Date of Award
Fall 2023
Document Type
Open Access Dissertation
Department
Electrical Engineering
First Advisor
Herbert Ginn
Abstract
The field of economic dispatch (ED) focuses on optimizing power flow in a power system to minimize costs. It has the potential to significantly enhance system effectiveness, and efficiency, and reduce operating costs. Various techniques have been employed to tackle this problem, each with its own strengths and weaknesses. One promising approach is simulation-based optimization (SBO), which allows for accurate modeling of system interactions and improved representation of expected results. However, SBO requires running numerous simulations to identify an optimal solution, and there is a possibility of not achieving the global optimum. This work aims to address these challenges using machine learning. The first contribution involves enhancing the computational efficiency of the SBO model by employing state-reduction techniques and neural network-based observers. This optimization reduces simulation time, thereby speeding up the search process. The second contribution involves developing a hybrid search algorithm by combining the genetic algorithm and the particle swarm method. Additionally, leveraging the cost-to-parameter correlation helps expedite the parameter search. This modified hybrid genetic algorithm reduces the number of simulations required to discover the optimum solution while providing increased confidence in the result. Finally, these two methods are applied to a system to demonstrate that, with their integration, a simulation-based optimizer can align economic dispatch parameters within minutes using standard computing devices. This significantly improves upon the traditional offline approach, which was necessitated by time constraints. This research focuses on enhancing the SBO technique for economic dispatch through machine learning. It includes improving computational efficiency and developing a hybrid search algorithm, ultimately enabling real-time parameter alignment for economic dispatch on regular computing devices.
Rights
© 2024, Tyler Van Deese
Recommended Citation
Deese, T. V.(2023). Simulation-Based Optimization of a DC Microgrid: With Machine-Learning-Based Models and Hybrid Meta-Heuristic Algorithms. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/7643