20 citations · 26 across the 20 of their papers we have counts for
12 papers · 1 filter
Gradient-Free Topology Adaptation for Power Flow Surrogates via In-Context Whitening
Ayushi Jolotia, Parikshit Pareek
Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a…
PACR: Parameter-Optimized AC Power Flow Restoration for AC Feasible DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy th…
Fast Diffusion with Physics-Correction for ACOPF
Shashank Shekhar, Abhinav Karn, Kris Keshav +2
Generating large-scale, physically consistent AC Optimal Power Flow (ACOPF) datasets is essential for modern data-driven power system applications. The central challenge lies in ba…
Towards AC Feasibility of DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
DC Optimal Power Flow (DCOPF) is widely utilized in power system operations due to its simplicity and computational efficiency. However, its lossless, reactive power-agnostic model…
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works
Bendong Tan, Tong Su, Yu Weng +6
The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern…
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
Daniel Glover, Parikshit Pareek, Deepjyoti Deka +1
Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based t…
Degradation-Infused Energy Portfolio Allocation Framework: Risk-Averse Fair Storage Participation
Parikshit Pareek, L. P. Mohasha Isuru Sampath, Anshuman Singh +3
This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service elec…
A Convergence Predictor Model for Consensus-based Decentralised Energy Markets
Parikshit Pareek, L. P. Mohasha Isuru Sampath, Hung D. Nguyen +1
This letter introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms. The CPM serves as a tool to detect potential cyber-attacks that affect the…
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
Parikshit Pareek, Sidhant Misra, Deepjyoti Deka
The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability…
Gaussian Process Learning-based Probabilistic Optimal Power Flow
Parikshit Pareek, Hung D. Nguyen
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary d…
Non-parametric Probabilistic Load Flow using Gaussian Process Learning
Parikshit Pareek, Chuan Wang, Hung D. Nguyen
In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncer…
Probabilistic Robust Small-Signal Stability Framework using Gaussian Process Learning
Parikshit Pareek, Hung D. Nguyen
While most power system small-signal stability assessments rely on the reduced Jacobian, which depends non-linearly on the states, uncertain operating points introduce nontrivial h…