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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 - Vimarsana News

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 relat...

Source: mdpi.com
"A Deep Q-Network Approach to Optimize Spatial Reuse in WiFi Networks" by Yiwei Huang and Kwan Wu Chin - Vimarsana News

"A Deep Q-Network Approach to Optimize Spatial Reuse in WiFi Networks" by Yiwei Huang and Kwan Wu Chin

The proliferation of IEEE 802.11 or WiFi networks, and the explosive growth in traffic demands call for solutions to maximize the capacity of WiFi networks. Hence, maximizing the spatial reuse of WiFi networks is critical as doing so allows multiple concurrent transmissions. In this respect, a critical network parameter, Clear Channel Assessment (CCA) threshold, plays a vital role as it dictates whether a node is allowed to transmit after sensing the channel. In this paper, we propose to use Deep Q-network (DQN) under two learning patterns to select the CCA threshold of devices. We further con...

Source: uow.edu.au