000267843 001__ 267843
000267843 005__ 20190812204805.0
000267843 037__ $$aCONF
000267843 245__ $$aPerformance Assessment of Linearized OPF-based Distributed Real-time Predictive Control
000267843 260__ $$c2019-06-25
000267843 269__ $$a2019-06-25
000267843 336__ $$aConference Papers
000267843 520__ $$aWe consider the problem of controlling heterogeneous controllable resources of a distribution network with the objective of achieving a certain power flow at the grid connection point while respecting local grid constraints. The problem is formulated as a model predictive control (MPC), where a linearized grid model, to retain convexity, based on sensitivity coefficients (SCs) is used to model the grid constraints. We consider and compare the modelling performance of three different update policies for the SCs: when they are updated once per day considering static injections, updated once per day considering point prediction of the nodal injections, and recursively estimated using on-line measurements. Simulations are performed considering the CIGRÉCIGR´CIGRÉ low voltage benchmark network. Performance is evaluated in terms of convergence speed, tracking error, and constraints modeling errors. Further, we perform a sensitivity analysis on the dominant model w.r.t. the length of the predictive horizon and number of controllable units.
000267843 542__ $$fCC BY-SA
000267843 6531_ $$aDistributed control, energy storage, photovoltaic(PV), linear optimal power flow, sensitivity coefficients.
000267843 700__ $$g268765$$aGupta, Rahul Kumar$$0260528
000267843 700__ $$aSossan, Fabrizio
000267843 700__ $$aPaolone, Mario
000267843 7112_ $$dJune 2019$$cMilan Italy$$aIEEE PES PowerTech
000267843 8560_ $$frahul.gupta@epfl.ch
000267843 8564_ $$uhttps://infoscience.epfl.ch/record/267843/files/Performance%20Assessment%20of%20Linearized%20OPF-based%20Distributed%20Real-time%20Predictive%20Control.pdf$$zPREPRINT$$s580094
000267843 909C0 $$pDESL$$mdarius.farman@epfl.ch$$0252423$$zMarselli, Béatrice$$xU12494
000267843 909CO $$pconf$$pSTI$$ooai:infoscience.epfl.ch:267843
000267843 960__ $$arahul.gupta@epfl.ch
000267843 961__ $$afantin.reichler@epfl.ch
000267843 973__ $$aEPFL$$rREVIEWED
000267843 980__ $$aCONF
000267843 981__ $$aoverwrite