Thesis
Complementary quadratic programming and artificial neural network for computationally efficient microgrid dispatch optimization with unit commitment
Washington State University
Master of Science (MS), Washington State University
2018
Handle:
https://hdl.handle.net/2376/100837
Abstract
Microgrid infrastructures allow for a cleaner energy future by reducing transmission losses, enabling combined heat and power efficiency upgrades, employing onsite renewable generation, and providing power stability especially when paired with energy storage devices. Microgrid dispatch optimization allows wider implementation of microgrid infrastructures by lowering microgrid operations costs. The computational bottleneck of dispatch optimization is unit commitment, which is a mixed integer optimization problem. Three methods to reduce the computational effort of unit commitment and maintain satisfactory optimality are. Complementary Quadratic Programming (cQP), modified complementary Quadratic Programming (mcQP), and Artificial Neural Network (ANN) with dynamic economic dispatch. Both cQP and mcQP are capable of quickly optimizing receding horizon dispatches with storage, creating training sets which facilitates machine learning approaches such as method three. This thesis presents cQP and mcQP development as a means of training a neural network unit commitment solver, and compares all three approaches to solutions of the full mixed-integer problem using a commercial solver. Decision trees are employed for feature selection, and ANNs of varying depth are compared for ANN structure selection. The mcQP method is the most robust, and the ANN method is the most computationally efficient. All three methods outperform the commercial solver in computational efficiency, robustness, and dispatch cost.
Metrics
19 File views/ downloads
22 Record Views
Details
- Title
- Complementary quadratic programming and artificial neural network for computationally efficient microgrid dispatch optimization with unit commitment
- Creators
- Nadia Victoria Panossian
- Contributors
- Dustin F. McLarty (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Mechanical and Materials Engineering, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900524802001842
- Language
- English
- Resource Type
- Thesis