Multi-vector microgrids that utilis

Multi-vector microgrids that utilise several forms of energy storage are becoming popular in smart grid topologies due to their ability to cope with problems induced in the power network from the usage of distributedgeneration. While these microgrids appear to be pivotal in future energy systems, they impose several problemsin the design and operation of the network mainly due to their complexity and the different properties that eachenergy subsystem has. In this work, we propose a novel, generic and systematic way of modelling the assets in amicrogrid including the energy management method that is used to control the operation of these assets undermultiple stochastic loads. This gives a unique tool that allows the testing/derivation of multiple energy management methods including demand side response and the usage of forecasting tools to optimise the performanceof the system. A thorough study of the proposed method, using data from a real hybrid energy system (built inGreece), proves that the performance and efficiency of the system can be significantly improved while at thesame time the requirement for external power supply or the consumption of fossil fuels can be reduced. The mainconcept is based on a state space modelling approach that transforms the power network into a hybrid dynamicalsystem and the implemented energy management method into the evolution operator. The model integratesstructural, temporal and logical features of smart grid systems in order to identify and construct multiple different energy management strategies EMS which can then be compared with respect to their ability to best servethe considered demands. Other than coping with several energy carriers, the model inherently accounts forforecasting, handles multiple random loads with time dependant importance and supports the use of demandside response strategies. Conclusions drawn from numerical case studies are used to demonstrate the advantagesof the proposed method. In this work we clearly show that by using 20 different energy management methodsand analysing their performance through a multi-criteria assessment approach we obtain non-trivial energymanagement approaches to support the operation of a multi-vector smart-grid considering variable externaldemands.