Browsing by Author "Simpson-Porco, John W."

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Data-Driven Predictive Control: Equivalence to Model Predictive Control Beyond Deterministic Linear Time-Invariant Systems
(University of Waterloo, 2025-02-07) Li, Ruiqi; Smith, Stephen L.; Simpson-Porco, John W.
In recent years, data-driven predictive control (DDPC) has emerged as an active research area, with well-known methods such as Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC) being validated through reliable experimental results. On the theoretical side, it has been established that both DeePC and SPC methods can generate equivalent control actions as one can obtain from Model Predictive Control (MPC), for deterministic linear time-invariant (LTI) systems. However, similar results do not yet exist for the application of DDPC beyond deterministic LTI systems. Therefore, the objective of our research is to generalize this theoretical equivalence between model-based and data-driven methods for more general classes of control systems. In this thesis, we present our contributions to DDPC for linear time-varying (LTV) systems and stochastic LTI systems. In our first piece of work, we developed Periodic DeePC (P-DeePC) and Periodic SPC (P-SPC) methods, which generalize DeePC and SPC from LTI systems to linear time-periodic (LTP) systems, as a special case of LTV systems. Theoretically, we demonstrate that our P-DeePC and P-SPC methods have equivalence control actions as produced from MPC for deterministic LTP systems, under appropriate tuning conditions. As an intermediate step in our theoretical development, we extended certain aspects of behavioral systems theory from LTI systems to LTP/LTV systems. This includes extending Willems’ fundamental lemma to LTP systems and the defining the concepts of order and lag for LTV systems. In our second piece of work, we proposed a control framework for stochastic LTI systems, namely Stochastic Data-Driven Predictive Control (SDDPC). Our SDDPC method theoretically achieves equivalent control performance to model-based Stochastic MPC, under idealized conditions of appropriate tuning and noise-free offline data. This method, which applies to general linear stochastic state-space systems, serves as an alternative to the data-driven method previously proposed by Pan et al., which also achieved theoretical equivalence to Stochastic MPC but was limited to a narrower class of systems. Beyond the theoretical assumption of noise-free offline data, we performed our SDDPC method in simulations with practical noisy offline data. The simulation results demonstrated that our SDDPC method outperforms benchmark methods, achieving lower cumulative tracking cost and lower rate and amount of constraint violation.
Microgrid Stability Definitions, Analysis, and Examples
(Institute of Electrical and Electronics Engineers (IEEE), 2019-06-28) Farrokhabadi, Mostafa; Canizares, Claudio A.; Simpson-Porco, John W.; Nasr, Ehsan; Fan, Lingling; Mendoza-Araya, Patricio A.; Tonkoski, Reinaldo; Tamrakar, Ujjwol; Hatziargyriou, Nikos; Lagos, Dimitris; Wies, Richard W.; Paolone, Mario; Liserre, Marco; Meegahapola, Lasantha; Kabalan, Mahmoud; Hajimiragha, Amir H.; Peralta, Dario; Elizondo, Marcelo A.; Schneider, Kevin P.; Tuffner, Francis K.; Reilly, Jim
This document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid Stability Definitions, Analysis, and Modeling, IEEE Power and Energy Society, Piscataway, NJ, USA, Tech. Rep. PES-TR66, Apr. 2018, which defines concepts and identifies relevant issues related to stability in microgrids. In this paper, definitions and classification of microgrid stability are presented and discussed, considering pertinent microgrid features such as voltage-frequency dependence, unbalancing, low inertia, and generation intermittency. A few examples are also presented, highlighting some of the stability classes defined in this paper. Further examples, along with discussions on microgrid components modeling and stability analysis tools can be found in the TF report.
Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility
(Elsevier, 2021-03-05) Restrepo, Mauricio; Cañizares, Claudio A.; Simpson-Porco, John W.; Su, Peter; Taruc, John
This paper presents the development, implementation, and commissioning of two different Energy Management Systems (EMSs) for the Canadian Renewable Energy Laboratory (CANREL), a microgrid testbed located in Guelph, ON, Canada, for the existing hardware, software, and communication infrastructure, which constrained the implementation options. A Rule-based EMS (RBEMS), which is typically found in microgrid controllers nowadays, and an implementation of an Optimization-based EMS (OBEMS), which is not usual in today’s controllers, are proposed, tested, and demonstrated in the microgrid testbed. The RBEMS consists of a state machine that represents the commitment of different genset units in the system and the curtailment of load and renewable generation. The OBEMS is based on a unit commitment model for microgrids that minimizes the generation and curtailment costs, while operating the microgrid equipment according to technical limits. Both EMS systems are integrated into a Python application which integrates various open-source packages and solvers, making it affordable, flexible and easy to replicate and upgrade. The successful implementation and performance of the EMS is discussed, showing that the components of the microgrid follow the dispatch commands, with the OBEMS yielding better overall results than the RBEMS, as expected, using the existing communications links and maintaining the stability of the microgrid.
Regulation Signal Design and Fast Frequency Control With Energy Storage Systems
(Institute of Electrical and Electronics Engineers (IEEE), 2021-06-02) Guzman E., Noela Sofia; Arriaga, Mariano; Cañizares, Claudio A.; Simpson-Porco, John W.; Sohm, Daniel; Bhattacharya, Kankar
This paper presents a novel H2 filter design procedure to optimally split the Frequency Regulation (FR) signal between conventional and fast regulating Energy Storage System (ESS) assets, considering typical Communication Delays (CDs). The filter is then integrated into a previously validated FR model of the Ontario Power System (OPS) including Battery and Flywheel ESSs, which is used to analyze the impact of these ESSs, CDs, and limited regulation capacity in the FR process in a real system. The proposed methodology to split the FR signal is also compared with the existing FR process, with the results showing that the proposed H2 filter design and signal splitting strategy can improve the FR process performance significantly, in terms of reducing the Area Control Error (ACE) signal, and thus reduce the need for regulation capacity.