arXiv subjects

Xiaonan Lu

Publications and source records attributed to Xiaonan Lu.

21 records · Page 2Linked to original sources

arXiv2022

Sparse and Safe Frequency Regulation for Inverter Intensive Microgrids

This paper developed a novel control approach for the sparse and safe frequency regulation for inverter intensive microgrids (MGs). In the scenario, the inverters and external grids are expected to reach a synchronized desired frequency under regulations. To this end, the active power set-point acting as a control from a high-level controller is designed while considering two important performance metrics "sparsity" and "safety", which are to reduce the information exchange between controllers and ensure that the frequency keeps in safety regions during the whole operation process. Our proposed control design framework allows the sparse linear feedback controller (SLFC) to be unified with a family of conditions for safe control using control barrier functions. A quadratic programming (QP) problem is then constructed, and the real-time control policy is obtained by solving the QP problem. Importantly, we also found that the real-time control for each inverter depends on the cross-layer communication network topology which is the union of the one between controllers from SLFC and the one determined by the power flow network. Furthermore, our approach has been validated through extensive numerical simulations.

eess.SY↗

arXiv2021

Optimal Power Flow for DC Networks with Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and operational constraint satisfaction are of concern. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. In contrast, this paper studies a DN-OPF formulation which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator setpoints are obtained by optimizing over the proposed convex stability set. The validity and value of the proposed algorithm are demonstrated through various DC networks adapted from IEEE test cases.

math.OC↗

arXiv2020

Robust Adaptive Model Predictive Control: Performance and Parameter Estimation

For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control algorithm incorporating online model adaptation is proposed. Sets of model parameters are identified online and employed in a robust tube MPC strategy with a nominal cost. The algorithm is shown to be recursively feasible and input-to-state stable. Computational tractability is ensured by using polytopic sets of fixed complexity to bound parameter sets and predicted states. Convex conditions for persistence of excitation are derived and are related to probabilistic rates of convergence and asymptotic bounds on parameter set estimates. We discuss how to balance conflicting requirements on control signals for achieving good tracking performance and parameter set estimate accuracy. Conditions for convergence of the estimated parameter set are discussed for the case of fixed complexity parameter set estimates, inexact disturbance bounds and noisy measurements.

math.OC↗