Sammendrag
The need for power is expanding quickly, and as
fossil fuels are depleted, alternatives based on renewable energy
are emerging. In recent years, microgrids, which are based on
distributed generation and storage systems, have been on an
increasing trend. This generates various new opportunities, one
of which is the capacity to monitor, control, and regulate the
energy flow inside and outside of the microgrid. By making
microgrid’s distributed energy resources (DERs) and energy storage
components economically viable with artificial intelligence
and machine learning incorporated into the cost-optimization
process, the demand for and growth of these technologies will
be accelerated significantly. This paper focuses on employing
reinforcement learning (RL) algorithms to control energy flow
in an AC microgrid. By incorporating artificial intelligence and
machine learning into the energy management system (EMS),
the paper aims to optimize costs and facilitate the integration
of renewable energy sources. The RL agent is designed to trade
energy with the main grid, taking advantage of the energy storage
system and achieving cost savings. The RL agent is tested using
real spot prices data in Norway from a simulation model that
combines Python and MATLAB-Simulink software programs for
efficient co-simulation technique. Results show significant cost
savings of around 14% for a simple model and 7.5% for a
complex dynamic model.
10.1109/SGRE59715.2024.10428874
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