Digital Ecosystem for Better Management of Power Systems: An Application on Microgrids

Over the past decade, new concepts have emerged in the electricity field, including the Smart Grids, the Distributed Generation and the Microgrids (MGs). In this thesis, we will be mainly focusing on the study of the MGs. An MG is a small-scale power system, consisting of local power generation, local loads and energy storage systems. Thanks to their numerous economical, ecological and operational benefits, the MGs are expected to hold the promise of becoming a major ingredient in the implementation of the future power systems. However, there are several significant challenges to overcome in order to achieve its expected benefits, namely: the cyber-attacks, the mobility aspect, the interoperability, the non-cooperation, and the demand-side management. Three main contributions are developed. First, we present OntoMG, an ontology-based data model, capable of representing the heterogeneous components of the MG and their properties, while being compliant with existing models and information standards (i.e., IEC 61970 and IEC 61850) and coping with the interoperability issues and the multi-objective aspect of MG. Secondly, we introduce DECF, a cooperative model for the optimization of the electricity exchange in the MG, offering several advantages over existing approaches, in particular: 1) its generic in that it considers all heterogeneous components of MG, 2) it is a cooperative model that reduces the technical, ecological and economic costs and encourages the local power exchange, and 3) it is user-oriented in that it gives the user the possibility to fine-tune the weight of each objective aspect. Finally, we introduce MOCSF, a multi-objective cooperative scheduler designed to schedule the power production, consumption and storage in the MG, while taking into account the preferences of MG components. Illustrative examples are provided after each step to facilitate understanding of each module. Then, a number of simulations are made to show the effectiveness of our approaches to solve our challenges in relation to the existing approaches. (author)