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One can define an energy system as a system that converts one or more energy fluxes into other energy fluxes of a different kind. This definition may describe a relatively small system, for instance, a power plant, a chemical plant, or the heating and cooling apparatus of a single-family house, as well as one covering larger energy needs, for example, those of a city, a country, or even a continent. As energy systems are developed through the centuries, the way we structure these systems goes through changes affected by contextual conditions. Recently, concerns about the availability of traditional fossil energy sources and their environmental effects are revolutionizing the way energy systems are planned, designed, and operated.

This paper presents a case study oftwo approaches for optimal microgrid control. The simulations are based on a real test site located in Sweden. One of the two presented models replicates the actual implemented rule based control(RBC)that is currently realized on the test site. The second model applies a model predictive control (MPC) scheme assuming the same system and boundary conditions. The performance is assessedin terms of maximizing the islanding time, signifying the timethe microgrid(MG)can disconnect from the remaining distribution grid.

In the course ofthe 2020 Horizon project InterFlex,French distribution system operator(DSO)ENEDIS is building Nice Smart Valley, a demonstratorto test innovative solutions for distribution systems.In their first use case,the network of the Lérins’ islandsis equippedwith a variety of distributed energy resources (DERs) to investigate the combination of local storage systems and renewable generation, with specific focus on the temporary operation asafully autonomous island. This paper presents the application of apreviously developed model predictive control (MPC) based energy management system (EMS) to the demonstrator network under construction. Simulations show how the DERs can be used for different objectives like collective self-consumptionandmaximization of potential islanding time (PIT).

2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) : [Proceedings] - IEEE, 2019. - ISBN 978-1-7281-3587-8 - doi:10.1109/ICRERA47325.2019.8996537 2019 8th International Conference on Renewable Energy Research and Applications, ICRERA, Brasov, Romania, 3 Nov 2019 - 6 Nov 2019; Brasov : IEEE 857-863 (2019). doi:10.1109/ICRERA47325.2019.8996537, Published by IEEE, Brasov

Missing data impairs the performance of most neural networks with a particularly strong effect on time series prediction networks. Imputation addresses this issue and by replacing missing values with substitute values. The choice of a suitable imputation method requires fundamental knowledge of the dataset.Autoencoders (AE) have been widely applied in representation learning and feature extraction. In this paper we usea stacked denoising overcomplete autoencoder for imputation in multi-variate time series. We assess the model’s feature reproduction capability and compare its effect to simple mean imputation on a open source dataset. Moreover, we assess the imputation’s influence on a recurrent neural network’s short-term load forecasting results and show that our proposed autoencoder model yields better results in feature imputation and significantly improves the forecasting accuracy for low and high fractions of missing data.

One of the important goals of Project RESERVE (www.re-serve.eu/) is to develop and demonstrate voltage control algorithms for low voltage distribution systems in high RES penetration contexts. On this basis, an impedance based decentralised algorithm to monitor dynamic voltage stability margins and virtual output impedance control is developed. This paper presents the field trials that are planned in ESB Networks, Ireland to validate the technique and the network code proposals derived from the technical work. Additonally, this paper also performs an impedance based stability analysis for an exemplary 3-Node LV network.

This paper discusses how the thermal side of a local energy community can offer flexibility to the electrical grid. In this regard, a model is presented in which a heat pump is used to connect low temperature heating and cooling networks and create flexibility being powered by renewables. This is done in combination with an overall control strategy aligning with production and demand across different energy carriers integrating the heat pump model with the thermal storage capacity offered by heating/cooling grids presenting the theoretical demand response opportunities in technical terms.