Thesis supervisor: Attila Magyar
Location of studies (in Hungarian): University of Pannonia ,H-8200 Veszprém Egyetem u. 10. Abbreviation of location of studies: PE
Description of the research topic:
Smart grids seem to be a gool solution for the growing energy problems as well as for the outdates power transmission network. In a smart grid, consumers, service provider, market, household powerplants, power storage units and central dispatch center are connected via communication lines so that it is possible to synchronize the energy demand and the production in an optimal way taking the actual market circumstances into account [1].
The aim of the research is the modeling and parameter estimation of electrochemical storage elements applicable for renevable sources, moreover the use of them in an energy market environment. An important question in such an application is the optimal scheduling of the storage (charge) and sell (discharge) of such elements if the primary aim is the operation stability, or the maximal profit. [2]
The applied methodology depends on the available network model. If it is possible to describe the behavior of the system with linear models, probably MILP approach can be used. On the other hand, if the stochastic elements of the system does not enable it, the use of artificial intelligence based learning algorithms can be a good choice.
The preliminaries of the research topic can be found in the following papers of the supervispors
[1] Göllei A, Görbe P and Magyar A (2012), "Modeling and optimization of electrical vehicle batteries in complex clean energy systems", Journal of Cleaner Production., 2012. Vol. 34, pp. 138-145. Elsevier.
[2] Görbe P, Magyar A and Hangos KM (2011), "Low voltage grid optimization with power injection of renewable sources and EV batteries", Chemical Engineering Transactions. Vol. 25, pp. 893-898.
[3] Pózna AI, Hangos KM and Magyar A (2018), "Design of Experiments for Battery Aging Estimation", IFAC-PapersOnLine. Vol. 51(28), pp. 386-391. Elsevier.
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