A Forecasting Based Hierarchical Energy Management for Sustainable Data Centers

Renewable energy (RE) integrated with a hybrid electric-hydrogen storage system (HEHSS) is considered a promising solution for data centers to increase their energy efficiency and decrease their greenhouse emissions. This paper proposes a forecasting based hierarchical energy management strategy for a data center with wind energy, tidal energy and HEHSS, which is taken as a microgrid. A multi-population genetic algorithm-based day-ahead scheduling is used as the first layer and a rolling predictive control-based real-time dispatching is added as a second layer to minimize the degradation costs of HEHSS and maximize the revenue of the electricity and hydrogen trading. Also, a long-short-term-memory-based forecasting framework is developed to forecast power generation and load demand.