The need to cope with the rapid transformation of the conventional electrical grid into the future smart grid, with multiple connected microgrids, has led to the investigation of optimal smart grid architectures. The main components of the future smart grids such as generators, substations, controllers, smart meters and collector nodes are evolving; however, truly effective integration of these elements into the microgrid context to guarantee intelligent and dynamic functionality across the whole smart grid remains an open issue. Energy trading is a significant part of this integration.
In microgrids, energy trading refers to the use of surplus energy in one microgrid to satisfy the demand of another microgrid or a group of microgrids that form a microgrid community. Different techniques are employed to manage the energy trading process such as optimization-based and conventional game-theoretical methods, which bring about several challenges including complexity, scalability and ability to learn dynamic environments. A common challenge among all of these methods is adapting to changing circumstances. Optimization methods, for example, show promising performance in static scenarios where the optimal solution is achieved for a specific snapshot of the system. However, to use such a technique in a dynamic environment, finding the optimal solutions for all the time slots is needed, which imposes a significant complexity. Challenges such as this can be best addressed using game theory techniques empowered with machine learning methods across grid infrastructure and microgrid communities.
In this thesis, novel Bayesian coalitional game theory-based and Bayesian reinforcement learning-based coalition formation algorithms are proposed, which allow the microgrids to exchange energy with their coalition members while minimizing the associated cost and power loss. In addition, a deep reinforcement learning scheme is developed to address the problem of large convergence time resulting from the sizeable state-action space of the methods mentioned above. The proposed algorithms can ideally overcome the uncertainty in the system. The advantages of the proposed methods are highlighted by comparing them with the conventional coalitional game theory-based techniques, Q-learning-based technique, random coalition formation, as well as with the case with no coalitions. The results show the superiority of the proposed methods in terms of power loss and cost minimization in dynamic environments.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43673 |
| Date | 02 June 2022 |
| Creators | Sadeghi, Mohammad |
| Contributors | Erol Kantarci, Melike |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0021 seconds