Search Results for author: John W. Simpson-Porco

Found 9 papers, 0 papers with code

A Multi-Area Architecture for Real-Time Feedback-Based Optimization of Distribution Grids

no code implementations18 Jan 2024 Ilyas Farhat, Etinosa Ekomwenrenren, John W. Simpson-Porco, Evangelos Farantatos, Mahendra Patel, Aboutaleb Haddadi

A challenge in transmission-distribution coordination is how to quickly and reliably coordinate Distributed Energy Resources (DERs) across large multi-stakeholder Distribution Networks (DNs) to support the Transmission Network (TN), while ensuring operational constraints continue to be met within the DN.

Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC

no code implementations23 Dec 2023 RuiQi Li, John W. Simpson-Porco, Stephen L. Smith

We propose a data-driven receding-horizon control method dealing with the chance-constrained output-tracking problem of unknown stochastic linear time-invariant (LTI) systems with partial state observation.

Model Predictive Control

Closed-Loop Motion Planning for Differentially Flat Systems: A Time-Varying Optimization Framework

no code implementations19 Oct 2023 Tianqi Zheng, John W. Simpson-Porco, Enrique Mallada

The standard approach to combine these methodologies comprises an offline/open-loop stage, planning, that designs a feasible and safe trajectory to follow, and an online/closed-loop stage, tracking, that corrects for unmodeled dynamics and disturbances.

Motion Planning

Data-Driven Fast Frequency Control using Inverter-Based Resources

no code implementations11 Apr 2023 Etinosa Ekomwenrenren, John W. Simpson-Porco, Evangelos Farantatos, Mahendra Patel, Aboutaleb Haddadi, Lin Zhu

To address the control challenges associated with the increasing share of inverter-connected renewable energy resources, this paper proposes a direct data-driven approach for fast frequency control in the bulk power system.

Data-Driven Output Regulation using Single-Gain Tuning Regulators

no code implementations31 Mar 2023 Liangjie Chen, John W. Simpson-Porco

Current approaches to data-driven control are geared towards optimal performance, and often integrate aspects of machine learning and large-scale convex optimization, leading to complex implementations.

Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits

no code implementations18 Feb 2023 Babak Abdolmaleki, John W. Simpson-Porco, Gilbert Bergna-Diaz

Reactive power sharing and containment of voltages within limits for inverter-based resources (IBRs) are two important, yet coupled objectives in ac networks.

Distributed Optimization

A Fixed-Point Algorithm for the AC Power Flow Problem

no code implementations4 Oct 2022 Liangjie Chen, John W. Simpson-Porco

This paper presents an algorithm that solves the AC power flow problem for balanced, three-phase transmission systems at steady state.

Data-Driven Model Predictive Control for Linear Time-Periodic Systems

no code implementations30 Mar 2022 RuiQi Li, John W. Simpson-Porco, Stephen L. Smith

Robustness of the algorithm to noisy data is illustrated via simulation of a regularized version of the algorithm applied to a stochastic multi-input multi-output LTP system.

LEMMA Model Predictive Control

Low-Gain Stability of Projected Integral Control for Input-Constrained Discrete-Time Nonlinear Systems

no code implementations11 Mar 2021 John W. Simpson-Porco

We consider the problem of zeroing an error output of a nonlinear discrete-time system in the presence of constant exogenous disturbances, subject to hard convex constraints on the input signal.

Optimization and Control

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