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Tania Rifat Jahan

Publications and source records attributed to Tania Rifat Jahan.

2 recordsLinked to original sources

A Control Co-Design Framework to Optimize Sustainability with Application to Microgrid-Driven Data Centers

This work studies the optimization of physical plant characteristics and controller parameters for sustainability. Environmental sustainability is strongly correlated with the development and operation of energy systems, with data centers as the preeminent modern example. Data centers, and the grid technologies that provide their power, consume nonnegligible amounts of global energy and pose a risk to increase greenhouse gas (GHG) emissions and electronic waste. Addressing such issues requires improved plant design and control strategies, often approached through optimization-based methods. However, there are noticeable gaps regarding data center and microgrid design: (i) simultaneous optimization of plant and controller features is rarely explored, and (ii) sustainability criteria are not emphasized. This work addresses these gaps by establishing a generalized, sustainability-centric control co-design (CCD) framework for energy systems. Uniquely, the CCD framework defines and categorizes sustainability metrics into three lifecycle stages - manufacturing, operation, and disposal - supporting optimization and comparative analysis of the metrics for different design options. To exemplify its use, the CCD framework is applied to a microgrid-driven data center system, providing a family of sustainability metrics correlated to plant and controller parameters. The analysis enabled by the framework provides insights into the CCD of microgrids and data centers, such as that GHG-equivalent emissions from manufacturing of components can dwarf those generated during operation of the system. The system designs identified by the proposed framework show substantial improvements to environmental sustainability categories as compared to designs identified through baseline procedures.

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A Control Co-Design Framework to Achieve Solution Feasibility in Energy System Optimization Problems

This work explores methods to identify energy system designs for infeasible control co-design optimization problems. Control co-design, or CCD, has been recognized as a powerful tool to maximize energy system capabilities through simultaneous determination of plant and controller parameters. However, due to the inherent nonlinearities, complexity, and conflicting criteria of energy systems, CCD optimization problems are susceptible to infeasibility and can lack potential solutions. While transforming the optimization problem by relaxing constraints has been developed for optimal control infeasibility challenges, solution feasibility for CCD is relatively unexplored. This paper proposes a framework to convert infeasible optimization problems into solvable forms for a class of CCD problems. The framework introduces a procedure to rank metric bounds from least likely to most likely to cause infeasibility. This provides guidance to algorithmically relax a limited number of constraints, leaving others intact. The proposed framework is applied to a CCD problem for designing a battery within a microgrid. Comparison against a baseline approach for relaxing optimization problems shows the framework requires only a reduced number of iterations to determine a solution.

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