000230344 001__ 230344
000230344 005__ 20190619023717.0
000230344 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6778-1
000230344 02471 $$2nebis$$a11000580
000230344 0247_ $$a10.5075/epfl-thesis-6778$$2doi
000230344 037__ $$aTHESIS
000230344 041__ $$aeng
000230344 088__ $$a6778
000230344 245__ $$aModel Predictive Control Strategies for Polygeneration systems and microgrids
000230344 269__ $$a2017
000230344 260__ $$bEPFL
000230344 260__ $$c2017
000230344 300__ $$a175
000230344 336__ $$aTheses
000230344 502__ $$aDr Jan Van Herle (président) ; Prof. François Maréchal, Prof. Mario Paolone (directeurs) ; Dr Rachid Cherkaoui, Prof. Brian Elmegaard, Prof. Pierluigi  Mancarella (rapporteurs)
000230344 520__ $$aIncreasing electricity and thermal demand in all sectors, an increasing focus on the reduction in carbon emissions and use of nuclear power, advent of distributed generation and greater use of renewable technologies on an aging electrical and thermal grid system has necessitated the need for modern control and management systems. These new control and management systems need to be able to integrate new technologies, stochasticities and maximise the utilisation of the existing infrastructure while satisfying demands, without requiring complete overhaul of the pre-existing centralised grid system and the transmission and distribution systems. A model predictive control system has been proposed and demonstrated here which is able to create strategies for thermal and electrical systems such that the grid efficiency and security is maintained while minimising resource usage and emissions, while, simultaneously reducing the operating costs in the grid.  The model predictive control(MPC) utilises a fully energetic approach for low-voltage microgrids and houses in the residential and commercial sector which comprises of CHP units, heat pumps, storage systems(electric and thermal) and stochastic renewable resources, while accounting for the varying dynamics of the electrical and thermal systems. Finally, validation of the MPC is performed on a testbed with physical units and building emulators which have access to meteorological and resource market data. The capability of the MPC to provide strategies for systems with photovoltaics (PV), heat pumps and CHP units is demonstrated.  The MPC implementation developed is input into an optimal system design algorithm based on a multi-objective optimisation genetic algorithm developed for microgrids and urban systems/grids with end-users and polygeneration systems and storage devices. The optimal design of the system is so that the optimal sizes of the polygeneration systems can be identified. This will help in maximising the utilisation of heat pumps, storage devices and other systems in a LV microgrid equipped with an MPC-based thermo-electric energy management system. The work also aims to compare the cost effectiveness versus ability of thermal storage devices compared to electrical storage devices for the same grid in question.
000230344 592__ $$bseptembre-2017
000230344 6531_ $$aSmart energy systems
000230344 6531_ $$aUrban system
000230344 6531_ $$aPolygeneration
000230344 6531_ $$aDemand Response
000230344 6531_ $$aModel Predictive Control
000230344 6531_ $$aDistributed Generation
000230344 6531_ $$aMicrogrid
000230344 700__ $$g207247$$aMenon, Ramanunni Parakkal$$0245225
000230344 720_2 $$e"dir."$$aMaréchal, François
000230344 720_2 $$e"dir."$$aPaolone, Mario
000230344 8560_ $$fsimon.marechal@epfl.ch
000230344 8564_ $$uhttps://infoscience.epfl.ch/record/230344/files/EPFL_TH6778.pdf$$zn/a$$s10877031$$yn/a
000230344 909C0 $$xU10315$$0252044$$pLENI
000230344 909C0 $$pIPESE$$xU12691$$0252481
000230344 909CO $$ooai:infoscience.tind.io:230344$$qGLOBAL_SET$$pSTI$$pthesis$$pthesis-bn2018$$pthesis-public$$pDOI$$qDOI2
000230344 917Z8 $$x108898
000230344 917Z8 $$x108898
000230344 917Z8 $$x108898
000230344 918__ $$dEDEY$$cIGM$$aSTI
000230344 919__ $$aLENI
000230344 920__ $$b2017$$a2017-9-4
000230344 970__ $$a6778/THESES
000230344 973__ $$sPUBLISHED$$aEPFL
000230344 980__ $$aTHESIS_LIB