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 7 the aim of maintaining a clean environment. To reduce energy consumption 8 from fossil fuels, new tools for micro-grids have been proposed. In the context 9 of multi-vector energy management systems, the present work proposes an 10 optimal scheduler based on genetic algorithms to manage flexible assets in the 11 energy system, such as energy storage and manageable demand. This tool is 12 applied to a case study for a Spanish technology park (360 kW consumption 13 peak) with photovoltaic and wind generation (735 kW generation peak), 14 hydrogen production (15 kW), and electric and fuel cell charging stations. 15 It provides an hourly day-ahead scheduling for the existing flexible assets: 16 the electrolyser, the electric vehicle charging station, the hydrogen refuelling 17 station, and the heating, ventilation, and air conditioning system in one 18 building of the park. 19 A set of experiments is carried out over a period of 14 days, using real 20 data and performing computations in real time, in order to test and validate 21 the tool. The analysis of results show that the solution maximises the use of 22 local renewable energy production (demand is shifted to those hours when 23 there is a surplus of generation), which means a reduction in energy costs, 24 whereas the computational cost allows the implementation of the tool in real 25 time.