Multi-vector energy management system including scheduling electrolyser, electric vehicle charging station and other assets in a real scenario
Today, in the field of energy, the main goal is to reduce emissions with the aim of maintaining a clean environment. To reduce energy consumption from fossil fuels, new tools for micro-grids have been proposed. In the context of multi-vector energy management systems, the present work proposes an op...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/22184 |
| Acceso en línea: | http://hdl.handle.net/10256/22184 |
| Access Level: | acceso abierto |
| Palabra clave: | Estacions de càrrega (Vehicles elèctrics) Battery charging stations (Electric vehicles) Xarxes elèctriques intel·ligents Smart power grids Sistemes integrats de gestió |
| Sumario: | Today, in the field of energy, the main goal is to reduce emissions with the aim of maintaining a clean environment. To reduce energy consumption from fossil fuels, new tools for micro-grids have been proposed. In the context of multi-vector energy management systems, the present work proposes an optimal scheduler based on genetic algorithms to manage flexible assets in the energy system, such as energy storage and manageable demand. This tool is applied to a case study for a Spanish technology park (360 kW consumption peak) with photovoltaic and wind generation (735 kW generation peak), hydrogen production (15 kW), and electric and fuel cell charging stations. It provides an hourly day-ahead scheduling for the existing flexible assets: the electrolyser, the electric vehicle charging station, the hydrogen refuelling station, and the heating, ventilation, and air conditioning system in one building of the park. A set of experiments is carried out over a period of 14 days, using real data and performing computations in real time, in order to test and validate the tool. The analysis of results show that the solution maximises the use of local renewable energy production (demand is shifted to those hours when there is a surplus of generation), which means a reduction in energy costs, whereas the computational cost allows the implementation of the tool in real time |
|---|