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Energies | Free Full-Text | Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the s ....

United Kingdom , Google Deepmind , Keele University , Energy Balance Model , Smart Energy Network , Smart Energy Network Demonstrator , Deepq Networks , Wind Turbine , Optimal Control , Cost Saving ,

Curious Cook: The human metabolism puzzle

The columnist uncovers some interesting facts about human metabolism, including the fact that from ages 20 to 60, there is no significant decline in metabolic rates. ....

United States , Energy Balance Model , Duke University , Carbohydrate Insulin Model , American Journal , Curious Cook , Chris Chan , Human Metabolism , Metabolic Rate , Weight Gain ,