Open AccessDistributed model predictive based secondary control for economic production and frequency regulation of MG
Author(s) -
Mehmood Faisal,
Khan Bilal,
Ali Sahibzada M.,
Rossiter John Anthony
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6226
Subject(s) - control theory (sociology) , convergence (economics) , lyapunov function , node (physics) , mathematical optimization , microgrid , function (biology) , stability (learning theory) , exponential stability , computer science , economic dispatch , control (management) , automatic frequency control , optimal control , mathematics , engineering , electric power system , artificial intelligence , economics , telecommunications , power (physics) , physics , structural engineering , nonlinear system , quantum mechanics , evolutionary biology , machine learning , biology , economic growth
This work focuses on Distributed Secondary Control (DSC) technique, for frequency regulation and Economic Load Dispatch of Microgrid (MG). The fluctuating nature and large quantity of Distributed Energy Resources (DER) in an autonomous MG result in complex control requirements, demanding fast and robust response. The contemporary DSC schemes are mostly based on Distributed Averaging Integration techniques, with slow response. This paper proposes Distributed Model Predictive based Secondary Control (DMPSC) which effectively complies with the control requirements of MG. DMPSC requires each DER‐node to solve a local optimization problem with the cost function penalizing the deviation of states from their desired values and the differences between the assumed and predicted values. The desired‐states are based on local intermediate‐optimum values, computed using local and neighbouring information. Equality based terminal constraints are introduced to ensure the stability, where each node is forced to reach the desired‐state value at the end of prediction horizon. The terminal‐consensus of the network affirms convergence of the desired‐states to a global optimal point of the network. The asymptotic stability of the proposed control is proved by using the sum of local cost‐functions as a candidate Lyapunov function. Simulation results validate the effectiveness of the proposed control scheme.