Framework for Scenario Generation and Reduction in Photovoltaic-Integrated Generation Commitment

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Luis, L. (2018). Framework for Scenario Generation and Reduction in Photovoltaic-Integrated Generation Commitment. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Photo-voltaic (PV) power generation is promising from an environmental and economic standpoint. However, increased penetration of PV power into the electric grid can present significant challenges to decision-making in energy markets. To maintain grid robustness, especially with renewable sources like PV power that are highly uncertain and variable, system operators seek the most accurate information available on the generation characteristics. In this regard, a scenario generation and reduction framework that captures the uncertainty and variability of PV power is proposed. This work characterizes the forecast error via a set of uncertainty and variability indices. A large set of scenarios is generated using a pseudo-random number generation process. Next, a scenario reduction framework to improve computational tractability is proposed. Finally, the efficacy of these methodologies is proven by observing their impact on the unit commitment solution via a cost-benefit analysis. The proposed work is tested using measured and forecasted PV power data for a specific geographical location in North Carolina. The entire framework is built using MATLAB/Simulink and GAMS Optimization software. The results indicate that incorporating scenarios in the deterministic unit commitment model improves overall operational costs while capturing the uncertainty and variability of PV generation.

Details
Author

Luis, Lee

Title

Framework for Scenario Generation and Reduction in Photovoltaic-Integrated Generation Commitment

Physical Description

1 online resource (136 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Photo-voltaic (PV) power generation is promising from an environmental and economic standpoint. However, increased penetration of PV power into the electric grid can present significant challenges to decision-making in energy markets. To maintain grid robustness, especially with renewable sources like PV power that are highly uncertain and variable, system operators seek the most accurate information available on the generation characteristics. In this regard, a scenario generation and reduction framework that captures the uncertainty and variability of PV power is proposed. This work characterizes the forecast error via a set of uncertainty and variability indices. A large set of scenarios is generated using a pseudo-random number generation process. Next, a scenario reduction framework to improve computational tractability is proposed. Finally, the efficacy of these methodologies is proven by observing their impact on the unit commitment solution via a cost-benefit analysis. The proposed work is tested using measured and forecasted PV power data for a specific geographical location in North Carolina. The entire framework is built using MATLAB/Simulink and GAMS Optimization software. The results indicate that incorporating scenarios in the deterministic unit commitment model improves overall operational costs while capturing the uncertainty and variability of PV generation.

Genre

masters theses

Subjects--Topics

Electrical engineering
Power resources

Degree

M.S.

Keywords

Clustering
Photovoltaic
Scenario Generation`
Scenario Reduction
Uncertainty and Variability
Unit Commitment

Subject Area

Electrical Engineering

Advisor(s)

Kamalasadan, Sukumar

Committee Members

Cecchi, Valentina
Cali, Umit

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2018.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Luis_uncc_0694N_11749

Permalink

http://hdl.handle.net/20.500.13093/etd:116