems were installed in the 2010-2011 time frame. the integration of these systems within the operating framework of the traditional utility distribution system creates many challenges. the goal of these integration efforts is the safe, reliable, and cost-effective operation of the resulting distribution system-but with the added benefit of a considerable amount of PV-based distributed generation, resulting in lower carbon emissions and compliance with state energy policies and standards.unlike PV systems interconnected with transmission systems and their relevant integration studies, PV integration into the distribution system requires the consideration of a number of local distribution grid impacts. the range of potential impacts requires the incorporation of utility expertise across the entire distribution division of most utilities, including planning, operations,
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