The tabu ant colony optimizer and its application in an energy market

<p>"A new ant colony optimizer, the 'tabu ant colony optimizer' (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement.</p><p>The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day"--Abstract, page iii.</p>

Name thesis:degree_name

Ph. D. in Systems Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Haynes, David Donald

• Ant colony optimization
• Energy management
• Power distribution economics
• Power generation economics
• Programming
• Swarm intelligence
• Electrical and Computer Engineering
• Systems Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2776

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3781