Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation

Nouha Dkhili, David Salas, Julien Eynard, Stéphane Thil and Stéphane Grieu
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Nouha Dkhili: PROMES-CNRS (UPR 8521), Université de Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
David Salas: Instituto de Ciencias de la Ingeniería, Universidad de O’Higgins, O’Higgins 2841935, Chile
Julien Eynard: PROMES-CNRS (UPR 8521), Université de Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Stéphane Thil: PROMES-CNRS (UPR 8521), Université de Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Stéphane Grieu: PROMES-CNRS (UPR 8521), Université de Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France

Energies, 2021, vol. 14, issue 6, 1-28

Abstract: In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems’ operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme’s sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.

Keywords: low-voltage power distribution grids; smart grid paradigm; distributed generation; model-based predictive control; flexible asset management; mixed-integer nonlinear programming; relaxation; computational complexity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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