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Smart energy networks provide for an effective means to accommodate highpenetrations of variable renewable energy sources like solar and wind, whichare key for deep decarbonisation of energy production. However, given thevariability of the renewables as well as the energy demand, it is imperative todevelop effective control and energy storage schemes to manage the variableenergy generation and achieve desired system economics and environmental goals.In this paper, we introduce a hybrid energy storage system composed of batteryand hydrogen energy storage to handle the uncertainties related to electricityprices, renewable energy production and consumption. We aim to improverenewable energy utilisation and minimise energy costs and carbon emissionswhile ensuring energy reliability and stability within the network. To achievethis, we propose a multi-agent deep deterministic policy gradient approach,which is a deep reinforcement learning-based control strategy to optimise thescheduling of the hybrid energy storage system and energy demand in real-time.The proposed approach is model-free and does not require explicit knowledge andrigorous mathematical models of the smart energy network environment.Simulation results based on real-world data show that: (i) integration andoptimised operation of the hybrid energy storage system and energy demandreduces carbon emissions by 78.69%, improves cost savings by 23.5% andrenewable energy utilisation by over 13.2% compared to other baseline modelsand (ii) the proposed algorithm outperforms the state-of-the-art self-learningalgorithms like deep-Q network.

Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand using Deep Reinforcement Learning