As the development of the multi-energy system (MES), various ME applications are deployed. ME applications not only bring advanced functionalities to the MES, but also show great potentials in promoting the operation performance of the MES, especially improving the accommodation of renewable energy sources (RES). However, the realization of these potentials largely relies on the energy management, which shall facilitate the effective function of each ME application and the coordinated collaboration of all the ME applications. Without a comprehensive energy management methodology, ME applications may mutually interfere, which not only hinder the RES utilization, but also may harm the MES operation performance. In this premise, this paper integrates the energy management model of the combined cooling, heat and power plants, power-to-hydrogen/gas-to-power plants, and demand side management model of the EV charging loads into the energy management model of the MES, and proposes an comprehensive optimal day-ahead energy management framework to simultaneously improve the profit, RES utilization rate, and energy saving performance of the MES. To address the proposed optimization model, Elitist Non-dominated Sorting Genetic algorithm II algorithm is employed to heuristically find the Pareto-optimal results. Finally, case studies prove the effectiveness of the proposed methodology.
An Energy Community Implementation: The Unical Energy Cloud
