Ana Radovanovic
Ana's research focuses on fundamental principles of operation, design and control of systems in which uncertainty is an inherent property and important assumption in the analysis and design. She is mainly interested in developing approaches that lead to explicit and insightful results, which highlight business tradeoffs and provide general design guidelines.
Ana has developed models, analysis and optimization approaches in different areas of technology: Web caching, stochastic service networks, revenue management and pricing in capacitated systems with reusable resources, job scheduling algorithms in large computer centers, online advertising. In 2013, she reached out to the opportunity to work in the area of energy systems, and has been passionate about energy efficient technical solutions that enable clean energy economy.
Authored Publications
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This paper provides a methodology for modeling and optimally managing the demand of an aggregator with deferrable (flexible) loads (e.g., electric vehicles and HVACs) under uncertainty. We propose a unified framework for treating different types of flexible loads, that captures uncertainties in their parameters, and environmental conditions they are exposed to. Our optimization formulation minimizes the total expected cost, whose goal is to optimally balance two terms: user discomfort cost (regret), and cost paid to the utility. The main contribution of the paper is in estimating the impact of uncertainty in temperature forecasts and load parameters on optimal program selection with utilities, and, consequently, optimal demand side management (DSM). We propose a cost-efficient procedure for risk estimation, and provide guidelines for its consideration in cost-effective program selection.
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When wholesale prices are high and/or when the electricity system reliability is jeopardized, utilities have programs that encourage their customers to shed loads; these programs are called demand response (DR). Most large energy consumers, for example the buildings comprising Google’s Mountain View, CA campus, have some ability to control, and therefore reduce, their demand, and in this way contribute to stabilizing the grid. In this paper, we present a novel risk-aware nomination strategy in DR programs where the capacity commitments are made month ahead. The proposed methodology gives special treatment to the variability in load response to DR signals, variability that stems from the uncertainty in month-ahead weather forecasts and the inherent unpredictability of load profiles. The framework incorporates an efficient procedure for statistically characterizing the drop in demand associated with thermostatically controlled, heating, ventilation and air conditioning (HVAC) loads in commercial buildings, as well as applying the derived models to nominate revenue-optimal bids. The methodology is reasonably generic and adaptable to different kinds of DR markets and customer demand profiles. By way of example, we work out the capacity nomination for the Capacity Bidding Program (CBP) offered by Pacific Gas and Electric Company (PG&E) that Google participated in with a fraction of its buildings.
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PowerNet for distributed Energy Resources
Anand Ramesh
Sangsun Kim
Jim Schmalzried
Jyoti Sastry
Michael Dikovsky {{+mdikovsky
Konstantin Bozhkov
Eduardo Pinheiro
Scott Collyer
Ankit Somani
Ram Rajagopal
Arun Majumdar
Junjie Qin
Gustavo Cezar
Juan Rivas
Abbas El Gamal
Dian Gruenich
Steven Chu
Sila Kiliccote
Conference: 2016 IEEE Power and Energy Society General Meeting (PESGM), IEEE Power and Energy Society, Boston, MA, USA (2016)
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We propose Powernet as an end-to-end open source technology for economically efficient, scalable and secure coordination of grid resources. It offers integrated hardware and software solutions that are judiciously divided between local embedded sensing, computing and control, which are networked with cloud-based high-level coordination for real-time optimal operations of not only centralized but also millions of distributed resources of various types. Our goal is to enable penetration of 50% or higher of intermittent renewables while minimizing the cost and address security and economical scalability challenges. In this paper we describe the basic concept behind Powernet and illustrate some components of the solution.
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Accuracy at the Top
Stephen Boyd
NIPS: Neural Information Processing Systems Foundation (2012)
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We introduce a new notion of classification accuracy based on the top τ -quantile
values of a scoring function, a relevant criterion in a number of problems arising
for search engines. We define an algorithm optimizing a convex surrogate of the
corresponding loss, and show how its solution can be obtained by solving a set
of convex optimization problems. We also present margin-based guarantees for
this algorithm based on the top τ -quantile of the scores of the functions in the
hypothesis set. Finally, we report the results of several experiments in the bipartite setting evaluating the performance of our algorithm and comparing the results to several other algorithms seeking high precision at the top. In most examples, our algorithm achieves a better performance in precision at the top.
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Revenue Maximization in Reservation-based Online Advertising Through Dynamic Inventory Management
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Assaf Zeevi
48th Annual Allerton Conference on Communication, Control and Computing (2010), pp. 1502-1509
Asymptotic Performance of the Non-Forced Idle Time Scheduling Policies in the Presence of Variable Demand for Resources
Cliff Stein
Proceedings of the 46th Annual Conference on Communication, Control, and Computing (2008), pp. 499-503
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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4797599&tag=1
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