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Hydrogen and renewable electricity-based microgrid is considered to be a promising way to reduce carbon emissions, promote the consumption of renewable energies and improve the sustainability of the energy system. In view of the fact that the existing day-ahead optimal operation model ignores the uncertainties and fluctuations of renewable energies and loads, a two-stage energy management model is proposed for the sustainable wind-PV-hydrogen-storage microgrid based on receding horizon optimization to eliminate the adverse effects of their uncertainties and fluctuations. In the first stage, the day-ahead optimization is performed based on the predicted outpower of WT and PV, the predicted demands of power and hydrogen loads. In the second stage, the intra-day optimization is performed based on the actual data to trace the day-ahead operation schemes. Since the intra-day optimization can update the operation scheme based on the latest data of renewable energies and loads, the proposed two-stage management model is effective in eliminating the uncertain factors and maintaining the stability of the whole system. Simulations show that the proposed two-stage energy management model is robust and effective in coordinating the operation of the wind-PV-hydrogen-storage microgrid and eliminating the uncertainties and fluctuations of WT, PV and loads. In addition, the battery storage can reduce the operation cost, alleviate the fluctuations of the exchanged power with the power grid and improve the performance of the energy management model.

Aiming at the optimization problem of operation dispatching of multi-energy systems including cold, heat, electricity and gas, in order to achieve the goal of energy saving and emission reduction, a microgrid optimization model including microsources such as fans, micro gas turbines, waste heat boilers, lithium bromide absorption chillers, etc. is established with the microgrid operation cost and new energy utilization rate as the optimization objectives. The improved multi-objective grey wolf algorithm (MOGWO) is used to solve the multi-objective model, which effectively compensates for the disadvantage that the grey wolf algorithm is easy to fall into local optimum. The simulation analysis shows that the algorithm has good solution speed and global search performance, which provides an optimal dispatching strategy for multi-energy systems, realizes flexible mobilization of microgrid, and achieves the effects of reducing operating costs and low grid-connected volatility.

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