The commercial viability of flexible energy systems relies on the formulation of specialized large-scale optimization models and the development of scalable strategies to obtain their solutions efficiently. My PhD. research focuses on building models and tools to simultaneously optimize decisions over multiple disparate timescales. I also have experience with Techno-economic optimization, data driven modeling and software development.
Key research projects include:
Modeling and solution strategies for multi-timescale optimization problems: How to make design and operational decisions over both fast operational dynamics and slow degradation? Problems with multiple timescale dynamics result in very large nonlinear programming problems. To solve these complex problems, we propose model reduction strategies including adaptive timescale coupling, event based triggers and apply decomposition techniques to solve the resulting large scale nonlinear programming problem.
Modeling of microstructure degradation and thermal stress evolution in reversible Solid-Oxide Cells (rSOC): In this work, a modeling framework was developed to assess the impact of operating conditions on the degradation and deformation of the Solid-Oxide Cell structure. The models are combined into the IDAES integrated modeling framework and can be applied in dynamic optimization, control and planning models for rSOC systems.
Techno-Economic optimization of flexible energy systems: In this study, a framework was developed to assess the economic viability of various configurations of integrated energy systems involving an NGCC power plant, post combustion carbon capture and a PEM electrolysis based H2 production system for application in fluctuating energy markets.
Hybrid first-principles and Machine Learning models: Here we investigate a superstructure optimization based approach to hybridizing first-principles and data-driven models for adaptive, real-time predictions. Substructures include series, parallel and nested implementations of the first-principles and machine learning models.
Recent Publications:
[1] Giridhar, N.V., Allan, D.A., Li, M., Zitney, S.E., Biegler, L.T. and Bhattacharyya, D., 2024. Optimal operation of solid-oxide electrolysis cells considering long-term chemical degradation. Energy Conversion and Management, 319, p.118950.
[2] Giridhar, N.V., Le, Q.M., Bhattacharyya, D., Allan, D.A., Liese, E. and Zitney, S.E., 2025. Stress evolution and creep deformation in solid-oxide electrolysis cell systems–Dynamic modeling and multi-objective optimization to maximize stack life and efficiency. Journal of Power Sources, 653, p.237687.
[3] Giridhar, N.V., Bhattacharyya, D., Allan, D.A., Zitney, S.E., Li, M., Biegler, L.T. Optimization of solid oxide electrolysis cell systems accounting for long-term performance and health degradation, Sys. Control Transac. (2024) 177040.