Show simple item record

dc.contributor.authorShah, Chinmay
dc.date.accessioned2022-09-22T18:03:00Z
dc.date.available2022-09-22T18:03:00Z
dc.date.issued2022-05
dc.identifier.urihttp://hdl.handle.net/11122/13015
dc.descriptionDissertation (Ph.D.) University of Alaska Fairbanks, 2022en_US
dc.description.abstractIntegration of power electronic converter-based distributed energy resources (DERs) in electric power distribution networks is growing exponentially with the recent interest in reducing carbon emissions from fossil fuel-based generation. As the contribution of renewable energy sources in the DER mix continues to increase, so does the incorporation of battery energy storage systems and other controllable loads to compensate for the high variability and uncertainty in the generation from renewable DERs and grid demand. Strategies for increasing the contribution of renewable energy sources and using reserves to accommodate for variations and uncertainty in generation and load include distributed optimal power flow (OPF) methods and improved forecasting. This work proposes a co-optimization of power flow and flexibility reserves, executed on a private blockchain for security, solved using a parameterized deterministic method based on semi-distributed architecture and alternating direction method of multipliers (ADMM) based distributed architecture that addresses uncertainty and enhances the flexibility of the distribution network. However, ADMM guarantees convergence only for strictly convex problems and hence a relax-and-fix heuristic algorithm is proposed in co-ordination with ADMM to solve the OPF problem, which is non-convex in nature. Also, an accurate short-term load forecasting algorithm is essential to reduce the uncertainty in the dispatch results using the OPF algorithm. In this work, a short-term residential load forecasting algorithm is proposed using a two-stage stacked long short-term memory network-based recurrent neural network and Hampel filter to address this issue. All the proposed algorithms are tested using different case studies. Results demonstrate that the proposed algorithms reduce the impact of uncertainty in the distribution network, automate scheduling flexibility reserve and minimize its cost, reduce the OPF execution time using a distributed architecture, and produce residential load forecast with a significantly lower prediction error.en_US
dc.description.sponsorshipAlaska Center for Energy and Power, UAF Center for Innovation, Com­mercialization and Entrepreneurship, Pacific Northwest National Laboratory, National Re­newable Energy Laboratory, Office of Naval Research, U.S. Department of Energy EPSCoRen_US
dc.description.tableofcontentsChapter 1. Introduction and summary of algorithms and models -- Chapter 2. Three-stage learning-based power flow and flexibility reserve cooptimization for converter dominated distribution network look-ahead model using blockchain and S-ADMM -- Chapter 3. Distributed ADMM using private blockchain for power flow optimization in distribution network with coupled and mixed-integer constraints -- Chapter 4. A novel short-term residential load forecasting methodology using two stage stacked long short-term memory (LSTM) and Hampel filter -- Chapter 5. Observations, conclusion, and future work.en_US
dc.language.isoen_USen_US
dc.subjectDistributed generation of electrical poweren_US
dc.subjectMathematicsen_US
dc.subjectElectrical power systemsen_US
dc.subjectElectric batteriesen_US
dc.subjectBlockchainsen_US
dc.subjectMathematical optimizationen_US
dc.subject.otherDoctor of Philosophy in Engineeringen_US
dc.titleOptimization and forecasting algorithms for converter dominated distribution networks using blockchain and AIen_US
dc.typeDissertationen_US
dc.type.degreephden_US
dc.identifier.departmentDepartment of Electrical Engineeringen_US
dc.contributor.chairWies, Richard W.
dc.contributor.committeeAl-Badri, Maher
dc.contributor.committeeHuang, Daisy
dc.contributor.committeeCicilio, Phylicia


Files in this item

Thumbnail
Name:
Shah_C_2022.pdf
Embargo:
2023-04-15
Size:
11.44Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record