Abstract:
People's concerns about environmentally friendly power generation have given rise to a new
concept known as distributed generation. The integration of renewable-based distributed
generation resources into the main power grid, on the other hand, is difficult due to their
constant monitoring and control requirements. As a result, microgrids have been identified as
appropriate platforms for integrating distributed generation resources and loads. Instead of
using traditional centralized control architecture, these microgrids use distributed control
systems like multi-agent-based systems as a novel approach. Social, reactive, proactive, and
autonomous are the common features of these control agents. These agents can be improved
by using machine learning-based technologies to introduce intelligence. As a result, the focus
of this research is on using Reinforcement Learning as an experimental machine learning
method to optimize the energy generation function of a grid-connected microgrid so that the
microgrid's grid dependency is minimized as the agent learns. To determine the best technique
for system optimization, the proposed microgrid's performance is tested using single and
multi-agent reinforcement learning models in Python. A hardware testbed is developed for the
selected high-performance model to demonstrate the practical applicability of reinforcement
learning.
Citation:
Perera ,M,K. (2021). Optimization of multi agent based energy management systems using reinforcement learning for microgrids [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22542