Multi-vector energy management system including scheduling electrolyser, electric vehicle charging station and other assets in a real scenario. (20th December 2022)

• Record Type: Journal Article
• Authors: Massana, Joaquim; Burgas, Llorenç; Herraiz, Sergio; Colomer, Joan; Pous, Carles
• Is Part Of: Journal of cleaner production. Volume 380:Part 2(2022)
• Publication Date: 2022-12-20
• Subjects: Optimisation scheduling -- Multi-vector energy manager system -- Flexibility -- Hydrogen -- Smart grids -- EV charging station; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.134996
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Highlights: A genetic algorithm for an optimal scheduler for controlling assets is proposed The proposed scheme performs 24-hour optimal scheduling for an existing micro-grid HVAC; EV charging station; electrolyser and hydrogen refuelling station are scheduled Experiments are done on existing assets during 14 days using real data on real time