Optimality Condition Decomposition Approach to Distributed Model Predictive Control

Yamé, Joseph Julien; Gabsi, Farah; Darure, Tejaswinee; Jain, Tushar; Hamelin, Frédéric; Sauer, Nathalie

doi:10.23919/acc.2019.8814374

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Therefore, this work adapts the method to solve DMPC problems. A review of the literature reveals that the integration of the OCD approach in a DMPC scheme is a novel approach: although some preliminary results are reported in [23], the proposed method is not demonstrated for a DMPC.…”

Section: Summary Of the Paper And Contributionmentioning

confidence: 99%

Distributed model predictive control using optimality condition decomposition and community detection

Segovia

Puig

Duviella

et al. 2021

Journal of Process Control

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Section: Summary Of the Paper And Contributionmentioning

confidence: 99%

Distributed model predictive control using optimality condition decomposition and community detection

Segovia

Puig

Duviella

et al. 2021

Journal of Process Control

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Decentralized approach for security enhancement of wind‐integrated energy systems coordinated with energy storages

Ebrahimi

Abapour

Mohammadi‐Ivatloo

et al. 2021

Intl J of Energy Research

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Summary Wind speed fluctuations in wind‐integrated energy systems render the need for the fast and proper response of the conventional thermal generating units. This is while ramping constraints of these units as the backbone of the traditional power generation cycle depreciate their response, which poses a serious security challenge. This article is aimed at enhancing the security of multiarea wind‐integrated energy systems by the coordinated deployment of energy storage systems (ESSs). To realize a practical model, a probabilistic reformulation of decentralized security‐constrained optimal power flow by taking into account the thermal units' ramping constraints is developed, which tackles the uncertainties of wind speed fluctuations through a proper provision of ramping requirements by ESSs. Moreover, the optimality condition decomposition algorithm is used to solve security‐constrained optimal power flow at each area of the energy system in a decentralized manner. Furthermore, to reduce the computational burden, a contingency filtering approach is employed that filters the uncritical conditions during the solution process. Different scenarios of transmission line and generator outages are explored by linear outage factors, including the line outage distribution factor and the power transfer distribution factor. In the established model, the value of loss of load and the value of wind curtailment factors are employed to reflect the economic impact of load shedding and wind energy curtailment. For supplying the lost power in generation outages, the participation factor of the connected sources is modified, which considers the remained generation capacity of sources. To assess the efficiency of the proposed model, the New England 39‐bus testbed is put under extensive investigation. The results are discussed in depth.

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Decomposition–Coordination of Double-Layer MPC for Constrained Systems

Wang

Zhang

Yang

et al. 2022

Entropy

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Large-scale industrial processes usually adopt centralized control and optimization methods. However, with the growth of the scale of industrial processes leading to increasing computational complexity, the online optimization capability of the double-layer model predictive control algorithm is challenged, exacerbating the difficulty of the widespread implementation of this algorithm in the industry. This paper proposes a distributed double-layer model predictive control algorithm based on dual decomposition for multivariate constrained systems to reduce the computational complexity of process control. Firstly, to solve the problem that the original dual decomposition method does not apply to constrained systems, two improved dual decomposition model prediction control methods are proposed: the dual decomposition method based on the quadratic programming in the subsystem and the dual decomposition method based on constraint zones, respectively. It is proved that the latter will certainly converge to the constraint boundaries with appropriate convergence factors for the controlled variables. The online optimization ability of the proposed two methods is compared in discussion and simulation, concluding that the dual decomposition method based on the constraint zones exhibits superior online optimization ability. Further, a distributed double-layer model predictive control algorithm with dual decomposition based on constraint zones is proposed. Different from the objective function of the original dual decomposition model predictive control, the proposed algorithm’s dynamic control-layer objective function simultaneously tracks the steady-state optimization values of the controlled and manipulated variables, giving the optimal solution formulation of the optimization problem consisting of this objective function and the constraints. The algorithm proposed in this paper achieves the control goals while significantly reducing the computational complexity and has research significance for promoting the industrial implementation of double-layer model predictive control.

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Optimality Condition Decomposition Approach to Distributed Model Predictive Control

Cited by 3 publications

References 11 publications

Distributed model predictive control using optimality condition decomposition and community detection

Distributed model predictive control using optimality condition decomposition and community detection

Decentralized approach for security enhancement of wind‐integrated energy systems coordinated with energy storages

Decomposition–Coordination of Double-Layer MPC for Constrained Systems

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