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Browsing by Author "Gupta, Sarthak"

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    Machine Learning and Quantum Computing for Optimization Problems in Power Systems
    Gupta, Sarthak (Virginia Tech, 2023-01-26)
    While optimization problems are ubiquitous in all domains of engineering, they are of critical importance to power systems engineers. A safe and economical operation of the power systems entails solving many optimization problems such as security-constrained unit commitment, economic dispatch, optimal power flow, optimal planning, etc. Although traditional optimization solvers and software have been successful so far in solving these problems, there is a growing need to accelerate the solution process. This need arises on account of several aspects of grid modernization, such as distributed energy resources, renewable energy, smart inverters, batteries, etc, that increase the number of decision variables involved. Moreover, the technologies entail faster dynamics and unpredictability, further demanding a solution speedup. Yet another concern is the growing communication overhead that accompanies this large-scale, high-speed, decision-making process. This thesis explores three different directions to address such concerns. The first part of the thesis explores the learning-to-optimize paradigm whereby instead of solving the optimization problems, machine learning (ML) models such as deep neural networks (DNNs) are trained to predict the solution of the optimization problems. The second part of the thesis also employs deep learning, but in a different manner. DNNs are utilized to model the dynamics of IEEE 1547.8 standard-based local Volt/VAR control rules, and then leverage efficient deep learning libraries to solve the resulting optimization problem. The last part of the thesis dives into the evolving field of quantum computing and develops a general strategy for solving stochastic binary optimization problems using variational quantum eigensolvers (VQE).
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    Real-time Integration of Energy Storage
    Gupta, Sarthak (Virginia Tech, 2017-08-28)
    Increasing dynamics in power systems on account of renewable integration, electric vehicle penetration and rising demands have resulted in the exploration of energy storage for potential solutions. Recent technology- and industry-driven developments have led to a drastic decrease in costs of these storages, further advocating their usage. This thesis compiles the author's research on optimal integration of energy storage. Unpredictability is modelled using random variables favouring the need of stochastic optimization algorithms such as Lyapunov optimization and stochastic approximation. Moreover, consumer interactions in a competitive environment implore the need of topics from game theory. The concept of Nash equilibrium is introduced and methods to identify such equilibrium points are laid down. Utilizing these notions, two research contributions are made. Firstly, a strategy for controlling heterogeneous energy storage units operating at different timescales is put forth. They strategy is consequently employed optimally for arbitrage in an electricity market consisting of day-ahead and real-time pricing. Secondly, energy storages owned by consumers connected to different nodes of a power distribution grid are coordinated in a competitive market. A generalized Nash equilibrium problem is formulated for their participation in arbitrage and energy balancing, which is then solved using a novel emph{weighted} Lyapunov approach. In both cases, we design real-time algorithms with provable suboptimality guarantees in terms of the original centralized and equilibrium problems. The algorithms are tested on realistic scenarios comprising of actual data from electricity markets corroborating the analytical findings.