Abstract
Under the transition towards sustainable smart energy systems (SES), utilization of distributed intelligence has been gradually proposed along with the expansion of Information and Communication Technology (ICT) infrastructure and advanced control services. Distributed intelligence (DI)-based control and management solutions proved a perfect complement to the existing control structures to handle the SES’ uncertainty which is getting quite complex with different system layers and involved stakeholders. Advanced modelling and simulation techniques are crucial here to realize and enable the applications of DI to enhance grid reliability while optimize market operation. However, several challenges arise while modelling DI applications and integrating them in the simulation platform due to the complexity of the multi-disciplinary smart grids. As an activity of IEEE Task Force on Interfacing Techniques for Simulation Tools, this paper mainly reviews the interface issues between modelling and simulation of physical, ICT, and application layers, as well as business processes of the whole smart energy systems. By means of a conceptual framework for SES development, this paper aims to position most of DI-based control applications in specific research domain and elaborate on their interface with the whole SES context.
Highlights
DEVELOPMENT of smart grids, or Smart Energy System (SES) in a broader sense, is facing challenges related to uncertainty in both securing the electricity networks and balancing energy supply and demand [1,2]
By means of a conceptual framework for SES development, this paper aims to position most of Distributed intelligence (DI)-based control applications in specific research domain and elaborate on their interface with the whole SES context
Summarizing the content of this paper so far, we have seen that the application of DI has been expanded significantly in different application aspects to mitigate the uncertainty in SES
Summary
DEVELOPMENT of smart grids, or Smart Energy System (SES) in a broader sense, is facing challenges related to uncertainty in both securing the electricity networks and balancing energy supply and demand [1,2]. The anticipated massive integration of stochastic renewable energy sources (RES) and the introduction of new energyintensive appliances, e.g. electrical vehicles or heat pumps, aggravates these challenges because of larger uncertainties on all time scales [3]. Distributed intelligence (DI) has been considered, among other computation intelligence methods, as an enabler for bottom-up modelling and control solutions to handle the SES’ uncertainty which is getting quite complex with different system layers and involved stakeholders.
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