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Abstract
Future lowcarbon societies will be driven by renewable energy sources (e.g. wind and solar power). This will flip the characteristics of our power system from a productiontrackingconsumption paradigm, to a consumptiontrackingproduction paradigm. This will increase the need of complex coordination of our power consumption as power grids require a strict balancing between power production and consumption. This dissertation investigates the potential of applying nonlinear model predictive control algorithms to solve complex power market coordination problems, where flexible consumers leverage the more volatile balancing power prices and thereby indirectly help neutralizing production and consumption imbalances. This dissertation only considers continuousdiscrete stochastic models. Paper A provides a tutorial on how to formulate the entire algorithmstack of nonlinear model predictive control algorithms where system dynamics are governed by stochastic differential equations with discretetime observations. The techniques introduced in Paper A are applied in four casestudies relating to energy systems. Paper F introduces a new filtering technique that generalizes the observational model to contain general likelihood models. Paper B and Paper C propose an optimal control problem to operate the aeration equipment at wastewater treatment plants with the criteria to minimize the operational costs and the accumulated nutrient concentrations of the discharged effluent. Paper D considers the operation of a noninvasive icetank module added to a small retail refrigeration system located at Danfoss’ testfacility in Nordborg, Denmark. It is shown that the icetank is an efficient method for curtailing the power consumption of the refrigeration system, without rearranging and modifying the entire piping and general hardware infrastructure. Paper E presents an optimization technique to optimally leverage
the volatile power prices observed in the Northern European regulating power market using a Vanadium redoxflow battery. This
paper shows that the payback time of investing in gridscale flowbatteries are approximately seven years, when implementing this
optimizationbased trading strategy.
the volatile power prices observed in the Northern European regulating power market using a Vanadium redoxflow battery. This
paper shows that the payback time of investing in gridscale flowbatteries are approximately seven years, when implementing this
optimizationbased trading strategy.
Original language  English 

Publisher  Technical University of Denmark 

Number of pages  117 
Publication status  Published  2021 
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Dive into the research topics of 'Stochastic Control Theory Optimization of Energy Systems'. Together they form a unique fingerprint.Projects
 1 Finished

Stochastic Dynamic Optimization and Control Theory
Brok, N. B., Fleten, S., Knudsen, T., Madsen, H., Jørgensen, J. B., Poulsen, N. K., Thygesen, U. H. & Sørensen, M. P.
Technical University of Denmark
01/09/2017 → 18/08/2021
Project: PhD