Browsing by Keyword "Microgrids"
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Item Development and Initial testing of a Virtual Laboratory for the Build-up and Testing of Microgrid Management Algorithms(2022-09) Fernández, G.; Menéndez, A.; Meneses, P.; Zubiria, A.; García, A.; Díez, F.; Jimeno, J.; Rodríguez-Seco, J.E.; Cortés, F.; POWER SYSTEMS; Tecnalia Research & InnovationIn a bid of facilitating the increasing penetration of intermittent and random renewable energies, microgrids along with their management algorithms are becoming crucial assets. To prove their effectiveness, these algorithms need to be tested in real environments and/or laboratories, which can be very difficult in many cases, especially at the initial development stages. To solve this issue, this article proposes the use of a laboratory digital twin, i.e., a virtual laboratory with a behaviour that is similar to that of real installations, aimed at facilitating the development, testing and debugging of microgrids management algorithms. The proposed solution is demonstrated to be safe and complete when it comes to test these algorithms.Item Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm(2018) Jiménez-Fernández, Silvia; Camacho-Gómez, Carlos; Mallol-Poyato, Ricardo; Fernández, Juan; Del Ser, Javier; Portilla-Figueras, Antonio; Salcedo-Sanz, Sancho; IAn this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.Item PowerDynamics.jl—An experimentally validated open-source package for the dynamical analysis of power grids(2022-01) Plietzsch, Anton; Kogler, Raphael; Auer, Sabine; Merino, Julia; Gil-de-Muro, Asier; Liße, Jan; Vogel, Christina; Hellmann, Frank; Tecnalia Research & Innovation; POWER ELECTRONICS AND SYSTEM EQUIPMENTPowerDynamics.jl is a Julia package for time-domain modeling of power grids that is specifically designed for the stability analysis of systems with high shares of renewable energies. It makes use of Julia’s state-of-the-art differential equation solvers and is highly performant even for systems with a large number of components. Further, it is compatible with Julia’s machine learning libraries and allows for the utilization of these methods for dynamical optimization and parameter fitting. The package comes with a number of predefined models for synchronous machines, transmission lines and inverter systems. However, the strict open-source approach and a macro-based user-interface also allows for an easy implementation of custom-built models which makes it especially interesting for the design and testing of new control strategies for distributed generation units. This paper presents how the modeling concept, implemented component models and fault scenarios have been experimentally tested against measurements in the microgrid lab of TECNALIA.