20 citations · 26 across the 20 of their papers we have counts for
25 papers
Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
Jitin Singla, Parikshit Pareek, Pratik Jawanpuria +1
Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based optimization algorithms. Ex…
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
Anuj Pal, Raunak Kumar, Dhruvi Solanki +3
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classi…
Conditions for Quantum Advantage in AC Power Flow
Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin +1
This paper aims to contextualize the requirements for Quantum Computing (QC) algorithms to achieve a quantum advantage in solving the alternating current power flow (ACPF) problem,…
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…
Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
Dhruvi Khandelwal, Anurag Basistha, Ayushi Jolotia +1
Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought expe…
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…
Small LLMs with Expert Blocks Are Good Enough for Hyperparamter Tuning
Om Naphade, Saksham Bansal, Parikshit Pareek
Hyper-parameter Tuning (HPT) is a necessary step in machine learning (ML) pipelines but becomes computationally expensive and opaque with larger models. Recently, Large Language Mo…
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…
Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach
Parikshit Pareek, Abhijith Jayakumar, Kaarthik Sundar +2
Constrained optimization problems arise in various engineering systems such as inventory management and power grids. Standard deep neural network (DNN) based machine learning proxi…
Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow
Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin +1
Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical quantum advantage can be sai…
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
Parikshit Pareek, Deepjyoti Deka, Sidhant Misra
This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a networ…
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…
Health-Focused Optimal Power Flow
Logesh Kumar, Parikshit Pareek, Sivakumar Nadarajan +3
In this paper, we propose a novel Health-Focused Optimal Power Flow (HF-OPF) to take into account the equipment health in operational and physical constraints. The health condition…
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…
A Convexification Approach for Small-Signal Stability Constrained Optimal Power Flow
Parikshit Pareek, Hung D. Nguyen
In this paper, a novel convexification approach for Small-Signal Stability Constraint Optimal Power Flow (SSSC-OPF) has been presented that does not rely on eigenvalue analysis. Th…
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…
A Sufficient Condition for Small-Signal Stability and Construction of Robust Stability Region
Parikshit Pareek, Konstantin Turitsyn, Krishnamurthy Dvijotham +1
The small-signal stability is an integral part of the power system security analysis. The introduction of renewable source related uncertainties is making the stability assessment…
Computationally Efficient Day-Ahead OPF using Post-Optimal Analysis with Renewable and Load Uncertainties
Parikshit Pareek, Ashu Verma
This paper presents a method to handle renewable source and load uncertainties in Dynamic Day-ahead Optimal Power Flow (DA-OPF) using post-optimal analysis of linear programming pr…